Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise

Abstract

Physical exercise (PE) has become an essential tool for different rehabilitation programs. High-intensity exercises (HIEs) have been demonstrated to provide better results in general health conditions, compared with low and moderate-intensity exercises. In this context, monitoring of a patients’ condition is essential to avoid extreme fatigue conditions, which may cause physical and physiological complications. Different methods have been proposed for fatigue estimation, such as: monitoring the subject’s physiological parameters and subjective scales. However, there is still a need for practical procedures that provide an objective estimation, especially for HIEs. In this work, considering that the sit-to-stand (STS) exercise is one of the most implemented in physical rehabilitation, a computational model for estimating fatigue during this exercise is proposed. A study with 60 healthy volunteers was carried out to obtain a data set to develop and evaluate the proposed model. According to the literature, this model estimates three fatigue conditions (low, moderate, and high) by monitoring 32 STS kinematic features and the heart rate from a set of ambulatory sensors (Kinect and Zephyr sensors). Results show that a random forest model composed of 60 sub-classifiers presented an accuracy of 82.5% in the classification task. Moreover, results suggest that the movement of the upper body part is the most relevant feature for fatigue estimation. Movements of the lower body and the heart rate also contribute to essential information for identifying the fatigue condition. This work presents a promising tool for physical rehabilitation.

A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States

“…They are consistent with previous studies that have used IMUs for monitoring physical activity. Common features computed from the acceleration signal are the mean [ 68 , 94 , 95 , 98 , 100 ], variance or standard deviation [ 68 , 95 , 101 , 120 ] and the entropy and energy of the data [ 61 , 68 ].…”

“…), time (which can be manifested through detrimental performance due to fatigue and improved performance due to learning effects) and degree of exercise difficulty. Therefore, to enhance fatigue estimation the use of artificial intelligence systems in optimizing and transforming human performance has been implemented as a further alternative to monitor and understand how an individual's performance deteriorates with fatigue accumulation [38,68]. These models appear as a complementary alternative to collected human performance data from the diverse detection techniques (i.e., qualitative or quantitative approaches) that allow classifying fatigue levels.…”

Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters

“…Since physical exertion is considered the primary source of fatigue, different methods have been proposed for its estimation, such as monitoring physiological responses and the use of subjective scales [ 12 ]. To avoid subjectivity and allow continuous monitoring, wearable technology has also made major advances, facilitating the noninvasive collection of multiple physiological variables in real-time [ 13 , 14 ].…”

“…Different supervised machine learning algorithms have been proposed to estimate physical fatigue within construction workers while performing simulated manual handling tasks [ 8 , 18 ] and regular duties in the field [ 19 ]. Equivalent approaches have been used to determine stress levels in train drivers in a high-speed rail simulator [ 20 ] and to distinguish fatigued and non-fatigued states after specific occupational activities [ 12 , 21 , 22 ]. In addition, neural networks have been applied to develop binary fatigue classifiers during manufacturing tasks [ 23 ].…”

Entwicklung einer KI-gestützten Bewegungstherapie bei onkologischen Palliativpatienten

“…Bei Chowdhury et al 12 werden multimodale Parameter wie Herzfrequenz, elektrodermale Aktivität und Hauttemperatur von am Körper getragenen Sensoren erfasst und mit Hilfe KI klassifiziert, um die subjektive Intensität der körperlichen Aktivität vorherzusagen. Eine Schätzung der subjektiven Ermüdung mittels KI wird durch Pyszora et al 13 vorgestellt. Zu diesem Zweck werden kardiale und kinematische Merkmale in einer Sitz-Steh-Übung verarbeitet.…”