UNIVERSITÀ DI PISA
Facoltà di Ingegneria
Corso di Laurea Magistrale in Ingegneria Biomedica
Tesi di Laurea Magistrale in Ingegneria Biomedica
Development, design and experimental
validation of a gait-phase predictor
based on adaptive oscillators for
lower-limb assistive robotics
Relatori:
Candidato:
Prof. Cecilia Laschi
Andrea Parri
Dr. Nicola Vitiello
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List of contents
List of contents 3 List of figures 6 List of tables 9 Abstract 10 Chapter 1: Introduction 111.1. Gait disorders and effect on industrialized society 11
1.2. Biomechanics of gait 12
1.2.1. Definitions from Winter [11] 13
1.2.2. Hip joint biomechanics 15
1.2.3. Ground reaction forces 18
1.3. Biomechanics of gait in elderly: a qualitative comparison 18
1.4. Wearable robotics 19
1.4.1. Physical human robot interaction 20
1.4.2. Cognitive human robot interaction 21
1.4.3. Bioinspiration and adaptivity 23
1.5. Objectives of the work 24
Chapter 2: Active lower-limb exoskeletons and orthoses 26
2.1. State of the art 26
2.1.1. Multi-joint lower-limb exoskeletons 26 2.1.1.1. Sensitivity amplification control 26
2.1.1.2. Cybernic control 28
2.1.1.3. Model-based control 30
2.1.1.4. Oscillators-based control 31
2.1.1.5. Impedance control 32
2.1.2. Single-joint: Ankle-foot orthoses 33
2.1.2.1. Proportional Myoelectric Control 33
2.1.2.2. Position control 33
2.1.2.3. Variable impedance control 34
2.1.3. Single-joint: Knee orthoses 34
2.1.3.1. Positive feedback force amplification 34
2.1.3.2. Position control 35
2.1.3.3. Sliding mode control 35
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2.1.4.1. Myoelectric control 36
2.1.4.2. Stride Management Assist 36
2.2. Light-weight active pelvis orthosis 37
2.2.1. Mechanical structure 37
2.2.2. Actuation units 38
2.2.3. Control system 39
2.2.3.1. Low-level closed-loop torque control 40 2.2.4. Characterization of low-level closed-loop torque control 41
2.2.4.1. Step response 41
2.2.4.2. Chirp response 42
2.2.4.3. Output impedance 42
2.2.4.4. High-level assistive control 43
2.2.5. Usability for implementing an adaptive assistive strategy 44 Chapter 3: Development of a phase prediction algorithm based on adaptive frequency oscillators 49
3.1. Learning of motor primitives 49
3.2. Adaptive frequency oscillators 50
3.3. State of the art of adaptive frequency oscillators 51 3.3.1. Adaptive frequency oscillators for walking task of bipedal robot 51 3.3.2. Adaptive oscillators-based control strategies 51 3.3.2.1. Model-based and approach model-free approach 52 3.4. Formulation of the phase detection algorithm 52 3.4.1. Dynamical system for non-sinusoidal teaching signals 54
3.4.2. Architectures 56
3.4.3. Kernel-based non-linear filter 57
3.5. Phase detection 58
3.5.1. Event detection through sensorized insoles 59
3.5.2. Event detection through encoders 60
3.5.3. Selected architectures 61
3.6 Gait segmentation 61
3.6.1. Phase reset error 61
3.6.2. Introduction of an additional state variable in the dynamical system 62 3.6.3.1. Integrator saturated by sin function 63 3.6.3.2. Integrator saturated by arctangent and gaussian functions 64
3.6.3.3. First order step response 65
3.6.4. Synchronization 66
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3.7. Robustness of the event detection algorithm 68
3.8. State machine 69
3.8.1. Standing/walking condition 69
3.8.2. Phase synchronization 69
Chapter 4: Development of the oscillator-based phase prediction algorithm in RT Labview Core 71 4.1. Flexibility of the implemented Labview library 71
4.2. Vis description 71
Chapter 5: Experiments and results 78
5.1. Alternative methods proposed in scientific literature 78
5.2. Experimental protocol 79
5.3. Collected data and post-processing analysis 79 5.3.1. Compensation of the phase reset error within the segmented stride period 80 5.3.2. Comparison of the adaptive capability 81
5.4. Results and discussions 81
5.4.1. Initial transitory 82
5.4.2. Performance of the segmentation algorithm 82
5.4.3. Phase prediction performance 85
Chapter 6: Proof of feasibility of the assistive strategy. 86
6.1. Phase-based assistive torque 86
6.1.1. Parametric assistive torque 88
6.2. Experimental session to assess the feasibility of the assistive strategy 89
6.3. Results and discussion 90
6.3.1. Kinematic effects 91
6.3.2. Kinetic effects 92
6.3.3. Efficacy of the assistive strategy 94
Chapter 7: Conclusions and future perspectives 96
References 98
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List of figures
Fig. 1 - Segmented gait cycle. 14
Fig. 2 - Hip joint flexion angle reported from Winter dataset segmented over the stride period. 15 Fig. 3 - Hip joint torque reported from Winter dataset segmented over the stride period. The sign of the torque is chosen according to Winter convention. 16
Fig. 4 - Hip joint power reported from Winter dataset segmented over the stride period. 17 Fig. 5 - Hip joint trajectory limit cycle during ground level walking 24 Fig. 6 - a) Berkley Lower Extremities Exoskeleton . b) Body extender. Adapted from [25],
[27]. 27
Fig. 7 - a) Hybrid Assistive Limb double leg version. b) MIT Exoskeleton. c) Vanderbilt
Orthosis. d) ReWalk. 29
Fig. 8 - a) LOPES platform. b) LOKOMAT platform. Adapted from [36], [40]. 32 Fig. 9 - a) Michigan powered ankle-foot orthosis. b) MIT active ankle-foot orthosis. Adapted
from [41], [42]. 34
Fig. 10 - Stride Management Assist by Honda. 36 Fig. 11 - a) Frontal, lateral, and backside view of the APO worn by a healthy adult subject. b) Frontal, lateral and backside view of CAD model [47]. 37
Fig. 12 - Exploded view of the actuation unit. 1) DC motor with incremental encoder. 2) Harmonic Drive. 3) 4-bar transmission mechanism. 4) Cstom torsional spring. 5) Absolute
encoder. [47] 39
Fig. 13 - Scheme of the control system of APO. a) Block diagram of the hierarchical control architecture. b) Low-level closed-loop torque control. [47] 40
Fig. 14 - Experimental characterization of the step response for different desired step amplitudes. Each graph reports the reference torque (red dotted line) and the averaged responses
over 15 iterations (black line). [47] 41
Fig. 15 - Experimental characterization of the torque control. a) Chirp response: amplitude bode diagram of the transfer function from desired torque to measured torque. b) Characterization of the joint output impedance: angular displacement and interaction torque over a 3 Hz-motion range. c) Amplitude and phase Bode diagram of the transfer function from angular displacement to interaction torque. [47] 43
Fig. 16 - Walking with the APO under TM. For each gait speed, the following variables for left and right hip joints are averaged over all strides and plotted against the percentage of the stride cycle: hip joint angle, hip joint velocity, SEA torque and power. For each graph the average curve (solid line; blue for left and red for right joint) is shown along with the standard
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Fig. 17 - Walking with the APO under AM. For each gait speed, the following variables for left and right hip joints are averaged over all strides and plotted against the percentage of the stride cycle: hip joint angle, hip joint velocity, SEA torque and power. For each graph the average curve (solid line; blue for left and red for right joint) is shown along with the standard
deviation contour. [47] 46
Fig. 18 - Block diagram of the assistive strategy. Phase of gait is learned by the Phase Predictor which input is the monitored signal . A computational block translate the current phase of gait in a reliable assistive desired torque . 53
Fig. 19 - Learning process for hip joint angle trajectory (left hip angle). The upper graph shows the phase of gait and the frequency of gait. The lower graph reports the learned waveform of the hip angle from the current one measured by the integrated encoders. 54
Fig. 20 - Block diagram scheme of a pool of AFOs. N frequency components of the teaching signal are learned with its offset and sum to replicate the waveform. The output of the system is the phase of the main frequency component of the signal. 55
Fig. 21 - Learning process for vGRF trajectory (left vGRF). The upper graph shows the phase of gait and the frequency of gait. The lower graph reports the learned waveform of the vGRF from the current one recorded by sensorized insoles. 55
Fig. 22 - Block diagram scheme of the coupling between the AFOs and the Kernel-based non-linear filter. The zero-delay estimate of the tracked signal is mapped by the filter by means of N Gaussian functions equi-spaced in the phase interval [0 ; ]. Adapted from [36]. 57
Fig. 23 - Supervised learning process of the Kernel-based non-linear filter. The estimate vGRF trajectory (red line) is reported along with the current vGRF signal (blue line) recorded by
sensorized insoles. 58
Fig. 24 - Overview on wearable sensorized insoles. The matrix of 64 optoelectronic sensors is inserted in shoes. The box include the Bluetooth transmission system and the battery. Adapted
from [24], [65]. 59
Fig. 25 - Example of Event Detection. HS event is detected as the transition of CoP signal
from a NaN to a finite value. 60
Fig. 26 - Collected data from insoles. The upper graph shows the vGRF signal, while in the
lower graph is reported the CoP signal. 60
Fig. 27 - Phase Reset Error computation. a) AFO phase (green curve) in advance with respect to the desired phase . b) AFO phase (green line) in delay with respect to the desired phase . Phase Reset Error is represented by the red arrow. 62
Fig. 28 - Desired behavior of the synchronization equation. The vector field indicates the desired variation of the state variable overlap on the color map. Blue region specifies the interval in which the should decrease as the red indicates a desired increasing. Red dot
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centered in (0,0) represents the starting condition while the yellow dot (0, ) represents the steady state condition in which the phase reset error is deleted by the synchronized system. 63
Fig. 29 - Synchronization of the n-phases of the AFO dynamical system. The upper graph shows that each phase grows with constant slopes represented by the frequencies which are all multiples of the first one. As a consequence the lower graph points out that the synchronization property of the system lead to an instantaneous phase reset for the phase of each oscillator
included in the pool. 67
Fig. 30 - Phase Predictor block diagram. Phase of gait is learned by AFOs which receive as input the monitored signal . The phase is constantly adjust to assume the null value in correspondence of the HS. The adaptivity is ensured by the Phase Reset Error triggered at each event detection on the control signal . 68
Fig. 31 - dStateVector vi block diagram. 72
Fig. 32 - PhaseEstimator vi block diagram. 73
Fig. 33 - d_ vi block diagram. 73
Fig. 34 - EventDetection vi block diagram. In the specific case it refers to the HS detection through CoP signals from sensorized insoles. 75
Fig. 35 - PhaseReset vi block diagram. 76
Fig. 36 - Detail of the simulation Real-Time loop of AFO dynamical system, Event detection, Phase reset algorithm and Adaptive Kernel-Based non-linear filter. 77
Fig. 37 - Initial transitory of state variables of AFOs dynamical system. Gait of phase (black line), frequency of gait (blue line), phase reset error at each event detection (red line) and phase shift following a step-response like behavior (green line) are reported. After five stride periods state variables reach a stationary condition. 82
Fig. 38 - Barplot of the RMSE of the Phase reset error for the developed algorithm, method proposed by Lenzi and method proposed by Ferris. The upper graph reports the RMSE for each steady-state walking speed. The lower graph shows the the RMSE for each walking speed
transition. 84
Fig. 39 - Comparison between the gait phase predicted by AFOs dinamical system and an ideal linear phase of gait reference. For each graph the average phase (blue continuous line) segmented for each gait cycle at every steady-state walking is reported along with its shadowed standard deviation contour. In each graph the ideal linear phase reference is overlayed (red
continuous line). 85
Fig. 40 - Phase Prediction provided by the algorithm. In the upper graph the phase φ averaged for each stride period vs. the gait cycle percentage is reported. The lower graph shows the segmented vGRF vs. the percentage of the stride period. The null phase in output from the phase predictor is totally synchronized with the desired reference HS. 87
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Fig. 41 - Assistive torque vs. phase of gait powered during experimental session. The upper graph refers to condition (i) for a torque peak of 10 Nm. The lower graph refers to condition
(iii). 90
Fig. 42 - Hip joint angle trajectory segmented over the stride period. For each graph (left and right joint) the averaged joint angle for each stride is reported along with its standard deviation contour. Each curve represents the variable for TM and different level of assistance. 91
Fig. 43 - Hip joint angle trajectory segmented over the stride period. For each graph (left and right joint) the averaged joint angle for each stride is reported along with its standard deviation contour. Blue line represents the variable for TM and red line the variable under AM combining
flexor and extensor torque peaks. 92
Fig. 44 - Hip joint kinetic variables. The upper graphs show the measured (continuous line) along with its standard deviation contour and desired torque (dotted line) powered by the left and right SEA. Each curve represents a different level of assistance. The lower graphs show the SEA power along with its standard deviation contour Each curve represents a different level of assistance. Kinetic variables are averaged over each gait cycle and reported over the percentage
of the stride period. 93
Fig. 45 - Bar-plot of the estimated mechanical work transferred on the user limbs for left and right joint. Each bar-plot averaged for each gait cycle refers to a different level of assistance. 94
List of tables
Table 1 - Average and Standard Deviation of step response characterization. [47] 41 Table 2 - Average and standard deviation of RMSE between desired and measured torque
during walking 47
Table 3 - Average and standard deviation of the gait-cycle mean value of the SEA power
during walking 47
Table 4 - Average and RMSE of the Phase Reset Error and Average RMSE of the phase
prediction during steady-state walking. 83
Table 5 - Average and RMSE of the Phase Reset Error and Average RMSE of the phase prediction during walking speed variations. 83
Table 6 - Average and standard deviation of RMSE between desired and measured torque during walking and mean SEA power over the gait cycle. 93
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Abstract
Longer life expectation and lower birth rate are challenging themes of industrialized society in which elder population is still required to remain healthy and productive. Therefore, since many years many research teams have focused their studies in designing wearable robotic devices for lower-limb rehabilitation and assistance.
Key challenges of wearable robotics have lead to face the design and development of wearable devices with different approaches. Nevertheless, comfortable and ergonomic pHRI, non invasive and reliable cHRI, bioinspired adaptive control strategies are still opening issues of this research field. In this work a lower limb wearable device, in particular an APO for gait assistance, will be presented and described in its mechatronic components and in the dynamic characterization of the controller.
Our study will be focused on developing and validating a phase prediction algorithm based on adaptive oscillator. The main element of the algorithm is the adaptive frequency oscillator, a mathematical tool capable to learn the features of a non-sinusoidal signal such as its frequency (and consequently the phase) and envelope modeling the inherent limit cycle of its dynamical system. The system was extended by means of an additional state variable responsible of synchronizing the null phase with a desired reference event. Experimental validation of the phase predictor pointed out its capability to instantaneously provide the phase of gait of the wearer simply monitoring plantar pressure signals through non invasive and reliable sensors. The phase of gait is then exploited to generate a phase-based torque aiming at assisting specific moments of the gait cycle. Furthermore an additional experimental session was carried out the prove the feasibility of the phase-based assistive strategy.
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Chapter 1:
Introduction
1.1.
Gait disorders and effect on industrialized society
The progressive decrease of birth rate and the higher life expectancy are important factors that characterize our society. Indeed, the ageing of the population is one of the most critical challenges that current industrialized societies will face in the next years, since it threatens the sustainability of our social welfare. In the next 40 years, nearly 35% of the European population will be older than 60, hence the urgency to provide solutions enabling our ageing society to remain active, creative, productive, and – above all – independent [1]. Gait disorders and lower-limb impairments are common and often devastating companions of ageing [1]-[3]: several population-based studies showed a 35% prevalence of gait disorders among persons over age 70, and 80% over 85 years of age [4]. Gait disturbances have major consequences, including falls (leading to major fractures or head trauma), the number of which is expected to reach 500,000 by the year 2040 in US, representing a total annual cost of 16 billion dollars [5]. Other consequences are the reduced mobility, which leads to loss of independence, and the reduced survival, which can be attributed to a combination of fatal falls, reduced cardiovascular fitness, and death from an underlying disease [3]-[4], [6].
Normal gait consists of three primary components: locomotion, including initiation and maintenance of rhythmic stepping; balance; and the ability to adapt to the environment. Normal deterioration of physiological functions due to ageing and pathological dysfunctions can affect one or more of these systems, giving rise to gait disorders.
Spontaneous walking speed normally decreases by about 1% per year from age 60 onward [7], and the observed decline of maximum walking speed is even greater. By the way 20% of very old individuals walk normally, hence gait disorders are certainly not an inevitable feature of old age [8]. Senile gait disorders could thus be an early manifestation of an underlying pathology, most notably subtle white-matter changes, vestibular dysfunction, visual changes, or oculomotor changes. Such disorders might alter gait directly, but may also act in an indirect way by causing a subjective sensation of instability and insecurity, forcing individuals to adopt a more cautious gait [9]. The association between ageing and gait disorders, is thus a complex, yet open issue, which is well summarized by Snijders et al. [3]. Ageing also affects the level of cognitive workload in gait-related motion tasks. Walking is traditionally seen as an automatic motor task that requires little, if any, higher mental functions. However, the safety and efficacy of normal walking rely not only on sensorimotor systems, but also critically depend on the interaction between the integration and decision of action with the cognitive dimension. A common situation where such an integration is challenged is when people must walk while performing a secondary task. In
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elderly people, this ability deteriorates because central resources decline. This leads to the fact that more mental effort is required in elderly people to perform locomotion. This further increases the complexity of the task, or dually, increases the chance of gait disfunctions. A possible scenario for the next years is that ageing-related gait syndromes will lead to a tremendous increase of the number of people needing assistance in their fundamental activities of daily living. There will be an increasing number of elderly, impaired people needing and relying on high-intensity human care for basic mobility, personal hygiene and safety awareness. Loss of autonomy of an increasing number of people will lead to major societal and economic costs (e.g. costs for medical care, costs of home assistance). It is plausible that people will become progressively more reliant on technology to meet their own needs to live active, fulfilling, and independent lives.
1.2.
Biomechanics of gait
Biomechanics is the application of methods and techniques from mechanical science to the analysis and understanding of biological systems. From a macroscopic point of view, the human body can be modeled as a set of rigid segments; this model allows to study human motion according to the postulates of continuum mechanics. The rigid segments are represented by bones covered by soft tissue and linked by articulations which represent the joints of the kinematic chain. It is usually assumed that there is no linear translation in the joints but only rotational motion with an instantaneous displacement of the centre of rotation. Internal forces and moments can be derived by knowing point of application of external forces and torques. However how forces are distributed in the muscles, that are the actuation units of a biological system, is an ambiguous problem. Due to redundancy, knowing the contribution of each muscle involved in the accomplishment of a motion task is not easy to assess, reason why minimization and optimization criterion are required to estimate the powered forces [10].
Description of anatomical human motion in medicine explains the relative movement between the bones and the range of motion (RoM) of the joints referring to three anatomical planes of the body. Frontal or coronal plane divides body in anterior and posterior parts, transversal plane divides the body into upper and lower parts, sagittal or lateral plane divides the body into right and left parts. Movements on coronal plane are called abduction (it refers to a motion that pulls a structure or part away from the midline of the body) and adduction (it refers to a motion that pulls a structure or part toward the midline of the body, or towards the midline of a limb). Movements on the sagittal plane are called flexion (it describes a bending movement that decreases the angle between two consecutive segments) and extension (it is the opposite of flexion, describing a straightening movement that increases the angle between two consecutive segments). Other
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descriptions of movement include rotation either internal or external which is the rotation of a joint around the long axis of the limb in a circular motion [11].
From a biomechanical point of view the lower limb can be studied as a kinematic chain that, according to the phase of gait, switches from a close kinematic chain (when the foot is in contact with the ground) to an open one (when the foot is not in contact with the ground and the limb is moved forward in the direction of the gait). Such kinematic chain is composed by the following parts: pelvis, thigh, shank and foot. The three main joints connecting the bones are the hip, knee and ankle which respectively joint the pelvis to the upper leg, the thigh to the shank and the shank to the foot [10]. For our investigation we will focus on a more detailed description of the hip joint, since it corresponds to the actuated joint of the device we will present in next chapters.
1.2.1.
Definitions from Winter [11]
Hereafter, according to Winter [11], we are going to provide definitions for some critical events and phases (Fig. 1) which characterize human walking:
Heel Strike (HS): is the instant when the heel of the foot strikes with the ground. Foot Flat (FF): is the instant in which the foot is flat on the ground.
Toe off (TO): is the instant when the toe of the foot leaves the ground. It usually defines the end of the stance phase.
Stride Period: is the period of time for two steps in seconds measured from an event of one foot (e.g. HS) to its subsequent occurrence on the same foot. It is also commonly expressed as 0 to 100% to compare subjects with different stride periods, or successive strides of the same subject.
Double Support: is the period of time in which both feet are in contact with the ground expressed in seconds or in percentage of stride period.
Single Support: is the period of time when only one limb is in contact with the ground expressed in seconds or in percentage of stride period.
Stance Period: is the period of time when the foot is in contact with the ground expressed in seconds or percentage of the stride period. Stance period can be subdivided in sub-events.
Weight Acceptance: is the period of time between HS and the maximum knee flexion to support limb during stance. During this time energy absorption takes place by the ankle, knee and hip joints.
Push Off (PO): is the period of time in the late stance when the limb is pushing away from the ground.
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Swing Period: is the period of time when the foot is not in contact with the ground expressed in seconds or in percentage of the stride period.
Ground Clearance: is the distance between the foot and the ground during the swing period.
Stride Length: is the horizontal distance covered along the plane of progression during one stride. The sum of the two steps is equal for left and right limbs if the person shows marked gait asymmetry.
Cadence: is the number of steps per unit time expressed as steps/min = 120/Stride Period. Natural or free cadence is the cadence that the subject achieves when he is asked to walk as naturally as possible. Natural cadence in adult male subjects varies from 98 to 116 steps/min. The average cadence for female is 6 to 11 steps/min higher than the male one. Height is a not negligible factor to determine cadence and stride length. Relevant study showed that both cadence and stride length decrease in elderly subjects. However elderly are not an homogeneous group since apparently conflicting measures are reported from several investigations.
For healthy subjects walking at natural cadence, all researchers report consistent results: 58% to 61% of stride period for stance phase, and 39% to 42% of stride period for swing phase. The results showed that when the subjects are instructed to walk at their natural or preferred speed, without the aid of visual or aural cues, the subjects tends to keep the cadence and stride length less variable than other walking speed.
However, in biomechanical gait studies, problem of variability is unavoidable. The intra-subject repeatability of joint angle motion is influenced by the inherent physiological variability as well as those introduced by the measurement system. The latter includes the effects of finite accuracy and resolution of the motion analysis system and the marker system used in the computation of joint angle patterns. The inter-subject variability, introduced by different factors (e.g. anatomical dimensions, work powered by muscles, age and gender), affects the statistical analysis [12].
For this reasons an important measure to consider in biomechanics studies is coefficient of variation (CV). CV is a statistical measurement represented by a normalized measure of the
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dispersion of a probability distribution. It shows the extent of variability in relation to the mean value of a population.
To reduce their dispersion and variability, kinematic and kinetic variables are often normalized for the height (in the case of kinematic variables) or the weight (in the case of kinetic variables) [11].
1.2.2.
Hip joint biomechanics
The hip joint is composed by the cup-shaped acetabulum, the pelvis and the head of femur which forms two-thirds of a sphere. It is supported by several ligaments that restrain movements and actuated by the strong lower limb muscles which provide stability to the joint. The hip joint is an example of the so called ball-and-socked synovial joint. The term ball-and-socket refers to the possibility of accomplishing rotations in each plane allowing a large RoM, i.e. in the case of a spherical mechanical joint, while the term synovial indicates a typology of anatomical joint that has an high level of mobility without frictions between the bones [10].
The movement that brings the thigh forward and upward is flexion, the opposite one is the extension. Most important muscles involved in the hip flexion are iliopsoas, rectus femoris and sartoriuos while the extension is accomplished by posterior muscles such the gluteus maximus,
Fig. 2 - Hip joint flexion angle reported from Winter dataset segmented over the stride period.
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semitendinous and biceps femoris. The range of the hip flexion/extension goes from -20 deg in extension and up to 120 deg in flexion.
In abduction the lower limb is moved away from the mid-line of the body thanks to the lateral muscles. On the contrary, adduction is the movement that gets the limb closer to the mid-line of the body. It is produced by adductor muscles. The range of the hip abduction/adduction goes from 40 deg in abduction to -35 deg in adduction.
The medial/lateral rotation is the rotation around the long axis of the femur. The medial rotation or inversion has a RoM of -20 deg while the lateral rotation or eversion has a RoM up to 60 deg [10], [11]. Hip joint angle has a quasi-sinusoidal behavior (Fig. 2). It is worth noting that in several studies the angle is positive when the hip is flexed and negative in extension. On average, hip joint is flexed at 20 deg at HS and decrease down to -10 deg in the extension during the stance phase. At 58-61% of the stride period the TO occurs and the hip joint starts to flex to accomplish the swing phase advancing the limb in the plane of progression. The maximum averaged flexion angle of 22 deg is reached at 80% of stride period. Walking at slow cadence (20 steps/min lower than natural cadence), hip joint angle reaches a lower flexion angle while such variation is not measured during extension.
Hip joint velocity is considered positive when the angle is flexing. From the HS the hip velocity is negative and reaches a minimum of -2 rad/s at 20% of stride period in extension. The sign of velocity (and consequently the movement direction) changes at 50% of stride period before the PO and reaches a maximum positive value of 3.5 rad/s during the swing phase. The eventual cross-zero, that corresponds to the maximum flexion angle, occurs at 80% of stride period. In slow cadence the hip joint velocity is reduced to the 20% respect with natural cadence.
Kinetic variables reported in Winter [11] are normalized for the body mass of the subject in order to reduce the CV of the studied variables. Torques and powers powered by the joints, indeed, are normalized for the body mass to reduce the inter-subject variability. Concerning the
Fig. 3 - Hip joint torque reported from Winter dataset segmented over the stride period. The sign of the torque is chosen according to Winter convention.
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hip joint torques, biomechanical convention (the same that we used in our device to define the sign of the torque) considers the extensor torque of the hip as negative while the flexor torque has positive sign.
Convention used by Winter defines all extensor torques as positive since they tend to push the body away from the ground; flexor torques have negative sign. In the sagittal plane, the extensor torque is active since the HS of the limb when the torque reaches a peak of 0.4 N·m/kg providing support during the stance period. From the 20% to the 70% of the stride period a flexor torque is exerted by the hip joint to move the limb forward in the plane of progression to accomplish the swing phase; a maximum flexor torque of 0.35 N·m/kg is powered around the 50% of the stride period (Fig. 3). Hip power, reported in Fig. 4, is calculated by multiplying the moment powered by the muscles exerted on the hip joint (sign is taken according to biomechanics convention) and the hip joint velocity. A typical power profile according to the averaged hip power form Winter has the following features: a positive power of 0.2 W/kg at the beginning of the stride indicates that the angle is in extension and muscles are powering an extensor torque on the joint to stabilize the initial stance phase of the stride. A low amount of power is absorbed by the hip joint from the 20% to the 40% (with minimum value of -0.1 W/kg). Around the 60% of the stride period the hip joint develops the highest amount of power (up to 0.3 W/kg) to accomplish the swing phase and move the limb forward in the plane of progression [11].
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1.2.3.
Ground reaction forces
Another important variable that will be exploited in our work is the ground reaction force (GRF). GRFs is equal in magnitude and opposite in direction to the force that the body exerts on the supporting surface through the foot. GRF is represented by a force vector in a 3D space applied in the instantaneous centre of pressure (CoP) of the foot. Given a defined reference system of the foot, the CoP is a point of the reference system in which the force is applied, and the distance between the origin of the reference system and the CoP is the lever arm of the GRF. For our study, we will focus on the analysis of the vertical component of the GRF (namely vGRF). At natural cadence the vGRF, normalized by the body mass of the subject, varies instantaneously from a null value during the swing phase up to 10 N/kg in the initial stance phase. A local minimum is found at 30% of the of the stride period during the mid stance while the limb is in full contact with the ground. The force rapidly comes back to its null value at 60% of stride period after a second peak represented by the PO [11].
1.3.
Biomechanics of gait in elderly: a qualitative comparison
Results from several studies on gait of elderly [13]-[15] showed that the torque exerted by lower limb muscles can have a different behavior with respect to healthy adults muscles; this is due to sarcopenia, namely a condition of weakness and reduced muscle dimension, or neurological disorders which imply the adoption of cautious gait by the subject. It is thought from therapist that sarcopenia can be understandably treated with strengthening muscular programs to enforce functionality of the muscular tissue; actually such prescription does not improve the walking ability of elderly [13]. While hip extensor muscles show evidences of weakness, making the gait of elderly less stable during the stance phase, flexor muscles tend to produce higher torques to accomplish the swing of the limb. A number of studies comparing kinematics between elderly and young adult subjects have reported subtle age-associated reductions in total joint ranges, particularly about the hip and ankle. The main evidences of alteration in the gait pattern are age-associated reduction in hip moment of force and age-associated reduction in peak ankle power generation at pre-swing [14], [15].
Peak hip extension was reduced in the elderly group at comfortable walking speed. The reduction in hip extension range in the elderly at both comfortable speed and faster cadences is consistent with the prevalence of hip flexion contractures, which prevent the hip from achieving full hyperextension. The observed reduction in hip extension is associated with an overall increase in anterior pelvic tilt.
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The persistent reduction in both peak ankle plantar-flexion and ankle power generation at both walking speeds suggests restricted ankle plantar-flexion range. Since normal ankle plantar-flexion in gait is only 35 deg to 40 deg and the elderly have only a small reduction in this parameter compared with young adults (approximately from 40 deg to 45 deg), an ordinary ankle plantar-flexion contracture can be ruled out. Alternatively, elderly subjects may have reduced ankle plantar-flexor strength, which is consistent with prior reports that ankle plantar-flexor power tested statically is specifically reduced in the elderly and correlates with their reduction in gait speed [14], [15].
It is difficult to determine whether peak hip extension or reduced ankle power are responsible for limiting step length and increasing double support time, or whether reduced hip extension is used as a strategy to maintain balance, thereby reducing step length and double support time. Alternatively, both could be secondarily related to some other, un- known impairment or strategy [15]. This means that assisting gait in elderly should target to stabilize the limb during the stance phase and assist the swing phase reducing the additional torque powered by the muscles.
It is evident that a lower peak of power is provided by the ankle joints of elderly at the moment of PO. As a consequence, it denotes an increase of power generated by the hip joint to compensate plantar-flexion energy provided by the ankle to keep a reasonable ground clearance during the swing phase of gait. This preliminary observation confirms the needing of assistance especially in the initial swing phase to compensate weakness of ankle joint and to reduce the additional effort provided by hip flexor muscles.
1.4.
Wearable robotics
Since many years research in robotics has moved away from its primary focus on industrial applications. For more than half a century robots were minimalist mechanical devices, far from achieving any general human-like performance, and there was essentially no interaction between humans and robots, except for a programmer uploading code. With the advance of information technology and mechanical design, vision of human-like, autonomous, and interactive robots has gradually become within the reach of research prototypes. Biomechanics studies and modeling of bio-inspired behaviors have finally opened new horizons to robotics that is going to be human-centered, i.e., robots will work with humans in normal human environments [16], [17].
Elder care, physical therapy, child education, search and rescue, and general assistance in daily and social life situations are some of the examples that will benefit from the new robotics in the near future. In the specific field of rehabilitation and healthcare, robots can act as physical therapists to help patients exercise properly and regain lost motor functions or can be well studied
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to deal with impairments leading to replace loss anatomical parts and empower weak functionality. These are all topics that are often discussed under the name of assistive robotics.
Wearable robots are person-oriented robots literally worn by human operator to supplement the functionality of a limb or to replace it completely; given the close interaction with the user they are anthropomorphic in nature and fits closely to his or her body. They may operate alongside with the limb as in the case of orthotics or they may operate as prosthetic devices replacing a limb following an amputation. In the first case the design is studied aiming at different operations such as complementing, enhancing or substituting functions and capabilities of the user [10]. Even if wearability implies the coupling between the robot and the user it does not necessary imply that these devices are ambulatory, portable or autonomous. Indeed, they can be exploited in laboratory environment or in rehabilitation tasks. Not negligible features are the weight, compatibility with user’s joints RoM and anthropometry [17]-[20].
1.4.1.
Physical human robot interaction
An important feature of wearable robotics is the intrinsic interaction between human and robot. It implies a physical coupling that leads to several complications. Superimposing a wearable robot on human limb represents a key aspect in designing such a device to achieve ergonomic and non-invasive interfaces.
The physical human–robot interaction (pHRI) should interact with large areas and should match the shape of the patient’s limb to reduce the pressure on the user’s skin [10], [20]. Ergonomics and comfort are not negligible in the design of a device that has to fit and adapt to the specific shape of a wide range of users. Furthermore, application of additional loads on certain areas of the human body has to take in account the intensity of the exerted pressure, thus surfaces of interaction should be wide to reduce such pressure and contain it within the limit of pressure discomfort and pain threshold. Finally an exoskeleton should be easy to don and doff, adjust to different wearers with fast and easy procedures.
Ideally the human must feel no restriction to his/her intentional movement, resulting in high level of compliance and high kinematic compatibility. A misalignment between exoskeleton joints and anatomical joints is translated in an uncomfortable or even painful interaction due to an incorrect force transmission or an overloading of the joints. This means that the robot should endowed not only actuated DoFs in correspondence of the actuated joints, but also passive DoFs which allow to instantaneously adapt the position of the centre of rotation of the specific joint [17], [19], [20]. Safety has to be guaranteed during utilization, e.g. avoiding fast movements or hyperextension/flexion of joints. Furthermore, the actuation and control should allow the robot to safely provide assistance with minimum-to-null output impedance: namely the user should be able to move without being hindered by the robot while receiving assistance as needed [21],[22].
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On an actuated joint of an assistive device the actuation system should be compliant and characterized by a stiffness similar to the one of the corresponding anatomical joint. The actuation axes have to be instantaneously aligned with the rotational axes of the articulations in order to avoid the transferring of load on articulations themselves. Since the rotational axes of the articulations during motion moves, also with respect to body segments, the position of the actuation axes should be instantaneously adjusted to follow the movement of the limb. Moreover, it should be adjustable due to high inter-subject variability of anatomical dimensions [10].
1.4.2.
Cognitive human robot interaction
Given the close interaction between the robot and the wearer the development of an active exoskeleton should allow to transfer a net flux of power bi-directionally from the wearer to the device and vice-versa. The relationship between humans and robots transcend the boundaries of simple physical interaction. It involves sensors, actuators and control strategies capable to gather and decode complex expressions aiming ad adapting and optimizing their functions transmitting back information and resulting in a cognitive process. The cognitive process can be seen as a sequence of task including learning, planning and executing an identified goal.
Ideally, the user should fully control the robot in order to have it synchronized with his/her intentions and, consequently, to benefit from the supplied assistance while performing the movement. For this reason the paradigm of the master-slave configuration (where the master is represented by the operator and the slave by the robot) has to be redefined; a common strategy for the human–robot interface is the so-called “shared control”. This approach allows one to share the cognitive effort needed to control the platform; at one extreme, this means that one system, e.g., the human, instructs the other system, the robot, what to do. At the other extreme, the two systems could work together to improve life quality of the user [16]. Hence, exoskeletons allow the user to control the robot: in turn the device include the human in the control loop providing information about the accomplished task.
There are two main types of cognitive human robot interaction (cHRI): unidirectional cHRI to control the robot or a bidirectional interface represented by the closed-loop interaction between human and robot. In an unidirectional interaction the user does not receive any information from the device and about its status. By the contrary in a bidirectional interface the closed loop is defined like a continuous symbiotic interaction that enhance awareness, efficiency and a natural control by the user.
A rather unexplored and hard question of human-robot interaction is how to extract the intent and preferences of somebody’s movement [16]. In case of lower limb assistance and rehabilitation, the user needs to relay his intention to start walking, and then the device has to identify when assistance is required, e.g., for stabilization of the knee during stance or providing
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assistive torque to aid particularly high energetic task such as PO and swing phase. Risk of fall is not ethically acceptable; therefore, the cognitive interaction on which this relay of intentions is based must be 100% reliable. For example, it is not acceptable that the robot interprets that the limb is swinging when it is actually in stance. Relaying information on intention and volition (in terms of desired motor tasks) to drive, command, and control a robot is a difficult task whenever it has to be achieved in a natural way. Learning and planning process can be accomplished by the robot using different techniques which can be used singularly or in a multimodal approach. The basis of the detection of user’s intention can be monitored through brain activity, muscle activity or limbs motion (studied through kinetic and kinematic measurements) [10], [17].
Brain activity can be monitored with two different approaches: invasive interfaces based in intra-neural electrodes or cortical electrodes or minimally invasive interfaces such as electroencephalography (EEG). The first approach is not usually pursued, since in the field of wearable robotics the interface between human and robot should be characterized by a minimum level of invasiveness. Furthermore, there are several important questions regarding the control of mechatronic systems directly from brain-derived signals. The properties of the CNS are not prone to the design of effective but not too invasive interfaces; biological reactions, e.g., gliosis, lead to non-stable interfaces in the case of implantable electrodes; advanced algorithms need to be developed to extract useful neural information. As a consequence, surface and noninvasive techniques are much more common in practical control of assistive devices. The main purpose of a control interface based on EEG is to generate a set of features to identify a class of action. They can be categorized in two main groups: monitoring the response to a given stimulus or monitoring spontaneous activity of the subject, the latter with respect to the first class brings the advantage of letting the user control the timing of the control.
Skeletal muscles are the actuators responsible for human motion and play a key role in human robot interaction. In most applications of wearable robotics (e.g. empowering exoskeleton or assistive devices), the control system acts like a muscle activity amplifier. A large number electromyography (EMG)-based controllers were proposed in the past years to detect the user motion intention for the control of exoskeletons [24]. These systems correlated the user’s EMG with the muscular joint force/torque using either model-based or model-free approaches. These robotic devices provided the wearer with a fraction of the estimated torque to decrease his/her effort. Despite these premises, EMG-based approaches have some drawbacks, mainly related to signal acquisition, and user-specific calibration. Electrodes positioning, as well as skin condition, greatly affects the recorded signal. As a consequence, EMG-based controllers require not only a long custom calibration for each user but also additional calibrations between and within experimental sessions. Moreover, model-based torque estimation can have poor accuracy and requires large computational effort [21], [22].
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The cHRI based on kinematic or pressure measurements can face different applications and it is often the preferred approach since a non-invasive sensory apparatus (encoders, goniometers, accelerometers or pressure sensors) is involved. Information coming from sensory apparatus endowed in the device are combined to extract features allowing to recognize transitions between different kinematic/kinetic conditions related to the task. Such a process can be described through stochastic models like Hidden Markov Model [10], [24]. Classification can also be achieved by means of threshold-based classifiers or neural networks.
1.4.3.
Bioinspiration and adaptivity
The design of an active orthosis or exoskeleton should take in account scientific areas such as biology, medicine, biomechanics and neuroscience. In specific applications a wearable robots can benefit from bio-inspired models and biomechatronic systems in aspect like control, sensing and actuation. Biomechatronisc is a scientific and engineering discipline whose goal is to replicate biological behavior by means of artificial models, e.g. sensors, actuators and control systems. Biomechatronics intrinsically includes bioinspiration in the development of the mechatronic systems and/or in some of their components, since it deals with the close interaction with a biological system (both physically and cognitively). Finally, one of the most discussed aspects of a wearable bioinspired device is represented by adaptivity. A biological system is indeed capable to deal with unpredictable situations, is robust to failure and can slightly adapt its behavior learning from the environment. From an exoskeleton design perspective source of variability in the execution of a movement using the same degrees of freedom (DoFs) carried on by different users is a crucial aspect to consider.
Thus, the human robot interface is essential for the robot to properly understand the operator goal and modeling its behavior and dynamics. This approach is defined as bioimitation and involves understanding the intention of accomplishing an action and how to replicate it and adapt to variations in the execution of the task [10]. It is often advantageous for robots to employ similar movement strategies to those of humans to be more easily accepted due to their inherent tuning to natural looking movement. An example of this approach is represented by limit-cycle learning through the utilization of dynamical models inspired from natural systems; as inherent property, a high level of similarity to natural periodic task, for instance walking task, can be achieved (Fig. 5).
Walking can be seen as a limit-cycle system being a sequence of steps that is stable as a whole but not instantaneously, i.e. a series of repetitions of the same closed trajectory which is not locally stable in every instant of the cycle. In the case of robots assisting limb motions, these high-level commands must specify the characteristics (e.g., direction, velocity, and amplitude) of the intended movement. The topic becomes even more complex if the interaction dynamics
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between a robot and its environment are taken into account - every walking system, for instance, has this interaction dynamics due to GRFs. Interactions with the environment create constraints, and the mathematics of model-based control becomes significantly more complex [21],[22].
1.5.
Objectives of the work
In the previous paragraphs, the most relevant premises for our work have been described. Ageing of society and the consequent need to develop assistive devices which can improve the quality of daily life in elderly people are the key issues of this work.
A wearable active pelvis orthosis will be presented and described in the next chapters in its mechatronic components. The first part of the work was focused in providing results of the dynamical characterization of the controller performance.
Within the next chapters a real-time gait predictor algorithm and a new adaptive assistive strategy for lower-limb orthoses will be described. The main element of the gait-phase predictor is an adaptive oscillator. Thanks to their inherent stable limit cycle, adaptive oscillators can learn and extract the main features of a periodic non-sinusoidal signal. The algorithm holds one of the most important features that a cHRI should have: indeed, thanks to the information acquired from the sensory apparatus, adaptive oscillators are capable to learn in real time the gait-phase and constantly keep themselves synchronized with user gait pattern (e.g. faster/slower walking speed) and to any changes in environmental condition (e.g. up-hill or down-hill walking). The cHRI exploits the flux of information coming from non-invasive and fully wearable sensory apparatus,
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thus avoiding the utilization of any brain derived signals, like EEG, or utilization of bio-signals, like EMG signals. Then, the information coming from the gait-phase predictor is translated in an adaptive assistive torque which can be consequently adapted to the gait pattern of the wearer. The strategy relies on a “model-free” approach, meaning that neither inverse dynamic model of lower-limbs nor pre-programmed kinematic trajectories are needed to detect and replicate wearer intentions. Furthermore, adaptivity ensures the robot to accomplish assistive task independently on the wearer from the first utilization.
In order to validate the performance of the control strategy, it was embedded in the high-level layer of the control architecture of the above-mentioned wearable device for lower-limb assistance. Thus, a flexible .dll library in RT Labview Core environment was developed; such library should be able to output the phase of gait of the wearer learning features of variables recorded through the system sensory apparatus. Finally, experimental sessions carried out to validate the phase prediction algorithm and to assess the feasibility of the phase-based assistive strategy will be reported and discussed.
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Chapter 2:
Active lower-limb exoskeletons and
orthoses
In this chapter we will focus the attention in providing an overview of the state of the art of the devices developed in recent years. In the last section, furthermore, a detailed description of our device and of its mechatronic system will be provided.
2.1.
State of the art
In recent years many research teams have moved their attention in studying and developing systems which aim is at empowering human capability in tasks that require high energy expenditure, e.g. load carriage, rehabilitating lower limbs in subjects who suffer of neurological disorders affecting gait, and finally assisting weakness ability and functionality of impaired subjects in daily life activities. A main classification of the existing devices was made distinguishing among multi-joint exoskeleton, namely for wearable suit which endow multiple actuated joint, and single joint exoskeletons or orthoses, namely in case of a single actuated joint (this does not exclude that passive mechanisms are designed to be coupled with other articulations). In this section an overview of exoskeleonts and orthoses developed in recent years [21]-[23] will be provided focusing on the control strategy they implement to assist, rehabilitate or empower functionality of the wearer.
2.1.1.
Multi-joint lower-limb exoskeletons
Hereafter we are going to present multi-joint exoskeletons. Since the actuation is endowed for more than one joint they can be classified as wearable suit assisting both the upper and lower parts of the human body, or in specific case of lower limbs they consist of kinematic chain over-imposed on the lower limb of the wearer.
2.1.1.1.
Sensitivity amplification control
Sensitivity amplification control method is designed to match a trade-off between increasing sensitivity to external forces and torques applied in the exoskeleton ensuring high robustness and disturbance rejection.
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Berkley Lower Extremities Exoskeleton: BLEEX, shown in Fig. 6-a, was first unveiled in 2004
at U.C. Berkeley’s Human Engineering and Robotics Laboratory and it is the first load-carrying and energetically autonomous exoskeleton [25]. Instead of powering the joints of the pilot directly, BLEEX transfers the load weight to the ground while moving in parallel to the human. With a pseudo-anthropomorphic design, BLEEX has a left and right three-segment leg in analogy to the human thigh, shank and foot. Each leg has 7 DoFs: three DoFs at the hip, one DoF at the knee, and three DoFs at the ankle. The control scheme of BLEEX aims at shadowing the pilot’s movements in real-time. This means BLEEX is sensitive to forces and torques coming from the dynamical interaction with the wearer but not from external forces, e.g. pushing. BLEEX control is realized by means of a positive feedback controller which exploits the exoskeleton inverse dynamic model making the stability of the controller highly dependent on its precision [26]. The control strategy is based on three separated gait phases: single support, double support and double support with one redundancy. Each phase corresponds to an independent dynamic model properly chosen by the controller depending on gait segmentation evaluated through sole pressure sensors.
The Body Extender: BE, developed by PERCRO laboratory of Scuola Superiore Sant’Anna
(Italy), is an advanced power-augmenting full-body wearable robot for handling heavy objects (Fig. 6-b). Like BLEEX, the weight of the payload is transferred to the ground through anthropomorphic legs. It has twenty-two independently actuated DOFs, with six DOFs on each leg. The first controller version was realized by mapping operator forces over joint velocities according to an established dynamic model like in BLEEX. The goal of the control is to compensate the inertia and weights of the BE itself [27].
Fig. 6 - a) Berkley Lower Extremities Exoskeleton . b) Body extender. Adapted from [25], [27].
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2.1.1.2.
Cybernic control
Cybernics is a neologism indicating the fusion of interdisciplinary academic fields dealing with human-assistive technology aimed at enhancing, supporting and expanding the capabilities of the wearer. It was born from several scientific areas such as neuroscience, physiology, robotics, computer science, and behavioral science [28]. In a cybernic control framework, the desired joints kinetic/kinematic variables are pre-recorded with a healthy wearer or adopted from human walking gait databases.
Hibrid Assistive Limb: A remarkable application of cybernic control strategy is the exoskeleton
HAL, from University of Tsukuba (Japan), which was developed to physically assist people affected by gait disorders in moving and executing daily life activities as healthy individual. HAL’s first developments started in 1992. Two primary versions used in a actual life exist: HAL-3 and HAL-5. HAL-HAL-3 is a 15-kg weight prototype of lower-limb exoskeleton with the purpose of aiding walking (Fig. 7-a); HAL-5 is a 23-kg weight full-body exoskeleton, powering also the upper-limbs. Based on these prototypes, a single leg version of HAL has been developed as well, which only weights approximately 7 kg [29]. The different prototypes have however the same system assembly, including power units (directly fastened on each joint), exoskeleton frames, sensors and controller. Each leg of HAL works on a sagittal plane and hip, knee and ankle joints have one DOF respectively (while hip and knee joint are actuated, the ankle joint endowed a passive spring). The control strategies used in HAL can be divided into two subsystems: cybernic voluntary control (CVC) and cybernic autonomous control (CAC). The CVC provides assistive torques for each joint by amplifying the torques exerted by the wearer, which is calculated from EMG. Hence, CVC is appropriate for healthy wearers, or people who can elicit a minimal residual muscular activity. On the other hand, in case of gait disorders, these biosignals are not sufficiently reliable for the CVC to work properly. So a CAC is used to provide a sufficient support for the wearer with a previously recorded motion sequence from a healthy subject [28].
MIT Exoskeletons: Two exoskeletal structures have been developed in Massachusetts Institute
of Technology (MIT) for load-carrying augmentation during walk [30]. The first version endowed springs at the hip and at the ankle and a variable-damping mechanism at the knee. In the second version, springs are replaced by an actuator providing power for the flexion/extension based on the gait cycle (Fig. 7-b). The active version has three DOFs at the hip, one at the knee, two at the ankle and one at the foot. The exoskeleton works in parallel to the wearer, transferring the load of the back-pack to the ground through a pelvic harness. The sensory system provides joints angles by means of rotary potentiometers on the hip and knee joints; bending moment of the shank and vertical force in the exoskeleton leg are monitored through strain gauges on the exoskeleton shank. Finally human-exoskeleton interaction force is read by force sensors on the thigh cuffs.
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Segmented gait cycle is used in a state-machine controller triggered by variables recorded from the sensory system. The desired torque is calculated by referring to data from a gait database [30].
Vanderbilt Orthosis: At Vanderbilt University (Tennessee, USA), a powered lower-limb
orthosis that is intended to provide gait assistance to spinal cord injured (SCI) individuals by providing assistive torques at both hip and knee joints was designed [31]. The orthosis, shown in Fig. 7-c, has a mass of 12 kg and is capable of providing maximum joint torques of 40 N·m with hip and knee joint RoMs from 105 deg flexion to 30 deg extension and 105 deg flexion to 10 deg hyperextension, respectively. Electrical power is provided by a lithium polymer battery which ensures one hour of continuous walking. In order to demonstrate the ability of the orthosis to assist walking, the orthosis was experimentally implemented on a paraplegic subject with a T10 complete injury. Experiment indicates a high degree of step-to-step repeatability of hip and knee trajectories (as enforced by the orthosis) and an average walking speed of 0.8 km/h. The electrical power required at each hip and knee joint during gait was approximately 25 and 27 W, respectively, contributing to the 117 W overall electrical power required by the device during walking. Each joint is powered by a brushless DC motor through a 24:1 gear reduction, which provides each joint with a maximum continuous joint torque of 12 N·m, and shorter duration maximum torques of approximately 40 N·m. The orthosis controller consists of a state-flow system with four states. Each state is defined by a set of joint angle trajectories, which are enforced by high-gain proportional-derivative (PD) control loops. Joint angle trajectories were preprogrammed for each motion based on normal biomechanical walking trajectories, obtained from a recording of the joint angle trajectories generated by a healthy subject while wearing the orthosis, resulting in an example of cybernic control. The states are switched between by voice command of the user [31].
Fig. 7 - a) Hybrid Assistive Limb double leg version. b) MIT Exoskeleton. c) Vanderbilt Orthosis. d) ReWalk.
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Rewalk: The ReWalk is the very first commercially viable upright system which enables
individuals with lower limb disabilities to stand, walk and even take stairs independently (Fig. 7-d). By restoring vertical mobility ReWalk delivers benefits in overall health, social interaction, and achieving economical healthcare. ReWalk was designed and developed by Argo Medical Technologies (Haifa, Israel) with an approximate weight of 20 kg is actually a wearable, DC motor-driven robotic device worn outside the clothing. The bionic suit allows the user to walk by detecting shifts within the centre of mass position determining balance, and then moves the users legs in a natural gait. This assistive technology holds significant possible health benefits as a safe ambulatory powered orthosis for motor-complete thoracic-level spinal cord injury patients reducing most of the health problems associated with long-term wheelchair use. The newest generation of the ReWalk is the culmination of more than a decade of research and development. There are currently two models: the ReWalk Rehabilitation and the ReWalk Personal System. ReWalk Rehab 2.0 has been approved by the FDA for use in a clinical settings and presently available at rehabilitation centers in the United States. ReWalk also comes in a personal version, currently available in Europe and awaiting authorization from the U.S. Food and Drug Administration (FDA) for release here in the U.S. The ReWalk Personal System is designed for day-to-day use in a range of environments.
2.1.1.3.
Model-based control
Model-based control is a control strategy in which the desired joint torques and forces are calculated on the basis of a human body model updated with measurements from the sensory apparatus.
Wearable Walking Helper: WWH is a wearable gravity-compensating support exoskeleton
developed in Japan in 2003, to assist the activities of daily life of disabled and elderly people [32]. It consists of an hip and a knee subsystems. The hip joint subsystem has two DOFs (actuated flexion/extension and passive abduction /adduction) and the knee joint subsystem has one actuated DOF for the flexion/extension. The assistive torques provided by WWH are proportional to the torques calculated based on an approximated human body model. Joints angles are measured by means of potentiometers and encoders.
Technische Universit at Berlin Powered Lower Extremity: TUPLEE exoskeleton covers the
right thigh, shank and foot, only actuating the knee flexion/extension through a linear actuator. TUPLEE has been designed based on the anatomy of one specific subject and is fastened with four straps at thigh and shank. Its controller uses a simplified biomechanical model which calculates the muscular torque exerted by the pilot by means of EMG signals provided by six surface electrodes. The requested torque is evaluated through a proportional EMG control, while
