On the Impact of Biceps Muscle Fatigue in Human Activity Recognition

Nowadays, Human Activity Recognition (HAR) systems, which use wearables and smart systems, are a part of our daily life. Despite the abundance of literature in the area, little is known about the impact of muscle fatigue on these systems' performance. In this work, we use the biceps concentration curls exercise as an example of a HAR activity to observe the impact of fatigue impact on such systems. Our dataset consists of 3000 biceps concentration curls performed and collected from 20 volunteers aged between 20-35. Our findings indicate that fatigue often occurs in later sets of an exercise and extends the completion time of later sets by up to 31% and decreases muscular endurance by 4.1%. Another finding shows that changes in data patterns are often occurring during fatigue presence, causing seven features to become statistically insignificant. Further findings indicate that fatigue can cause a substantial decrease in performance in both subject-specific and cross-subject models. Finally, we observed that a Feedforward Neural Network (FNN) showed the best performance in both cross-subject and subject-specific models in all our evaluations.

Automated summary

This research investigates the impact of muscle fatigue on Human Activity Recognition (HAR) systems, using biceps concentration curls as an example. Findings show that fatigue prolongs completion time of later sets and decreases muscular endurance, leading to changes in data patterns and affecting the performance of subject-specific and cross-subject models. Feedforward Neural Network (FNN) exhibits the best performance in both types of models.