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Thermography for skin temperature evaluation
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during dynamic exercise, a case study on an
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incremental maximal test in elite male cyclists
4 NICOLA LUDWIG,1,* ATHOS TRECROCI,2 MARCO GARGANO,1 DAMIANO FORMENTI,2 ANDREA BOSIO,3
5 ERMANNO RAMPININI,3 AND GIAMPIETRO ALBERTI2
6 1Department of Physics, Università degli Studi di Milano, via Celoria 16 20133 Milano, Italy
7 2Department of Biomedical Sciences for Health, Università degli Studi di Milano, via Kramer 4 20100 Milano, Italy 8 3Human Performance Laboratory, MAPEI Sport Research Centre, Olgiate Olona (VA), Italy
9 *Corresponding author: nicola.ludwig@unimi.it
10 Received 15 July 2016; revised 21 September 2016; accepted 21 September 2016; posted 23 September 2016 (Doc. ID 270699);
11 published 0 MONTH 0000
12 The use of thermal imaging in monitoring the dynamic of skin temperature during prolonged physical exercise is 13 central to assessing the athletes’ ability to dissipate heat from the skin surface to the environment. In this
14 study, seven elite cyclists completed an incremental maximal cycling test to evaluate their skin temperature re-
15 sponse under controlled-environment conditions. Thermal images have been analyzed using a method based on
16 maxima detection (Tmax). Data confirmed a reduction in skin temperature due to vasoconstriction during the
17 exercise, followed by a temperature increment after exhaustion. A characteristic hot-spotted thermal pattern was 18 found over the skin surface in all subjects. This research confirmed also the notable ability by highly trained 19 cyclists to modify skin temperature during an incremental muscular effort. This study gives additional contri- 20 butions in the understanding the capability of theTmax method applied to thermoregulation and physiological 21 processes. © 2016 Optical Society of America
OCIS codes: (100.0100) Image processing; (170.0170) Medical optics and biotechnology; (040.6808) Thermal (uncooled) IR
22 detectors, arrays and imaging.
23 http://dx.doi.org/10.1364/AO.99.099999
24 1. INTRODUCTION
25 Since the development of the first image detectors for infrared
26 thermal radiation, thermography has been considered a unique
27 method of nondestructive diagnostics on the human body [1].
28 Infrared thermography (IRT) has also been widely used in 29 biomedical studies to evaluate both skin temperature without 30 contact and human thermoregulation mechanisms [2].
31 Largely, the direction of heat transfer is from the underlying 32 tissues toward the skin. Any variations in blood flow determine 33 patterns of thermal contrast detectable on the bare human skin.
34 The dimensions of these patterns give rise to a corresponding
35 variation in the rate at which energy is dissipated through
36 long-wave IR radiation (8–12 μm).
37 IRT is nowadays an increasingly popular method for skin 38 temperature evaluation in athletic activities with a noncontact 39 approach. During continued physical activity, muscular exer-
40 cise causes an excessive metabolic production of heat that
41 the body has to remove to avoid an excessive increase of the
42 internal temperature. Through an activation of vasoconstric-
43 tion and vasodilatation mechanisms, blood flow is drained from
44
core districts to the skin, which plays an important role in
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thermoregulation [3]. Here, the heat is dissipated by means
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of several mechanisms: irradiation, sweating (and evaporation),
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convection, and respiration [4,5]. Some of them are in conflict
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because cooling down of skin by evaporation causes a decrease of
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irradiation toward the environment [6]. On the other side, a
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colder area on the skin contributes to cooling down blood in
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superficial capillary vessels by convection even if the rate of heat
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dissipation strongly depends on the thermal conductivity of skin
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itself. Fat skin is supposed to reduce the effectiveness of this
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mechanism [7]. Models of thermoregulation were definitely
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proposed by Stolwijk [5] and Tanabe [6], even if on manikins
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or using contact probes for the temperature measurements.
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Hence, in the sport science the study of the ability to
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dissipate heat, related to the skin temperature, is crucial to sustain muscular efforts until the end of the exercise [4]. 59
60
The aim of this study was to obtain a single temperature
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value representative for each subject while also evaluating tem-
62
perature distributions on thighs during a maximal incremental
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exercise in elite cyclists under controlled-environment
1559-128X/16/340001-01 Journal © 2016 Optical Society of America
64 conditions. Considering previous works [8,9], two experimen-
65 tal considerations were made: first, in order to be as close as
66 possible to the real agonistic performance, we studied the effects
67 of an increasing and controlled work for a selected group of
68 highly professional athletes in their actual training condition.
69 Second, a specific experimental setting was designed for the
70 thermal image acquisition.
71 Our goal was not to obtain a subject’s global temperature,
72 taking into account the reference points suggested by literature
73 [5,6], because we consider these measurements as perturbative
74 of the athlete’s performance and affected by inevitable experi-
75 mental errors. Our aim was actually to compare different meth-
76 ods of thermal imaging analysis in addition of what was studied 77 in previous works on agonistic versus untrained females in a calf
78 rise exercise and on trained males in a squat exercise [10,11]. To
79 perform this comparison, we evaluated results obtained from 80 analysis of thermal images recorded on a well-defined anatomic 81 region using different methods (Troi and Tmax) described in 82 Section 2. We hypothesized that the Tmax method based on 83 threshold criterion should be equivalent to theTroiin describ-
84 ing dynamics of skin temperature during physical exercise but
85 more accurate and less operator dependent [10].
86 2. MATERIALS AND METHODS
87 Seven male elite cyclists participated voluntarily in the study. All 88 subjects were selected for similar anthropometric characteristics 89 as shown in Table1. They were also instructed to avoid high- 90 intensity or strenuous physical activity 24 h prior to testing.
91 Subjects were deeply informed of the procedures before their par- 92 ticipation, and a written informed consent was signed by them.
93 All measurements were performed in the laboratory of
94 MAPEI Sport Research Center with a controlled environmen-
95 tal temperature and humidity (22–23°C and 50 5% RH),
96 natural and fluorescent lighting, and no direct ventilation.
97 Each subject was asked to perform the following protocol ex- 98 ercise (Fig.1): before the incremental cycling test, subjects rested 99 for 10 min in order to acclimatize the body skin with the temper- 100 ature of the room. During thermal image acquisitions, subjects 101 were asked to stay upright with leg extended toward the floor in a 102 sitting position on the cycle ergometer (Fig.2). Spectral emissiv-
103 ity was set to 0.98 as generally used for human skin [2].
104 After 10 min of warm up performed with a constant load of
105 100 W, subjects completed an incremental maximal cycling
106 test assessing maximal oxygen uptake and maximal power
107 output. Each participant started at a workload of 100 W with 108 an increase of 25 W every minute until exhaustion. Pedaling
109 cadence was kept constant throughout the test in a range be-
110 tween 80 and 90 rpm. Time to exhaustion point corresponded
111 with the cyclist’s incapacity to maintain a cadence of 80 rpm for
112 almost 5 s. Maximal power output coincided with the workload
113 at the same time.
114
The thermal camera (AVIO TVS700 micro bolometer
115
uncooled detector) was placed on a tripod in front of the sub-
116
jects perpendicularly to the tights (Fig. 2) in order to reduce
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perspective distortions. Two images were recorded interspersed 118 by 10 s at each specific time point:basal (at rest or pre-warm-
119 up), warm up end (pre-exercise), and after the incremental Table 1. Subjects’ Anthropometric and Physiological Characteristics
T1:1 Subj. Age (y) Height (m) Body Mass (kg) Body Fat (%) VO2max (ml kg−1min−1)
T1:2 1 21 1.78 74.0 7.70 69.90
T1:3 2 23 1.81 71.0 4.60 66.70
T1:4 3 19 1.81 79.0 7.80 65.30
T1:5 4 20 1.73 66.0 9.00 75.50
T1:6 5 22 1.80 74.0 11.20 64.50
T1:7 6 18 1.71 62.5 9.20 64.70
T1:8 7 19 1.79 64.5 11.00 61.60
T1:9 Mean value SD 20.3 1.8 70.14 6.00 1.77 0.03 8.64 2.25 66.89 4.55
F1:1 Fig. 1. Protocol of the training followed by the athlete in laboratory.
F1:2 After the warm-up, each step was of 25 W per minute until exhaus-
F1:3 tion. Arrows in bold represents the time of thermal shoots.
F2:1 Fig. 2. Experimental setting during the thermal image acquisition.
F2:2 The subject was asked to stay upright with legs extended toward the
F2:3 floor in a sitting position on the cycle ergometer.
1
120 training,exhaustion, and 3 min and 6 min after the exhaustion.
121 The bold arrows in Fig. 1represent thermal image shots.
122 The thermal images were analyzed using theTmax method, 123 which was recently proposed for high load exercise [11]. The
124 method consists in giving to each region of interest (ROI) a tem-
125 perature value as the average of the five highest temperatures de-
126 tected within the ROI. The average is done over an area of
127 5 × 5 pixels around the hottest one as shown in Fig.3. In this 128 way the results are representative of the same number of pixels on 129 each thigh, resulting into a more representative sampling of the 130 warmest areas of the thighs (see [10,11] for more details).
131 For all the subjects, after calculating theTmaxvalues of the 132 left and the right thigh, we averaged them to obtain one rep- 133 resentative temperature value per each subject. All values are
134 representative of the most important cutaneous areas for heat
135 dissipation.
136 Data normal distribution was verified by a Shapiro–Wilk
137 test, and all data (mean SD) met the assumption of
138 normality. A one-way analysis of variance for repeated measures
139 was used to compare skin temperature dynamics among the
140
time points. Partial eta squared (Partη2) was used to estimate
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the magnitude of the difference within each group, and the
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thresholds for small, moderate, and large effects were defined
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as 0.01, 0.06, and 0.14, respectively.
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Overall analysis was performed using IBM SPSS Statistics
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(v. 21, New York, United States), and an alpha threshold of p < 0.05 was set to identify statistical significance. 146
147
3. RESULTS AND DISCUSSION
The sequence of thermal images of one subject is shown in 148 149
Fig. 4, starting from rest condition before warm up, and up
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to 6 min after the exhaustion. It illustrates the changes of
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temperature distribution on thighs during and after the
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protocol training.
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The graphic in Fig. 5 shows the temperatures differences
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calculated with respect to the ones recorded during 10 min of the basal condition (rest) of the seven subjects; they are 155
156
plotted for the five representative time points before and after
157
the training.
158
We want to stress that theTmaxmethod in this specific case
159
allows us to obtain values of temperature representative of the
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blood in the cutaneous vessels. It is directly influenced by
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the heat produced in the muscles of the thigh. The temperature
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evolution of the muscular districts involved in this work is then
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monitored by these hot spots.
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After the end of the exercise we observed a characteristic hot-
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spotted pattern in all subjects (Fig.3, upper panel). This pat-
166
tern has been already reported in thermographic analyses on
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dogs and horses over a treadmill [12] and in squat exercise
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for humans [11]. The similarity could be explained by the
169
type of effort (e.g., incremental maximal effort) but not by
170
the type of exercise (e.g., running or cycling). In fact, similar
171
texture in thermal images has been found for different exercises
172
such as running tasks [9,13,14]. This characteristic thermal
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pattern has a strong influence on the skin temperature measure-
174
ments of a specific body area. In these cases, usually a non-
175
Gaussian thermal distribution inside a selected ROI is present,
176
and it is clear that the method based on averaging pixel temper-
177
atures over the whole ROI (Troi) cannot be applied for a
178
reliable analysis of these subjects [10,11].
179
On the other hand, theTmaxmethod does not evaluate the 180 average of the all thermal profiles but only considers the tem-
181 perature of the maxima. This implies that the non-Gaussian
182 distribution of pixel values is not so critical. Moreover, this
183 method is applied on the same number of pixels per image.
184
This ensures the same statistical weight per each ROI con-
185
sidered on the skin surface. The average time course of skin
186
temperature is shown in Fig.6. During incremental exercise,
187
skin temperature decreased substantially from 32.50 0.67°C to 30.87 0.73°C (P 0.002; Part η2 0.937) 188
189 between baseline and exhaustion time points.
190 In the recovery time, after 3 min, skin temperature increased significantly (P < 0.01) from the exhaustion time 191
192 point, returning similar to basal values and remaining constant
193 after 6 min from the end of the exercise.
F3:1 Fig. 3. Thermal image after exhaustion and magnification of the F3:2 rectangular area (ROI). Inside the ROI magnification, the gray squares F3:3 of 5 × 5 pixels are centered on one of the five hottest pixels found F3:4 automatically by the thermal analysis software.
194 This clear up and down behavior of skin temperature was
195 also observed by other authors [9,14]. However, to date, this is
196 the first study that has investigated the skin temperature
F4:1 Fig. 4. Thermal images of a representative subject shot at different F4:2 conditions before and after the incremental training. (spectral emissiv- F4:3 ity for skin at 8–12 μm set at 0.98).
F5:1 Fig. 5. Temperature variation with respect to average basal temper-
F5:2 ature in seven subject at the five most representative times before and
F5:3 after the training. Errors range in the order of0.2°C.
F6:1 Fig. 6. Skin temperature (average) dynamics among different time
F6:2 points.P < 0.05, exhaustion versus basal; P < 0.01 exhaustion versus
F6:3 after 3 min and after 6 min.
30.5 31.0 31.5 32.0 32.5 33.0 33.5
-0.4 0.0 0.4 0.8 1.2 1.6 2.0
Mean of Tmax and Troi (°C)
Difference between Tmax and Troi (°C)
Mean 0.82
-1.96 SD 0.29 +1.96 SD 1.35
F7:1 Fig. 7. Bland and Altman analysis for temperature values measured
F7:2 with theTmaxmethod andTroimethod on small areas inside the ther-
F7:3 mal images of thighs. This distribution demonstrated, as expected, a
F7:4 quite constant bias [0.8°C] and relatively low standard deviations.
F7:5 Only a little increase in skin temperature difference for higher values
F7:6 of mean temperature can be observed.
197 dynamics using theTmaxmethod during an incremental exer- 198 cise. With regard to the literature, our findings showed a greater 199 efficiency of vasoconstriction and vasodilation processes in re- 200 moving heat from the core to the skin layers of highly trained 201 subjects.
202 In order to compare data obtained with theTmax method 203 with a reference method in widespread use in the analysis of
204 thermal images, we used a Bland and Altman analysis on a
205 set of data obtained by basal and exhaustion thermography.
206 Temperature values were measured with the Tmax method
207 and Troi method on small areas (about 1000 pixels) of both 208 left and right thighs. The distribution (see Fig. 7) demon- 209 strated, as expected, a quite constant bias [0.8°C], and relatively 210 low standard deviations. However, for higher values of mean 211 skin temperature we observed a little increase in the difference 212 of skin temperature measured with the two methods.
213 4. CONCLUSION
214 In this work, thermal images have been analyzed using a
215 method based on maxima detection (Tmax). The results con-
216 tribute to better characterize this method in skin temperature
217 detection as particularly useful when a non-Gaussian distribu- 218 tion of temperature is found such as in high load physical ex- 219 ercise. The method based on automatic detection of highest 220 values of temperature is useful when nonhomogenous distribu- 221 tion of temperature is found.
222 In our trial, the skin temperature dynamic of muscle quadri- 223 ceps showed an explicit decrease during an incremental maxi-
224 mal exercise and a subsequent rapid recovery immediately after
225 exhaustion. This behavior reflects a remarkable ability to
226 dissipate metabolic heat through the cutaneous surface by
227 highly trained cyclists. This can be explained by the effect of 228 vasoconstriction/vasodilatation mechanisms.
229 This research confirmed the ability by highly trained cyclists 230 to modify skin temperature during an incremental muscular 231 effort.
232 Furthermore, an interesting finding of this work was the 233 characteristic hot-spotted thermal pattern over the skin surface
234 in all subjects.
235 Finally, this study suggests additional contributions in
236 the understanding the applicability of the Tmax method in
237 the study of thermoregulation and physiological processes.
238 In particular, it could be promising in the study of physical 239 activities or pathologies characterized by discontinuous thermal
240 patterns over the skin surface.
241 Acknowledgment. According to the declaration of
242 Helsinki, the study was approved by the Ethical Committee
243
of the Università degli Studi di Milano. Subjects were deeply
244
informed of the procedures before their participation, and a
245
written informed consent was signed by them. Human
246
Performance Laboratory, MAPEI Sport Research Centre, Olgiate Olona (VA), Italy, Dott. Davide Bugini of CNST— 247
248
IIT at Polimi di Milano, Italy.
249
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