SynthesisSensors (Basel, Switzerland)2022
Accelerometer-Based Identification of Fatigue in the Lower Limbs during Cyclical Physical Exercise: A Systematic Review.
Synthesis in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- The fatigue status feature of bicycle movement based on deep learning and signal processing technology.Scientific reports · 2025Article
- The effect of exercise-induced muscle fatigue on gait parameters among older adults: a systematic review and meta-analysis.European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity · 2025Review
- A novel method for assessing cycling movement status: an exploratory study integrating deep learning and signal processing technologies.BMC medical informatics and decision making · 2025Article
- Challenges and Prospects of Sensing Technology for the Promotion of Tele-Physiotherapy: A Narrative Review.Sensors (Basel, Switzerland) · 2024Review
- Movement Sensing Opportunities for Monitoring Dynamic Cognitive States.Sensors (Basel, Switzerland) · 2024Review
- A Physical Fatigue Evaluation Method for Automotive Manual Assembly: An Experiment of Cerebral Oxygenation with ARE Platform.Sensors (Basel, Switzerland) · 2023Article
- Inertial Sensors for Hip Arthroplasty Rehabilitation: A Scoping Review.Sensors (Basel, Switzerland) · 2023Article
- Upper-Limb Kinematic Behavior and Performance Fatigability of Elderly Participants Performing an Isometric Task: A Quasi-Experimental Study.Bioengineering (Basel, Switzerland) · 2023Article
- Detection of Horse Locomotion Modifications Due to Training with Inertial Measurement Units: A Proof-of-Concept.Sensors (Basel, Switzerland) · 2022Article
- Effects of Central and Peripheral Fatigue on Impact Characteristics during Running.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.