Accelerometer-Based Identification of Fatigue in the Lower Limbs during Cyclical Physical Exercise: A Systematic Review

Author:

Marotta LucaORCID,Scheltinga Bouke L.,van Middelaar Robbert,Bramer Wichor M.,van Beijnum Bert-Jan F.,Reenalda Jasper,Buurke Jaap H.

Abstract

Physical exercise (PE) is beneficial for both physical and psychological health aspects. However, excessive training can lead to physical fatigue and an increased risk of lower limb injuries. In order to tailor training loads and durations to the needs and capacities of an individual, physical fatigue must be estimated. Different measurement devices and techniques (i.e., ergospirometers, electromyography, and motion capture systems) can be used to identify physical fatigue. The field of biomechanics has succeeded in capturing changes in human movement with optical systems, as well as with accelerometers or inertial measurement units (IMUs), the latter being more user-friendly and adaptable to real-world scenarios due to its wearable nature. There is, however, still a lack of consensus regarding the possibility of using biomechanical parameters measured with accelerometers to identify physical fatigue states in PE. Nowadays, the field of biomechanics is beginning to open towards the possibility of identifying fatigue state using machine learning algorithms. Here, we selected and summarized accelerometer-based articles that either (a) performed analyses of biomechanical parameters that change due to fatigue in the lower limbs or (b) performed fatigue identification based on features including biomechanical parameters. We performed a systematic literature search and analysed 39 articles on running, jumping, walking, stair climbing, and other gym exercises. Peak tibial and sacral acceleration were the most common measured variables and were found to significantly increase with fatigue (respectively, in 6/13 running articles and 2/4 jumping articles). Fatigue classification was performed with an accuracy between 78% and 96% and Pearson’s correlation with an RPE (rate of perceived exertion) between r = 0.79 and r = 0.95. We recommend future effort toward the standardization of fatigue protocols and methods across articles in order to generalize fatigue identification results and increase the use of accelerometers to quantify physical fatigue in PE.

Funder

Horizon 2020 Framework Programme of the European Union for Research and Innovation

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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2. A Physical Fatigue Evaluation Method for Automotive Manual Assembly: An Experiment of Cerebral Oxygenation with ARE Platform;Sensors;2023-11-26

3. Exploring Runners' Preferences of Drone Based Feedback to Support their Well-Being;Proceedings of the 2nd International Conference of the ACM Greek SIGCHI Chapter;2023-09-27

4. Monitoring lower limb biomechanical asymmetry and psychological measures in athletic populations—A scoping review;Scandinavian Journal of Medicine & Science in Sports;2023-08-07

5. Small Data, Big Challenges: Pitfalls and Strategies for Machine Learning in Fatigue Detection;Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments;2023-07-05

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