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

Abstract

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.

ApeTI: A Thermal Image Dataset for Face and Nose Segmentation with Apes

“…Previous comparative psychological research used thermal imaging to study the emotional states of various animal species [2,4,5]. Thermal imaging also finds its application in the human health sector [6][7][8] as in the industry [9]. Professor Palvadis and his team have conducted a series of studies on the retrieval of physiological signals, such as heart rate [10], breath rate [11], and stress detection [12], by utilizing temperature information from the perinasal area of humans.…”

Quantitative assessment of muscle fatigue during rowing ergometer exercise using wavelet analysis of surface electromyography (sEMG)

“…Furthermore, distortions and interference of the sEMG signal, which can result from, e.g., crosstalk of EMG signals of adjacent muscles, cannot be eliminated during the data analysis phase, which can also affect these results (Zhang et al, 2022). In dynamic movement, changes in sensor positioning may occur and cause sEMG signal artifacts (Perpetuini et al, 2023), which adds an additional layer of limitation to this type of measurement. Moreover, investigations into the prediction of fatigue-induced electromyographic signals are also of interest (Bala and Joshi, 2022).…”

An Overview of Approaches and Methods for the Cognitive Workload Estimation in Human–Machine Interaction Scenarios through Wearables Sensors

“…The task entails the creation of algorithms capable of analyzing and interpreting intricate data, adjusting to novel situations, and generating intelligent choices or predictions based on incoming data [22]. For instance, ML applied to physiological signals and imaging data, could provide a strong contribution for diagnostic purposes [23,24]. ML can be of relevant importance in the assessment of cognitive effort, when applied to physiological data for HMI.…”