Non-obstructive monitoring of muscle fatigue for low intensity dynamic exercise with infrared thermography technique

Advancements in Textile-Based sEMG Sensors for Muscle Fatigue Detection: A Journey from Material Evolution to Technological Integration

Textile-based surface electromyography (sEMG) electrodes have emerged as a prominent tool in muscle fatigue assessment, marking a significant shift toward innovative, noninvasive methods. This review examines the transition from metallic fibers to novel conductive polymers, elastomers, and advanced material-based electrodes, reflecting on the rapid evolution of materials in sEMG sensor technology. It highlights the pivotal role of materials science in enhancing sensor adaptability, signal accuracy, and longevity, crucial for practical applications in health monitoring, while examining the balance of clinical precision with user comfort. Additionally, it maps the global sEMG research landscape of diverse regional contributors and their impact on technological progress, focusing on the integration of Eastern manufacturing prowess with Western technological innovations and exploring both the opportunities and challenges in this global synergy. The integration of such textile-based sEMG innovations with artificial intelligence, nanotechnology, energy harvesting, and IoT connectivity is also anticipated as future prospects. Such advancements are poised to revolutionize personalized preventive healthcare. As the exploration of textile-based sEMG electrodes continues, the transformative potential not only promises to revolutionize integrated wellness and preventive healthcare but also signifies a seamless transition from laboratory innovations to real-world applications in sports medicine, envisioning the future of truly wearable material technologies.

Thermal threshold for knee osteoarthritis people evaluated with infrared thermography: A scoping review

INTRODUCTION

Knee degenerative processes, such as osteoarthritis, are disabling. An early intervention is generally more effective making important a timely diagnosis. A pre-diagnosis tool could be the thermal camera that allows the detection of joint inflammation. Consequently, the objective of the present study was to evaluate the literature and propose a thermal attention threshold for infrared thermography data in people with knee osteoarthritis.

METHODS

four electronic databases were searched with specific keywords until the 25th of March 2024. Only original articles about joint inflammation due to osteoarthritis evaluated through digital infrared thermal images were included. A quality assessment analysis was performed. The attention threshold was extracted through the median of the extracted data. The findings were narratively discussed.

RESULTS

A total of 9 studies have been included after the eligibility criteria selection. The studies presented some differences in terms of acquisition protocol, thermal imaging camera, data extrapolation, and analysis. Despite these differences, the studies presented similar thermal data.

CONCLUSION

A knee thermography of or above 31.3 °C could indicate osteoarthritis, highlighting the necessity of further, more specific, and accurate analysis.

Thermal profile classification of the back of sportive and sedentary healthy individuals

BACKGROUND

Infrared thermography (IRT) is a non-harmful, risk-free imaging technique and it has application for healthy and pathological population.

OBJECTIVE

The aim of this study is to evaluate the thermographic profiles of the back of sport practitioners from different disciplines and compare it with those of sedentary healthy individuals.

METHOD

The back of 160 healthy subjects were evaluated, and participants were grouped considering their sport practice: team sport (TS), individual sport (IS), weight training (WT), inactive (I). Three regions of interest were identified to analyze the cervical, thoracic and lumbar temperatures of the back.

RESULTS

The Multivariate analysis of variance (MANOVA) resulted significant showing statistical differences for the cervical (p < 0.001), dorsal (p = 0.0011), and lumbar areas (p = 0.0366). The Tukey post-hoc test for pairwise comparison showed statistically significant differences between groups. For the cervical area significance was found between the IN and WT group (p = 0.002), the IN and IS group (p < 0.001), IN and TS group (p = 0.020). The dorsal area resulted significant between the IN and WT group (p = 0.007), the IN and IS group (p < 0.001), IN and TS group. The lumbar area showed significant differences only between the IN and WT group and the IN and IS group (p = 0.043).

CONCLUSION

This study demonstrated that inactive individuals manifest a statistically significant higher temperature in the cervical, dorsal and lumbar area of the back compared to sportive individuals.

Acute physiological responses to a pyramidal exercise protocol and the associations with skin temperature variation in different body areas

This study aimed to examine the skin temperature (Tsk) variations in five regions of interest (ROI) to assess whether possible disparities between the ROI's Tsk could be associated with specific acute physiological responses during cycling. Seventeen participants performed a pyramidal load protocol on a cycling ergometer. We synchronously measured Tsk in five ROI with three infrared cameras. We assessed internal load, sweat rate, and core temperature. Reported perceived exertion and calves' Tsk showed the highest correlation (r = -0.588; p < 0.01). Mixed regression models revealed that the heart rate and reported perceived exertion were inversely related to calves' Tsk. The exercise duration was directly associated with the nose tip and calf Tsk but inversely related to the forehead and forearm Tsk. The sweat rate was directly related to forehead and forearm Tsk. The association of Tsk with thermoregulatory or exercise load parameters depends on the ROI. The parallel observation of the face and calf Tsk could indicate simultaneously the observation of acute thermoregulatory needs and individual internal load. The separate Tsk analyses of individual ROI appear more suitable to examine specific physiological response than a mean Tsk of several ROI during cycling.

An exploratory, intra- and interindividual comparison of the deep neural network automatically measured calf surface radiation temperature during cardiopulmonary running and cycling exercise testing: A preliminary study

Non-invasive and contactless infrared thermography (IRT) measurements have been claimed to indicate acute neural, cardiovascular, and thermoregulatory adaptations during exercise. Due to challenging comparability, reproducibility, and objectivity, investigations considering different exercise types and intensities, and automatic ROI analysis are currently needed. Thus, we aimed to examine surface radiation temperature (T_sr) variations during different exercise types and intensities in the same individuals, ROI, and environmental conditions. Ten healthy, active males performed a cardiopulmonary exercise test on a treadmill in the first week and on a cycling ergometer the following week. Respiration, heart rate, lactate, rated perceived exertion, the mean, minimum, and maximum T_sr of the right calf (CT_sr (°C)), and the surface radiation temperature pattern (CP_sr) were explored. We executed two-way rmANOVA and Spearman's rho correlation analyses. Across all IRT parameters, mean CT_sr showed the highest association to cardiopulmonary parameters (E.g., oxygen consumption: r_s = -0.612 (running); -0.663 (cycling); p < .001). A global significant difference of CT_sr was identified between all relevant exercise test increments for both exercise-types (p < .001; η²_p = .842) and between both exercise-types (p = .045; η²_p = .205). Differences in CT_sr between running and cycling significantly appeared after a 3-min recovery period, whereas lactate, heart rate, and oxygen consumption were not different. High correlations between the CT_sr values extracted manually and the CT_sr values processed automatically by a deep neural network were identified. The applied objective time series analysis enables crucial insights into intra- and interindividual differences between both tests. CT_sr variations indicate different physiological demands between incremental running and cycling exercise testing. Further studies applying automatic ROI analyses are needed to enable the extensive analysis of inter- and intraindividual factors influencing the CT_sr variation during exercise to allow determine the criterion and predictive validity of IRT parameters in exercise physiology.

Can Data-Driven Supervised Machine Learning Approaches Applied to Infrared Thermal Imaging Data Estimate Muscular Activity and Fatigue?

Surface electromyography (sEMG) is the acquisition, from the skin, of the electrical signal produced by muscle activation. Usually, sEMG is measured through electrodes with electrolytic gel, which often causes skin irritation. Capacitive contactless electrodes have been developed to overcome this limitation. However, contactless EMG devices are still sensitive to motion artifacts and often not comfortable for long monitoring. In this study, a non-invasive contactless method to estimate parameters indicative of muscular activity and fatigue, as they are assessed by EMG, through infrared thermal imaging (IRI) and cross-validated machine learning (ML) approaches is described. Particularly, 10 healthy participants underwent five series of bodyweight squats until exhaustion interspersed by 1 min of rest. During exercising, the vastus medialis activity and its temperature were measured through sEMG and IRI, respectively. The EMG average rectified value (ARV) and the median frequency of the power spectral density (MDF) of each series were estimated through several ML approaches applied to IRI features, obtaining good estimation performances (r = 0.886, p < 0.001 for ARV, and r = 0.661, p < 0.001 for MDF). Although EMG and IRI measure physiological processes of a different nature and are not interchangeable, these results suggest a potential link between skin temperature and muscle activity and fatigue, fostering the employment of contactless methods to deliver metrics of muscular activity in a non-invasive and comfortable manner in sports and clinical applications.

Towards Exercise Radiomics: Deep Neural Network-Based Automatic Analysis of Thermal Images Captured During Exercise

Infrared thermography is increasingly applied in sports science due to promising observations regarding changes in skin's surface radiation temperature ( T_sr) before, during, and after exercise. The common manual thermogram analysis limits an objective and reproducible measurement of T_sr. Previous analysis approaches depend highly on expert knowledge and have not been applied during movement. We aimed to develop a deep neural network (DNN) capable of automatically and objectively segmenting body parts, recognizing blood vessel-associated T_sr distributions, and continuously measuring T_sr during exercise. We conducted 38 cardiopulmonary exercise tests on a treadmill. We developed two DNNs: body part network and vessel network, to perform semantic segmentation of 1 107 855 captured thermal images. Both DNNs were trained with 263 training and 75 validation images. Additionally, we compare the results of a common manual thermogram analysis with these of the DNNs. Performance analysis identified a mean IoU of 0.8 for body part network and 0.6 for vessel network. There is a high agreement between manual and automatic analysis (r = 0.999; p 0.001; T-test: p = 0.116), with a mean difference of 0.01 ^C (0.08). Non-parametric Bland Altman's analysis showed that the 95% agreement ranges between -0.086 ^C and 0.228 ^C. The developed DNNs enable automatic, objective, and continuous measurement of T_sr and recognition of blood vessel-associated T_sr distributions in resting and moving legs. Hence, the DNNs surpass previous algorithms by eliminating manual region of interest selection and form the currently needed foundation to extensively investigate T_sr distributions related to non-invasive diagnostics of (patho-)physiological traits in means of exercise radiomics.

Automated and Controlled System for Analysis of Residual Limbs Thermograms of Transtibial Amputees

This work describes the development of a controlled cabin for capturing and analyzing thermal images. The motivation of such a device is to aid in the thermal image acquisition process within a confined space. The thermograms generated provide helpful information for analyzing the residual human limb in subjects with transtibial amputation. Such a study proposes a non-intrusive method to study the thermal activity on the amputee residual limb and seek a correlation to the quality of the socket. The proposed cabin ensures the repeatability of the thermograms acquisition process and provides an isolated workspace, thus improving the quality of the samples. The methodology consists of the design of the mechanical elements and parts of the system on computer-aided design software, the electronic instrumentation, a graphic user interface, and the control algorithm based on a barrier Lyapunov function to solve the trajectory tracking for the camera movements, and numerical simulations to illustrate the functionality and the manufacture of a prototype. The results obtained by implementing the control design on the automated cabin reveal that the thermal image acquisition process is completed following the desired trajectory with a mean squared tracking error of 0.0052. In addition, an example of the thermal images of two subjects and the results processing this class of pictures using the designed interface is shown.

Effects of Unilateral Muscle Fatigue on Thermographic Skin Surface Temperature of Back and Abdominal Muscles—A Pilot Study

The present study aimed to assess the effects of asymmetric muscle fatigue on the skin surface temperature of abdominal and back muscles. The study was based on a pre-post/follow-up design with one group and included a total of 41 subjects (22 male, 19 female; age, 22.63 ± 3.91; weight, 71.89 ± 12.97 kg; height, 173.36 ± 9.95). All the participants were asked to perform side bends in sets of 20 repetitions on a Roman chair until complete exhaustion. The pre-, post- and follow-up test (24 h after) skin surface temperatures were recorded with infrared thermography. Subjective muscle soreness and muscle fatigue were analyzed using two questionnaires. The results of the post hoc tests showed that skin temperature was statistically significantly lower in the post-tests than in the pre- and follow-up tests, but no meaningful differences existed between the pre- and follow-up tests. Asymmetric side differences were found in the post-test for the upper and lower areas of the back. Differences were also noted for the front in both the upper and lower areas. No thermographic side asymmetries were found at the pre- or follow-up measurement for either the back or the front. Our results support the potential of using thermographic skin surface temperature to monitor exercise and recovery in athletes, as well as its use in rehabilitational exercise selection.