Online muscle tone monitoring during robot-assisted therapy

Corso di Laurea Magistrale in Ingegneria Biomedica

Dipartimento di Ingegneria dell’Informazione

Università di Pisa

A.A. 2016-2017

Tesi di Laurea Magistrale

Online Muscle Tone Monitoring During

Robot-Assisted Therapy

Online Muscle Tone Monitoring During

Robot-Assisted Therapy

Federica Viggiano

Master Thesis May-November 2017

Departement of Information Engineering University of Pisa, Pisa, Italy

Department of Health Science and Technology ETH Zürich, Zürich, Switzerland

Raffaele Ranzani

Prof. Dr. Roger Gassert

Rehabilitation Engineering Laboratory ETH Zürich, Zürich, Switzerland

Prof. Dr. Giovanni Vozzi

Research Center "Enrico Piaggio" University of Pisa, Pisa, Italy

Abstract—Stroke patients can suffer from abnormalities in muscle tone, as spasticity and rarely rigidity. A continuous assessment of muscle tone is important during rehabilitation, and some therapy approaches require to control hypertonia. Unfortunately, clinical assessment scales are not reliable and accurate, and are difficult to administer multiple times during the therapy. The Rehabilitation Engineering Lab (ETH Zürich) has developed a two DoF robot for the rehabilitation of the hand after stroke, the ReHapticKnob. The objective of this thesis was to design a new sensori-motor therapy exercise for the ReHaptichKnob, which is visually appealing and independently usable by stroke patients. Furthermore, an assessment of muscle tone was included online the exercise, to assess the presence of spasticity and rigidity, and monitor their evolution over time. To design the exercise, a perturbation-based method for muscle tone estimation was implemented. Four types of perturbations were included in the therapy game, entirely designed in Unity. The validity of the muscle tone estimation method was preliminarily evaluated using a mock-up spring setup. The results of the experiments showed that the robot is able to track well rapid perturbation trajectories, and accurately indentify changes in forces produced by the deformations applied to the springs. These results suggested that the robot would be able to estimate changes in muscle tone developed during the therapy exercise.

I. INTRODUCTION

A. Muscle tone abnormalities in stroke

Muscle tone, in skeletal muscles, is a state of tension that is maintained continuously, which increases in resistance to passive stretch [1]. The tonic activity is present mainly in antigravity muscles and contributes to maintaining posture. Tone is determined both by passive mechanical visco-elastic properties of the muscles and by a neural component, due to stretch reflex activity [2]. Stroke patients can suffer from abnormalities in muscle tone. After a lesion to the upper motor neurons, 80% of patients experience hemiparesis of the contralateral limbs [3]. Hemiparesis is often associated with weakness, loss of dexterity and fatigue. After a certain amount of time from the lesion, hypertonia (i.e. an abnormal increase in muscle tone [4, 5]) could appear, resulting in an augmented resistance to externally imposed movements about a joint. Among the various form of hypertonia, spasticity and rigidity are major motor and functional disorders in subjects

with lesions of the upper motor neurons [6]. There is a wide range of prevalence of spasticity. According to some studies, 39% of patients with first-ever stroke are spastic after 12 months from the event [7]. More recent data show an occurence from 19% to 43% [8–12], evaluated between 3 to 18 months after stroke. Stroke in critical locations of the brain, such as the midbrain and the basal ganglia, can cause the onset of vascular parkinsonism (VP), which often leads to rigidity [13]. In a clinical study was reported that 13 patients on 17 with VP suffered from rigidity [14], while in another study 100% of the patients analyzed exhibited rigidity [15]. Spasticity and rigidity have different characteristics. One of the definition more accepted of spasticity had been formulated by Lance [16] in 1980 that described spasticity as "a motor disorder characterized by a velocity-dependent increase in tonic stretch reflexes ("muscle tone") with exaggerated tendon jerks, resulting from hyperexcitability of the stretch reflex, as one component of the upper motor neuron syndrome". Lance underlined the role of the neural component in generation of spasticity. But, more recently, it has been recognized that the heightened limb stiffness in spastic patients is due not only to increased reflex activity but also to altered mechanical properties of muscles [17]. Spasticity is a form of hypertonia in which the resistance to an imposed movement is velocity dependent: "the faster you do the stretch, the greater is the resistance and the more reflex activity you get" [4]. Stretch reflex threshold is also reduced [18, 19]. Furthermore, spas-ticity varies with the direction of the movement and does not affect flexors and extensors muscles equally [20]. It seems to affect only flexors of the upper limb and only extensors of the lower limb. Rigidity is a form of hypertonia in which resistance to passive movement is independent from velocity of stretch and from posture. A simultaneous co-contractions of agonist and antagonist muscles may occur and makes the generated resistance direction independent. As a consequence of spasticity, stroke patients may be affected by fixed postures, while this is not observed in the case of rigidity [19]. A factor that may contribute to the increased muscle tone in rigidity is, also in this case, a change in muscular visco-elastic properties. The stretch reflex is involved in the control

of muscle tone [21]. When a disturbance or a perturbation is applied to a limb, a typical sequence of muscle activity follows the displacement onset:

1) At first a short-loop response, M1, that is a monosy-naptic spinal reflex, is elicited between 25 −50 ms [22] after the onset;

2) Then long-loop responses, M2 and M3, transcortical reflexes, occur between 50 −75 ms and 75 −105 ms [23, 24] after the onset;

3) After the excitation of the reflexes, two voluntary responses follow, early voluntary corrections between 120 −180 ms and a steady state activity between 750 −1250 ms [25] after the onset.

In the past, clinicians and physiologist indicated that the main contribution to muscle tone was given by spinal stretch reflex. But, as it was described by Davidoff in 1992 [1] both neural and non-neural mechanisms participate in the maintenance of muscle tone and in the adjustement required to compensate for suddenly perturbations applied to a limb. The neural components include spinal short-latency and long-latency reflexes, followed by voluntary reactions. Long-loop responses supplement the spinal stretch reflex during the response to unexpected displacements. As regards hypertonia, it is still unclear if it is mostly due to an increased reflex activity or to changes in mechanical properties of the muscles. In stroke patients with upper limb spastic paresis was observed an enhanced activity of short-loop reflexes in the spastic side, more pronounced in the flexors than in the extensors, associated with a reduced activity and an impaired modulation of the long-loop reflexes. In total, it looked like the EMG activity was reduced in the hemiparetic limb [26], in contrast with the accepted definition of spasticity and with other findings [27]. It was noticed by Thilmann et al. in 1991 that an abnormal reflex activity is actually present in spastic patients. They also suggested that the role of reflexes is more important in spasticity generation between 1-3 months from the stroke event. Indeed, changes of intrinsic muscle properties are more of impact after 3 months. For what concerns rigidity, it is not yet clear though whether this pathology is associated with an abnormal response of stretch reflexes. However, long-latency stretch reflex seems to have an important role in parkinsonian rigidity [28]. In 1975 for the first time, it was reported that the long-latency component of stretch reflex was augmented in patients with Parkinson’s Disease [24], and the hypothesis was further confirmed by other research studies. These findings suggest the hypothesis that stretch reflexes could play a role also in post-stroke rigidity. As a result of tight limbs and abnormal postures, hypertonia negatively affects the quality of stroke patients’ life, because of the presence of pain, the reduced ability to perform daily life activities, the reduced late motor recovery [29, 30].

B. Muscle tone assessment

Monitoring muscle tone is relevant in some rehabilitation therapy approaches, as the Perfetti method [31], which requires to re-learn how to control muscle tone when it is abnormal.

The Perfetti Method is a rehabilitative approach developed by Prof. Carlo Perfetti in 1960’s, who supported the theory that post-stroke and hemiplegia recovery would require a recovery of the cognitive processes that had been altered by the injury suffered. In clinics, the possibility of differentiating spasticity and rigidity has practical importance in the diagnosis and management of altered muscle tone, also in pathologies different from stroke. Muscle tone can be assessed in two ways: with a clinical assessment, using scales, or with tech-nological devices. Muscle tone is clinically assessed using passive movement around a joint to determine the amount of muscle resistance provided [19]. The most frequently used scales for spasticity assessment are the Ashworth Scale (AS) and the Modified Ashworth Scale (MAS). The assessment is performed quantifying the perceived resistance when a limb is passively stretched. This method is liable to human error and subjectivity. Furthermore, these scales suffer from low sensitivity, that lead to a clustering effect, so most of the patients are rated with the middle grades [32]. It has been also questioned about the effectiveness of the MAS in correctly assessing spasticity, because it is not able to differentiate weather the increase of muscle tone is neurogenic, a result of mechanical changes in tissue stiffness or both [33]. Such clinical scales also do not address the velocity-dependent aspect of the phenomenon. However, studies that evaluated inter-rater and intra-rater reliability of MAS showed a good reliability in assessing tone in the upper limb, but only moderate in the lower limb [34, 35]. Despite their limitations, the AS and the MAS are commonly used by clinicians, because they are easy and quick to use, and they do not need either intensive training or instrumentation to be carried out. Another clinical scale that could be a valid alternative in assessing spasticity is the Tardieu Scale [36]. According to Tardieu, three velocities are necessary to evaluate spasticity: a slow speed, below which stretch reflex is not elicited, and two fast speeds above the threshold necessary to trigger the stretch reflex. The scale takes into account the importance of velocity movement to differentiate between spastic hypertonia and rigidity/contracture (both velocity independent).

With the advancement of technological research, new in-struments have been provided to quantify muscle tone and assess hypertonia. In humans, skeletal muscle tone can be assessed measuring the resistance of a limb against mechanical perturbations [37]. We are not able to measure the tone of a single muscle, but rather the joint stiffness. Joint impedance estimation is commonly used to quantify muscle tone and it is defined as the ratio between the force or the torque applied to a joint and the linear or angular position of the joint:

Z = dF

dx (1)

There are two approaches to evaluate joint stiffness:

1) Apply a defined force and measure the relative displace-ment. Force pulses [38, 39] and stochastic force pertur-bations [40] are often used; sinusoidal perturpertur-bations have also been reported [41, 42];

2) Apply a defined displacement and measure the relative force. Ramps [43] or sinusoidal perturbations [44] are generally used.

The estimated joint stiffness is influenced by the experimental conditions as the subject task, and weather the perturbation is expected or not. The expectation of a perturbation could influence the long-latency reflexes response [1]. Motor tasks can be classified as active or passive, and static or dynamic. Active tasks refer to the subject that actively exerts forces, whereas passive tasks refer to the subject being in a state of muscle relaxation. Static tasks refer to the case where there is not displacement at the limb joints, whereas dynamic tasks refer to the case where the subject’s limb is moving. A wide number of experimental devices that attempted to measure muscle tone or joint stiffness was found in literature. Force perturbations have been used in several studies. In Lakie et al. 1984 [42] a sinusoidal torque was applied to the wrist in flexion-extension, to analyze muscle tone. The subject was relaxed and passive and the posture of the hand was fixed. In two studies [40, 45], stochastic perturbations have been used to estimate the endpoint stiffness of the arm. Random perturbations were chosen in order to minimize the likelihood of voluntary reactions. Unfortunately, force pertur-bations show a major limitation. According to Burdet et al. 2000 [46], a force perturbation of the same amplitude, applied at different points in the trajectory or in different directions, will displace the hand by different amounts, in a way that is not predictable. This is because limb stiffness depends on joint angle, angular velocity and perturbation direction. They instead recommended the application of displacement perturbations to estimate the endpoint stiffness of the arm using a planar parallel manipulator. Stiffness was identified using 40 trapezoidal displacement perturbations, along 8 di-rections randomly chosen, with 8 mm amplitude, and with a duration of 300 ms. During the experiment the subjects had to actively move the device following a predefined trajectory, while at a certain point the perturbation was applied. Gomi and Kawato in 1997 [43] experimented a method to measure joint impedance during multi-joint movements. The device used is a parallel manipulandum performing horizontal planar movements in 2 DOF. The subjects hold a handle while the forearm is coupled with it, in order to block the wrist. The first part of the experiment involved the use of a passive and static task, in which the subjects were instructed to relax the arm and to not exert any external force on the handle. Trapezoidal position perturbations of 6 −8 mm with a duration of 300 ms were applied to the hand, in 8 directions randomly ordered. The second part involved an active and dynamic task. The subject had to move the handle between two positions showed on a screen, and again he/she was instructed to not intervene voluntarily during the perturbations. In this case, force perturbation of 200 ms were employed. The method has given good results in estimating the stiffness and it has been shown that active tasks lead to higher estimated stiffness than passive ones, because muscle stiffness increases with

Figure 1. The ReHapticKnob

muscle contraction. Formica et al. in 2012 [47] proposed a quantitative method to estimate the passive stiffness of wrist and forearm using a wrist robot [48] with 3 DOF. The characterization was made on young healthy subjects. Angular trapezoidal displacements were applied, along a RoM from 30° to 90° at velocities that went from 6 °/s to 11 °/s. The velocity was kept low in order to not evoke reflexes. The hand of the subjects was closed around the robot handle and bound so that no muscle activity was required to grasp the motor handle. Subjects were instructed to relax and not interfere with the motion of the robot. To test whether muscles were indeed passive during the stiffness measurements, they sinchronously measured EMG in wrist muscles. Of particularly interest from this study is the fact that there are no prior reports of passive stiffness estimated in pronation-supination, probably due to the fact that pronation-supination hypertonia is quite rare. Hajian et Howe 1997 [49] conducted a study for the identification of end-point impedance at the human finger tip. A force sensor was placed on the piston of a pneumatic cylinder, able to measure the force exerted by the subject. The subjects were instructed to press upon the cylinder and increase finger force against the apparatus gradually. As the exerted force exceeded a certain threshold, the piston applied a displacement of 5 mm at the index finger in extension and in abduction, with a maximum duration between 20 ms and 30 ms in order to avoid the contribution of the stretch reflex.

The Grasp Perturbator is a device developed by the Institute of Robotics and Mechatronics (DLR) in Germany and validated in a study conducted by Höppner et al. in 2011 [50], with the aim of estimating human grasp stiffness. The device applies displacement perturbations, so it displaces the relative position of thumb and index. The amplitude of the movement is 10 mm, in a time window always less than 25 ms. The subjects were instructed to exert a constant force to the device, and keep a fixed position until the perturbation was sent. Studies on spasticity and on the estimation of muscle tone have been conducted over the years. An interesting commercial device, the NeuroFlexorTM_{, has been validated for the assessment}

of muscle tone, and its alterations, in wrist and fingers. In Lindberg et al. 2011 [51] the device has been tested on stroke patients. The NeuroFlexorTM_{is designed to apply ramp}

displacement of 50° at two velocities, 5 °/s and 240 °/s, with a duration respectively of 10 s and 200 ms. The patients wrist has been passively flexed, with the recommendation to not exert external forces. In Lee et al. 2004 [44] sinusoidal dis-placements of 60° of ROM have been performed, at 4 different frequencies to verify if there is a velocity-dependency in elbow spasticity. In Pisano et al. 2000 [52] a EMG-biomechanical technique has been applied to assess muscle tone in post-stroke patients under passive and dynamic conditions. Constant velocity displacement were applied to the wrist in flexion-extension, from 10 °/s to 500 °/s. Also in this experimental procedure patients were instructed to relax and to not react to the perturbations. As a result of the study, it has been shown that in spastic patients the threshold speed able to elicit the stretch reflex is lower and the EMG reflex activity is higher than in normal subjects.

The aim of investigating new kind of technological assessment of muscle tone and spasticity is to provide a more quantitative and objective measurement, that could be helpful to clinicians in order to define patient-tailored therapy programs.

C. ReHapticKnob

The Rehabilitation Engineering Laboratory (Department of Health Science and Technology, ETH Zürich) has devel-oped a device for neurocognitive robot-assisted therapy, the "ReHapticKnob", a robot designed for the assessment and therapy of hand function in post-stroke patients. The aim of robot-assisted rehabilitation is to complement traditional therapy. Robots can increase the intensity within a therapy session and provide more quantitative assessment thanks to integrated sensing systems. The ReHapticKnob (Fig. 1) is a 2 DOF device that allows to train both flexion-extension of the fingers (i.e., grasping) and pronation-supination of the forearm, in conditions similar to interactions with real objects. The actuation is provided using two brushed DC motors, that actuate independently the two DOF. About the sensing, linear and rotatory positions of the end-effector are measured by optical encoders mounted on the motors, and for a redundant measurement the robot is also equipped with one rotational and two linear potentiometers. Two 6 DOF force/torque sensors are mounted beneath the fingers support, and they allow a precise

Figure 2. A detail of the RHK, that shows the handles of the robot. The patient lies the arm on a support and places the fingers inside the handles, to manipulate the robot. From Metzger et al. 2014 [53]

measurement of the interaction force between user and device. The RHK consists of two computers, the host computer and the target computer. The host computer controls the therapy exercise state, commands the related control mode to the target and provide visual feedback from a Virtual Reality (VR) to the user, including a user interface and therapy exercises [54]. The target computer runs the target file, programmed in LabVIEW Real Time 10.0 (National Instrument), designated to control the actuators and read the signals from the sensors. The real-time target computer is connected via Ethernet LAN to the host computer. The computers communicate through the User Datagram Protocol (UDP) and the File Transfer Protocol (FTP). The architecture was modified in a recent thesis work [55], with the intention of simplify the structure, moving most of the control to the VR application. The current motor control of the RHK (low level controller) is a state machine that consists of 5 possible states: Wait, Calibrate, Run, Safety and Quit. When the robot is in the Run state we can change the motors mode, selecting between 3 possibilities: Stop, Move and Force. For each mode, a different control has been implemented: position control for Stop and Move mode, and impedance control with force feedback for the Force mode. The high level controller is placed in the host file, (i.e. the VR application) that sends instructions to the target computer via UDP, (e.g. changing the state of the machine or sending motor orders) and receives data from the sensors to execute various kind of functions. The patient sits in front of the robot, looking on a screen the task that he/she has to execute and interacts with the robot through the handles. The

arm lies on a soft support, and the patient places his/her hand inside the handles, as shown in Fig. 2. Seven rehabilitation exercises have been developed on the RHK based on the neurocognitive therapy approach described by Perfetti, that includes motor, sensory and cognitive aspects. The exercises train and assess proprioception, haptic perception, sensorymo-tor memory and coordination. The feasibility and acceptance of the neurocognitive robot-assisted therapy approach with RHK have been successfully validated in a recent study [56]. Then, to enhance the quality of the therapy, it was observed the importance to have motivating and challenging exercises, in order to stimulate active participation by the patient in the therapy. To maximize involvement and prevent frustration, it is important to design exercises that are adapted to the patient ability, in a way that they are neither too difficult nor too simple. Based on this principle, a difficulty adaptation model was applied to the RHK therapy exercises, whose difficulty gradually increases as the exercise progress [53]. The device is able to automatically adapt the difficulty level of the session based on the patient performance. In a previous thesis work [55], the robot was provided with a new user interface which aims to allow independent use of the robot at home or in the clinic. In this way, patients can practice rehabilitation exercises without the supervision of a therapist, for example during the evening or spare-time in the clinic. These mod-ifications were implemented with the purpose of increasing therapy intensity, which may lead to positive results in upper and lower limb rehabilitation [57]. Lately, the equivalence between dose-matched robot-assisted therapy with the RHK and conventional therapy has been tested in a clinical study conducted with the participation of Clinica Hildebrand Centro di Riabilitazione Brissago (Switzerland). During the study, clinicians have exhibited concerns about the possibility that robot-assisted therapy could lead to an increase in muscle tone, during the execution of active exercises, while some therapy approaches suggest that muscle tone should be controlled and monitored. Given these questions, we decided to implement an instrument to estimate muscle tone online during therapy. D. Goals of the thesis

The purpose of this thesis is to design a novel rehabil-itation exercise for the RHK. The exercise has to include neurocognitive aspects inspired by the Perfetti method, that were established with the help of a therapist from Clinica Hildebrand. It has to allow to train grasping and pronation-supination, both from a sensorimotor and cognitive point of view. Moreover, the exercise should be provided with an intuitive user interface, making it usable for training with and without supervision. With the help of a VR development software, the exercise should be pleasant and entertaining for the user, to involve active participation and increase therapy intensity. Indeed, patients had often reported that traditional therapy exercises can be boring and not engaging enough, because of their repetitive nature [58]. Lastly, we have to include an instrument to assess muscle tone online during the exercise, and monitor its evolution over the therapy sessions.

In order to do that, a perturbation based method should be included in the exercise.

II. METHODS

A. Exercise structure and description

The purpose of the therapy exercise that we propose is to actively train position propioception, haptic perception and sensorimotor coordination. The exercise was built following some concepts partly inspired by the stiffness identification exercise from Metzger et al 2014 [53], and partly by require-ments defined in collaboration with an occupational therapist (trained with the Perfetti method) from Clinica Hildebrand. The user explores the interface, press buttons and selects options via a keypad, with colored buttons. The button pad allows the user to interact with the VR in an easy and intuitive way. The exercise is divided in two parts:

- Training phase: during the Training, a variable number of spheres is displayed to the patient. The objects have different colors, which help in their identification and memorization, and variable compliance values, rendered by the robot controller. A virtual hand, whose dimension is comparable to a real one, is moved by the user. During the Training, the patient has to squeeze and learn the stiffness of the spheres. The user has the possibility to explore the objects as many times as he wants. Throughout the time between two trials, the patient is instructed to be passive. Position perturbations are sent during this interval. When all the spheres have been tried, the Training phase ends. The user has the possibility to restart the Training, or to start the Test phase.

- Test phase: during the Test phase a fixed number of transparent, glowing spheres (from this moment called halos) are arranged along an arc. One at a time, the halos fall radially from the initial position towards a position fixed at the center of the hand. The start position is chosen randomly. The patient has to actively rotate the arm and adjust the hand aperture, coordinating two movements, in order to catch the halo falling. When a halo is caught the patient has to squeeze the object and identify its stiffness. Then, he/she has to press the corresponding button on the colored keypad. If the patient chooses the correct answer, a green halo will be displayed; whereas if a wrong answer is given, a red halo and the correct option will be shown. Each Test phase lasts 3 min. At the end, the score collected during the session is presented, and a new session (i.e., Training phase followed by Test phase) is ready to start.

The exercise is composed by three sessions in total. At the end of the third session, it will be asked to the user to quit the game. Therefore, three set of perturbations will be sent, one per session. The characteristics of the perturbations were established based on the literature research. Displace-ment perturbations were preferred because they allow a better control of the robot, whereas force perturbations may move the fingers unpredictably, over the range of motion of the patient,

or at a velocity that might be annoying or painful for the patient. We established to use ramp perturbations of 20 mm amplitude, in order to not cause an excessive bother to the patient and to fit within the range of values found in literature. The perturbations have two different time durations: 150 ms and 250 ms. These time windows are long enough to include long-loop reflexes, that are important in the control of muscle tone, but short enough to exclude voluntary contributions. The corresponding velocities are ~130 mm/s and 80 mm/s. Furthermore, we decided to use perturbations in 2 directions, in flexion and in extension of the fingers. In the end, four perturbations were chosen for the muscle tone estimation, at slow and fast velocity and with the hand opening and closing. The profiles of the four chosen perturbations are shown in Fig. 3. The return phase from the perturbation is set as a slow ramp, to avoid useless disturbance to the patient. The duration of the return ramp is 900 ms. The patient needs to be instructed to be passive and to not exert forces on the handles throughout the movement.

Perturbations at different velocities could allow us to identify if the patient suffers from spasticity or rigidity, because spasticity has a velocity dependency, while rigidity does not. Moreover, finding a difference in the reaction force to perturbations in different directions might mean that we are dealing with spasticity, that also shows a direction dependency. For each perturbation at a certain time point we can compute the percentage change between the force before the perturbation and after the perturbation, calculated as:

∆F%=

Faf ter− F0

F0

(2) To evaluate the presence of rigidity or spasticity we can run at the same time point the following analysis:

- compare percentage force change at different velocities; ∆F%(vf ast) < ∆F%(vslow) (3)

- compare percentage force change in different directions (opening,closing).

∆F%(closing) 6= ∆F%(opening) (4)

We can also compare perturbation data over time to see if there is any change in muscle tone within therapy sessions and over the entire therapy program. We can examine:

- The percentage difference between percentage force at t0

and t1, t2 at the same velocity, repeated for slow and fast

speed;

∆F%(vf ast, t) =

∆F%(vf ast, t1) − ∆F%(vf ast, t0)

∆F%(vf ast, t0)

(5) - The percentage difference between percentage force at t0

and t1, t2at the same direction, repeated for opening and

closing direction;

∆F%(opening, t) =

∆F%(opening, t1) − ∆F%(opening, t0)

∆F_%(opening, t0)

(6)

- difference in percentage force change over multiple days of therapy.

Other requirements for the implementation of the game are:

- the exercise has to be placed in the third grade of Perfetti method, that includes complex movements involving more than 1 DOF;

- programming different velocities of falling of the halos;

- adding a sound when a halo starts falling towards the center;

- giving a glowing aspect to the halos can be useful to activate the pre-motor area of the cortex.

B. Implementation

The RHK functioning is controlled by an host file, that runs the VR and by a target file that runs the control in LabVIEW. To implement the exercise, the motor control and the VR were modified as follows.

1) Low Level Control - LabVIEW: To generate the per-turbations, we modified the current target file. A new mode was added to the selectable motor modes, that we called Perturbation mode. The new mode is based on a position control. Three variables are in input: the displacement (in mm), that is the amplitude of the perturbation, the time interval (in ms), that is the duration of the ramp, and the sign, that defines the direction. The magnitude of the displacement is added or subtracted to/from the current relative position, depending on the imposed sign, to determine the trajectory of the target position. The target position is given as input to a PID block, which calculates the necessary control output, in voltage, to move the handles to the desired position. The response of the robot to the perturbation and the performance of the chosen PID will be analyzed in section C. To analyze data, a saving loop is added to the target file, that is executed in parallel with the main program loop. The saving loop runs at a frequency of 1000 Hz, with a sampling time of 4 ms. At first, a TDMS file is opened. Then the variables necessary to the analysis are saved in the TDMS. In the end, the TDMS file is converted into a text file.

2) High Level Control and Virtual Reality - Unity: The VR and the high level control were designed in Unity 5.6.0f3 (Unity Technologies), a free cross-platform game engine used to develop video-games and other interactive environments. Unity allows the creation and manipulation of 3D objects via C # or JavaScript. We decided to program in C #.

As a first step, for our purpose we have to create deformable objects. In Unity the creation of 3D soft objects is relatively complex and require to write long scripts. A better result can be achieved using other software products for the im-plementation of complex 3D environments. Blender (Blender Foundation) is a free and open-source 3D computer graphic software that includes features, as 3D modeling and animation, that are useful for our aim. Models created in Blender can be easily transfered in Unity. Blender supports also the creation and manipulation of soft body physics, but this properties

0 200 400 600 800 1000 1200 0 5 10 15 20 t(ms) Position(mm)

Perturbation profile, hand opening

150 ms ramp 250 ms ramp (a) 0 200 400 600 800 1000 1200 0 5 10 15 20 t(ms) Position(mm)

Perturbation profile, hand closing

150 ms ramp 250 ms ramp

(b)

Figure 3. Example of perturbation profile. (a) Amplitude: 20 mm; Direction: hand opening. (b) Amplitude: 20 mm; Direction: hand closing

cannot be exported in Unity. However, Blender provides the possibility to deform objects, create an animation and export the animation to Unity. A model of the arm was already available from a previous project. We additionally created a simple sphere object in Blender. To create animations, we needed to add an appropriate armature to the object. Moving the bones of the armature, we can deform the object until we reach the desired result. The procedure for creating our animations is the following:

- put arm and sphere on scene, with the hand fully open and save the pose in a keyframe;

- move the bones of the hand until the fingers reach the surface of the sphere and save the pose in another keyframe;

- move both the armature until the sphere is realistically squeezed and save the pose in the last keyframe. The position between keyframes is automatically interpolated, so a smooth movement results. In the end, the animation is imported to Unity.

The GameControl.cs script is the main script to control the exercise. It contains functions that start and end both Training and Test phase, and start a new session, reinitializing all the variables correctly. When 3 sessions of game have been played, a message will be shown to invite the patient to quit the game. The exercise was implemented as follows:

- Initialization and calibration: at first the patient is asked to calibrate the rotatory axis, moving the robot handles to a horizontal position. From this moment, this angular position is set as the 0 angular relative position. Successively, a second message is shown, to asks the patient to close the hand as far as possible. From this moment, this will be the 0 linear relative position. The maximum possible aperture of the hand is set acquiring the translational ROM data loaded on the user interface, as (maximumtranslatoryROM − minimumtranslatoryROM )/2. Two virtual walls on the linear DOF (k=−30 N/mm) are added, one at the grasping minimum position set at 8 mm and one at the maximum possible aperture. Between the minimum and the maximum allowed positions the translational motor is

set to Force mode, and the robot is transparent. Instead, the rotatory motor is set to Stop mode. Finally a message is displayed, asking the patient to start the game, pressing a blue button on screen. Pushing on the correspondent blue button of the keypad, the Training phase of the exercise starts.

- Training phase: during the Training, only the animated arm and the spheres are shown on the scene. Because of screen resolution problems, the animation of the sphere designed in Blender could not be used. It was decided to perform the animation scaling the radius of the sphere on the x-axis depending on the current linear position. The animation is less realistic, but good enough for our pur-pose. We used scripts to control the arm and the spheres. In order to do that, data about position are sent through UDP to Unity. The hand is animated from the linear position of the robot. The user controls the movement of the hand and the robot, and the visual feedback on the screen corresponds with the current aperture of the patient hand. The number of spheres created depends on the difficulty level. A series of synchronized movements between arm and spheres are programmed. The arm moves forward the sphere that has to be squeezed, while the sphere group is stationary. When the arm stops, a force order is sent to the robot, activating the Force mode. The values of the spring-dampers are calculated following the difficulty adaptation law described by Metzger et al. in [53], and vary depending on the difficulty level (see Section C for more details). The squeezing of the sphere has to be accomplished at least two times by the patient. When the condition is verified, the arm moves back while the group of spheres moves automatically to the left. If the patient experiences some difficulties to squeeze the object, because of movement impairment, there is the possibility to move the sphere group using a pair of buttons "Next/Previous". The utility of these buttons is also to give the chance to the patient to try the spheres as many time as needed. While the arm is moving backwards a perturbation order is sent. The linear axis is set to Stop mode, so it is easier for the user to keep a relaxed and stable position. A function

generates a list of 4 perturbations. The sending order is randomized while each session starts, to avoid some kind of anticipatory behavior. The Training phase finishes when all the spheres on scene have been squeezed. A pair of buttons is finally displayed to show the possibility to restart the whole Training from the beginning or to start the Test phase of the exercise. To not unnecessarily bother the patient, the perturbations are not executed again in any of the subsequent Training.

- Test Phase: during the Test phase only the arm is shown on the scene and both the DOF are free to move. A Force mode order with a transparent spring damper is sent to the robot. An arc of halos is created with angular aperture ranging from maximum rotatory ROM to minimum rotatory ROM. The halos are not visible to the patient. The execution of a sound before the halo is visible on scene helps the patient to promptly react. The exercise trains the motor synchronization of linear and rotatory movement, since the patient needs to adjust both the grasping aperture and the forearm rotation for the purpose of collecting the halos. If the patient keeps a correct hand position, the halo stops in the center of the hand and a spring damper is randomly generated between the available values. The patient has to match the stiffness perceived with one of the spheres previously trained. The answer is given pressing one of the colored button on the keypad. If the answer is correct, the color of the halo changes to green. If the answer is wrong, the halo becomes red and the correct answer is displayed approximately 1 s beside the hand. The test phase lasts 3 min. When the time expires, the Test phase ends and a score summary is displayed to the user, to give information about the score and the difficulty level achieved.

C. Difficulty level adaptation

As previously mentioned, it was experimented on the RHK a difficulty adaptation method to maintain an engaging and challenging training level during therapy. Keeping the patient motivated to undergo the rehabilitation can increase the ther-apy dose. Each of the seven exercises currently present on the RHK has a system for calculating the performance and adapt the level of the exercise to the patient’s ability level. Scoring is usually an optional component in video-gaming. For the purpose of maintaining a high motivation, we added a score to the exercise. Keeping track of the score may encourage the patient to increase his performance and help the patient to easily evaluate his progress. A score panel is placed on the upper right corner of the screen. The score panel counts the number of spheres correctly answered and the score achieved. The player’s score is increased by 10 when the patient gives a correct answer, while it is decreased by 3 every time a halo is let get away or when a wrong answer is given. The performance of the player throughout 3 sessions is saved in a .csv file at the end of the game. At the end of the Test phase, a score summary is displayed to the patient. It shows the

total score achieved during the session, the number of spheres correctly answered and the total number of spheres presented in the session, and the difficulty level of the session that has just been completed and the difficulty level achieved. In our exercise, the performance of the patient during each session is evaluated as the number of correctly answered objects on the total number of objects. At the end of the session, the level is updated, as shown in Table II.

Table II DIFFICULTY ADJUSTMENT

Percentage of spheres correct Level adaptation

= 100% +2

> 70% +1

< 40% -1

otherwise No change

In Table I the division in difficulty levels is presented. The following parameters depends on the difficulty level:

- number of spheres;

- relative difference between spheres’ viscoelasticities (η(W F ));

- halo velocity;

- alignment angular range of movement.

As in Metzger et al. [53] the exercise consists of 10 levels, and we preserved the same distribution of the number of spheres through the levels presented in the paper. The performance of our exercise requires a previous assessment of linear and rotatory range of motion, and of the Weber stiffness fraction. The Weber fraction is the relative difference in stiffness between two objects that can be perceived by the patient. This value is unique for every patient and it is useful to make a first evaluation of his starting ability. The value of viscoelasticitiy assigned to the spheres is a function of the Weber fraction (η(W F )). See Appendix Section A for more details. The value of the relative difference in the spheres η(W F ) is decreased

Figure 4. Alignment angular tolerance of movement in Test phase. This value changes depending on the difficulty level

Table I DIFFICULTY LEVELS

Level Number of spheres Relative stiffness difference Halo velocity Alignment range

1 3 2 ∗ η(W F ) 1.5 m/s 80° 2 3 1.9 ∗ η(W F ) 1.5 m/s 74° 3 4 1.8 ∗ η(W F ) 2 m/s 68° 4 4 1.7 ∗ η(W F ) 2 m/s 62° 5 5 1.6 ∗ η(W F ) 2.5 m/s 56° 6 5 1.5 ∗ η(W F ) 2.5 m/s 50° 7 5 1.4 ∗ η(W F ) 3 m/s 44° 8 5 1.3 ∗ η(W F ) 3 m/s 38° 9 5 1.2 ∗ η(W F ) 3.5 m/s 30° 10 5 1.1 ∗ η(W F ) 4 m/s 30°

with an increase in difficulty level, but it is always kept in the range: 0.15 N/mm6 η(W F ) 6 0.9 N/mm. The smaller the patient η(W F ) and the easier is the difficulty level, the less is the difference in stiffness between the spheres, and the more the identification is difficult. Another parameter that varies is the speed at which the halos fall in the Test phase. The speed ranges from a minimum of 1.5 m/s to a maximum of 4 m/s. The velocity is kept low in the beginning of the game, and increases of 0.5 m/s every two levels, except for the last two. The higher is the velocity, the more the patient needs to be responsive to coordinate his movement in order to catch the halo. Before illustrating how the last parameter is adapted throughout levels, we explain how it was established the "catching" condition. At first, the angle θ between the direction of the falling halo and the y-axis is calculated. Then this value is compared to the rotatory position of the robot, φ. If φ, controlled by the patient, belong to the alignment angular range: ϑ − ϑcatch 6 φ 6 ϑ + ϑcatch (see Fig.4)

the condition is verified, and the halo is caught and stops in the hand. Thus, the patient can start the identification of the stiffness. If the condition is not verified, the halo does not stop and is lost. The value called ϑcatch is the alignment angular

range of movement left to the patient that verify the catching condition. The alignment angular range of movement varies from 80° at the minimum level to 30° at the maximum level, with a step of 6° except for the last two levels that are defined differently. The 9th level differs from the previous of 8°, while the 10th level has the same angle range of the 9th, i.e. 30°.

III. PRELIMINARYEXPERIMENTS

A. Control Performance of ReHapticKnob

For the Perturbation mode that we introduced, we needed to evaluate the ability of the position control to follow ramp perturbations, in the time range that we decided to adopt. Considering the high speed of the perturbation ramps, we at first evaluated the maximum achievable performance of our PID position control. We evaluated the response of the robot to a step displacement of 20 mm of amplitude. We conducted the experiment in two conditions: with the robot free to move and with a passive hand placed in the handles of the RHK.

We manually tuned the PID controller and the gain values that allowed us to have the better response were:

- Kp= 10 V mm−1 ;

- Ki= 0 V/(mm ∗ s) ;

- Kd= 0.000 38 (V ∗ s)/mm.

A PD controller was the best solution for implementing a position control. The response is shown in Fig 5. The rise time, the time to reach the 90% of the reference value is 64 ms. The settling time, i.e. the time required for the output to reach and remain within a given error band, set in this specific case to the 3%, is 84 ms. Therefore, the robot is relatively fast to reach a stable output, within a time that is short enough for our ramp perturbations. The overshoot, the maximum error value of the response curve with respect to the reference, is 0.231 mm, while the steady state error is 0.0937± 0.0938 mm (in percentage, 0.007±0.13 %) (mean ± SD). It is also interesting to see how the robot responds with a passive hand placed in the handles. This mimics the condition in which the muscle tone measurements will be carried out. With the passive hand, the rise time is 60 ms. Since the sampling time was set to 4 ms, there is not a significative difference between the rise time with and without hand. The settling time as well is little affected by the presence of the hand, since it is 89 ms. Therefore, there is not a significative difference in terms of time response to a step impulse with and without passive hand, while the overshoot decreases to 0 mm. The steady state error is 0.0796± 0.0419 mm (in percentage, 0.07±0.09 %) (mean ± SD). It seems as the hand could be of help to stabilize the response. We were also interested in how the RHK responds to the ramp displacements used in the Perturbation mode (i.e., at velocity of ~130 mm s−1 and 80 mm s−1). The response is shown in Fig. 6. We conducted the experiment with a passive hand placed inside the handles. With the fast ramp we obtained good results in terms of performance. Along the ramp the signal remains inside the error band of 3% (i.e., the settling time is 0 ms). The overshoot is 0.38 mm, the steady state error is 0.12±0.12 mm (in percentage, 0.02±0.17 %) (mean ± SD) and the error along the ramp is 0.89±0.20 mm (in percentage, 1.52±0.43 %) (mean ± SD). The errors are considerate accept-able. With the slow ramp, as we expected, the response is more

0 0.5 1 45 50 55 60 65 70 75 t(s) x(mm) 0 0.5 1 45 50 55 60 65 70 75 t(s) x(mm) x reference x x reference x

Figure 5. Robot response to a step disturbance of 20 mm. To the left, the response without hand. To the right, the response with passive hand. The chosen PID gain values are Kp= 10 V mm−1, Ki= 0 V/(mm ∗ s), Kd=

0.000 38 (V ∗ s)/mm 0 0.2 0.4 45 50 55 60 65 70 75 t(s) x(mm) 0 0.5 1 45 50 55 60 65 70 75 t(s) x(mm) x reference x x reference x

Figure 6. Robot response to ramp displacements of 20 mm, with a passive hand placed in the robot handles. The chosen PID gain values are Kp =

10 V mm−1_{, K}

i= 0 V/(mm ∗ s), Kd= 0.0001 (V ∗ s)/mm

stable and the error is lower. The settling time along the ramp in a band of 3% is 0 ms. The overshoot is 0.099 mm, the steady state error is 0.35±0.14 mm (in percentage, 0.04±0.05 %) (mean ± SD) and the error along the ramp is 0.52±0.07 mm (in percentage, 0.87±0.14 %) (mean ± SD). Results from the experiment of ramp response, with the robot free to move, are in Appendix Section C.

These results from the evaluation of the performance show that the RHK is able to follow the ramps that we implemented in our Perturbation mode, with good results in terms of performance.

B. Stiffness Identification

As a first step, we evaluated the performance of the tone monitoring during the Training phase using a mock-up setup. We wanted to see if the robot is able to estimate force changes based on our perturbations. To conduct the experiment, we chose two springs with different stiffness values. The springs were chosen to have a reaction force to a displacement of 20 mm in the range of forces typically used by patients (i.e., 30 N), taken from data collected during the execution of the RHK during the clinical study NCT02096445. The stiffness values declared from the company (Misumi Group Inc.) were 1.57 N/mm for the stiffer spring and 0.97 N/mm for the softer spring. To be sure that those values were correct, we measured the stiffness of the springs with a device from the Laboratory

for Orthopaedic Biomechanics, ETH Zürich (courtesy of Prof. Jess Gerrit Snedeker) and we found that the real values were: for the stiffer spring 1.557 N/mm, whereas for the softer spring 1.129 N/mm. We used the measured values as reference values to compare our data. Customized handles were 3D printed to fix the springs during the experiment. The procedure to conduct the experiment was the follow:

- lock the spring into the handles (as shown in Fig. 7); - pretension the spring of ~5 mm;

- apply the perturbation, both in opening and closing direction, with a trapezoidal profile;

- repeat the procedure from point 2.

We did 12 repetitions of the experiment. Instead of the profile chosen for the execution of the exercise with a patient, we decided to use a trapezoidal perturbation, for practical reasons. This is equivalent considering the linear characteristic of the spring. The plot in Fig. 8 represents the average force and position achieved by the robot, during the experiment with the soft spring, during the 12 repetitions. The plot shows a perfectly linear force trend with respect to the spring defor-mation. To estimate the stiffness we calculated the average value of the force on the constant part of the curve after the ramp and we subtracted the preload value (i.e., the force value before the application of the perturbation). Then, we divided the force difference by the difference in position calculated on the constant part of the curve (in average) and the initial position of the spring. We calculated the error between the measured stiffness value and the reference value as RMSD (Root Mean Square Deviation). Table III shows the results of our analysis, for both springs, but only for the opening direction (first slope of the trapezoidal perturbation curve). The results from the other cases are placed in appendix, section B. The RHK is able to estimate the stiffness of the springs with an acceptable error of 7.695% for the stiff spring and of 3.676% for the soft spring, in the case of experiment with the fast ramp (~130 mm s−1), and with an acceptable error of 7.641% for

0

0.5

1

1.5

0

20

40

t(s)

F (N)

0

0.5

1

1.5

30

40

50

60

t(s)

Position (mm)

150 ms ramp

250 ms ramp

150 ms ramp

250 ms ramp

Figure 8. On the top, the average force on 12 repetitions achieved by the robot during the experiment with the soft spring. On the bottom, the average position.

Table III

SPRING EXPERIMENT,OPENING DIRECTION

Fast ramp

Spring K reference [N mm−1] K mean ± SD [N mm−1] RMSD (SD) %

60 mm 1.56 1.437 ± 0.003 7.695 (0.2)

55 mm 1.13 1.087 ± 0.004 3.676 (0.5)

Slow ramp

60 mm 1.56 1.438 ± 0.008 7.641 (0.4)

55 mm 1.13 1.09 ± 0.004 3.434 (0.3)

the stiff spring and of 3.434% for the soft spring, in the case of experiment with the slow ramp (80 mm s−1). The standard deviation values (≤ 0.5 %) show that the estimation is highly repeatable. Two considerations can be done about the error. Firstly, the difference between the soft and the stiff spring can be explained with an issue found during the execution of the experiment with the stiff spring. This spring was exerting a force on the handles of the robot able to bend the thin metallic support on which the handles are mounted. Thus, the position achieved by the robot during the stretching of the spring was lower compared to the one measured by the position sensors. Secondly, the residual offset could be due to the error in the calibration of force and position sensors. With the soft spring this behavior was not noticed. There is not a significant difference between the RMSD calculated for the slow ramp, and between the measurement done in closing direction, so the results from the other cases are placed in appendix, Section B.

IV. DISCUSSION

In this project a new therapeutic exercise for the ReHap-ticKnob was developed. We were able to design a therapeutic exercise for post-stroke patient rehabilitation that include a perturbation based method for muscle tone assessment. The exercise meets the requirements established in the beginning with the help of the therapist. The virtual reality designed in Unity is more appealing compared to the one of the exercises currently available on the robot. We also tried to make the exercise entertaining, in order to increase involvement and motivation of the patient during therapy. Increasing the ac-tive participation could also increase the therapy dose. The integration of an intuitive and simple user interface gives the possibility of an independent use of the robot, without the supervision of a therapist, but supervision is always required in the first therapy sessions. The exercise was designed to realize an active sensory-motor training of two DoF, pronation-supination and grasping, inspired by the Neurocognitive Ap-proach of Perfetti. Neurocognitive aspects include haptic and position proprioception, and coordination of complex

move-ments. To enhance the quality of the virtual reality, the animation currently present could be replaced by the more realistic sphere animation implemented in Blender, but this require to improve the performance of the robot, in order to increase the resolution of the VR and to have an animation without lag. The size of the objects and the constraint applied in the Force motor mode limited the deformation of the spheres themselves, which could make stiffness identification harder in the more difficult levels. The execution of the exercise requires the patient to be totally relaxed and passive when the perturbation is applied to the limb. However, to be sure that the patient is not applying forces on the robot, use of EMG recording on hand and forearm muscles could be suggested. The performance of the position control was evaluated with the application of a step and subsequently with ramp dis-placements of durations chosen for the purpose of our tone estimation. The step response parameters that we calculated showed that the robot is able to reach the target position with a good performance. Rise time and settling time results showed that the robot has a fast step response, with limited overshoot and steady state error. The response is fast enough considering the time duration of our perturbations. The ramp response was evaluated using a different combination of gains. The use of the derivative gain Kd = 0.000 38 V/(mm ∗ s), employed

for the evaluation with the application of a step displacement and set for the position control of the Stop mode, made the response too slow, with a high ramp error. To reach a better result in terms of time response and ramp error we decided to reduce the derivative gain. This increased the overshoot in the case of the application of the fast ramp. However, the overshoot can be considered acceptable. The ramp error for the fast ramp (0.89±0.2 mm (mean ± SD) is also acceptable. The SD could seem significant, but excluding the first samples (~40 ms) the difference between current position and reference become more constant. With the slow ramp the results in terms of performance are better, the response is more stable, the error is decreased.

We obtained promising results from the spring stiffness eval-uation experiment. Experimental setup using mass-spring-damper systems, that simulate human limb characteristics, have been used in literature to preliminary validate joint impedance or muscle tone estimation methods [44, 59]. Mass, spring and damper elements in mechanical impedance rep-resent inertia, stiffness and damping of the limb respec-tively [60]. The error in the identification of the stiffness obtained during the experiment is amply acceptable, also if compared with other devices in literature that were primarily designed for stiffness identification [61]. Tucker et al. in 2017 validated the "Knee Perturbator" (Rehabilitation Engineering Lab, ETH Zürich, Switzerland), a device for the identification of knee impedance that was capable of estimating mass-spring system characteristics with an accuracy of 15%. The amount of the error for perturbations at different velocities and at different directions was very similar, and always smaller than 8 %, which suggests that the RHK would be able to discriminate between spasticity and rigidity. We can consider that the RHK

is able to obtain highly accurate and repeatable estimations of stiffness. These results are encouraging for future applications with patients. The RHK could be able to estimate changes in forces due to possible increases or decreases in spasticity or rigidity of the patient’s hand, resulting from the application of displacement perturbations.

V. CONCLUSIONS

In this thesis a new therapy exercise including an online assessment method for post-stroke patients rehabilitation has been developed. A continuous monitoring of muscle tone is important to control a potential increase in spasticity or rigidity in stroke patients, during the execution of active exercises. The preliminary experiments have shown promising results, but tests with subjects will be necessary to evaluate if the method could provide an accurate muscle tone estimation. With tests on both healthy subjects (or even stroke patients not exhibiting muscle tone alterations) and spastic/rigid patients we can identify which changes in forces measured over time can be considered normal. Abnormal changes could be a sign of increasing hypertonia. As a next step, test with patients will be carried out. If tests will exhibit good results, comparing them with current literature research, therapist could rely on a quantitative evaluation, that overcomes some limitations of clinical assessment. Clinical scales suffers from low sensitivity and are hard to administer anytime is needed during the rehabilitation. With our instrument, we can provide a monitoring of muscle tone during each therapy session and over the entire therapy cycle. Moreover, the difficulty level of the game could also be adapted based on the evolution of the patient’s tone pattern over time. Test will also be useful to evaluate the usability of the therapy game and the user perception of the perturbations. Depending on the evolution of patient’s muscle tone, therapists will be able to choose the best strategy to control hypertonia.

In the future, a new algorithm to save data from LabVIEW (e.g., position, force, motor state) and from Unity (e.g., number of session, type of exercise performed) should be added, to have a complete exercise ready to be tested with patients. Other features could be added to improve the current project. The virtual reality could include a pointer in the Test phase, to help the patient in the orientation of the hand, if he/she has particularly low coordination or position proprioception, measured in the initial assessment. Different parameters can be included in the difficulty adaptation. First, measurement of the response time of the patient for the evaluation of the performance. Second, adaptation of score to the difficulty level, in order to have a greater satisfaction in the higher levels. Third, reduction of difficulty in the case of augmented tone. Performance evaluation could also be customized to the patient’s ability. For instance, performance could be evaluated with two separate scores or scaling factors, if the patient is more impaired in coordinating complex movements, or is more impaired in haptic proprioception. Other neurocognitive aspects can be included, after consultation with a therapist. Viscosity changes in the environment during the Test phase

will increase the difficulty of the exercise and will stimulate the patient to learn how to rapidly react to environmental changes.

Finally, muscle tone estimation can be extended also to the pronation-supination DoF. The Perturbation mode needs to be added in LabVIEW. The position control for the rotational motor has not already been optimized. So, an evaluation of the performance of the position control will need to be executed, in order to choose the best combination of PID gains. The perturbation characteristics will need to be based on further literature research, but the ramp durations selected before should be appropriate also for the perturbation in pronation-supination.

The assessment method could be added to other exercises implemented in the RHK, as long as they have some phases in which the patient is passive, to correctly estimate muscle tone. To exclude the possibility that the patient applies forces during the tone measurement, an EMG recording of the muscular activity of hand and forearm would be useful to exclude measurements in which the patient was not relaxed. EMG data could be used to refine muscle tone measurements, as for instance the detection of the threshold velocity to elicit stretch reflexes [52], in order to see if our choice of the ramps’ velocity was appropriate.

The choice of making the exercise suitable for an independent use was also made to include this exercise in the new robot in development at the Rehabilitation Engineering Lab, the HandyBot. The HandyBot is a portable device for hand rehabilitation that could be independently used by stroke patients both during therapy in the clinic or at home. The inclusion of our perturbation-based tone monitoring method will make possible to assess muscle tone also outside the clinic, without the need for the presence of a therapist. This might also contribute to reduce the therapy-related costs by decreasing the length of the patient’s in-hospital stay or reducing the amount of supervised-therapy needed.

VI. ACKNOWLEGMENT

I would like to thank my supervisor, Raffaele, for his invaluable support, his precious advice and his special friendship over the entire period of my thesis, and Dr. Olivier Lambercy for the interest demonstrated in my project. I would like to thank the ETH Zürich RELab, especially all the students, for making my time abroad so enjoyable. Special thanks to Lucas Eicher for helping me when I was struggling with Unity, and to Luke for being my friend, through good times and bad. I would like to thank Prof. Roger Gassert, for allowing me the opportunity of undertaking my thesis at RELab. I would also like to thank Maya Kamber for doing all the administrative things related to the project and for being patient in helping me with my issues. Finally, I would also like to express my gratitude to Prof. Giovanni Vozzi for giving me the chance to carry out this experience abroad.

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