Trial reportAnnals of biomedical engineering2014
Classifying lower extremity muscle fatigue during walking using machine learning and inertial sensors.
Trial report in Annals of biomedical engineering, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
49 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review.Sports medicine (Auckland, N.Z.) · 2023Pooled it
- Accelerometer-Based Identification of Fatigue in the Lower Limbs during Cyclical Physical Exercise: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Photobiomodulation therapy for the improvement of muscular performance and reduction of muscular fatigue associated with exercise in healthy people: a systematic review and meta-analysis.Lasers in medical science · 2018Pooled it
- Development of a Strategy to Predict and Detect Falls Using Wearable Sensors.Journal of medical systems · 2019Trial
- Biomechanical Monitoring of Exercise Fatigue Using Wearable Devices: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Machine-Learning-Based Fatigue Trend Analysis on IMU Wearable Sensor Data from Construction Site Workers.Sensors (Basel, Switzerland) · 2025Article
- Non-Contact Fatigue Estimation in Healthy Individuals Using Azure Kinect: Contribution of Multiple Kinematic Features.Sensors (Basel, Switzerland) · 2025Article
- Ensemble learning for biomedical signal classification: a high-accuracy framework using spectrograms from percussion and palpation.Scientific reports · 2025Article
- A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model.Sensors (Basel, Switzerland) · 2025Article
- Effects of an active ankle exoskeleton on the walking biomechanics of healthy men.Frontiers in bioengineering and biotechnology · 2025Article
- Automatic Detection of Fatigued Gait Patterns in Older Adults: An Intelligent Portable Device Integrating Force and Inertial Measurements with Machine Learning.Annals of biomedical engineering · 2025Article
- Assessing and Enhancing Movement Quality Using Wearables and Consumer Technologies: Thematic Analysis of Expert Perspectives.JMIR formative research · 2024Article
- Evaluation of walking activity and gait to identify physical and mental fatigue in neurodegenerative and immune disorders: preliminary insights from the IDEA-FAST feasibility study.Journal of neuroengineering and rehabilitation · 2024Article
- Effects of static exercises on hip muscle fatigue and knee wobble assessed by surface electromyography and inertial measurement unit data.Scientific reports · 2024Article
- From Simulation to Reality: Predicting Torque With Fatigue Onset via Transfer Learning.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024Article
- Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics.PLoS computational biology · 2023Article
- DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units.Scientific data · 2023Article
- Classifying Unstable and Stable Walking Patterns Using Electroencephalography Signals and Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2023Article
- Model of the Performance Based on Artificial Intelligence-Fuzzy Logic Description of Physical Activity.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Fatigue in lower extremity musculature is associated with decline in postural stability, motor performance and alters normal walking patterns in human subjects. Automated recognition of lower extremity muscle fatigue condition may be advantageous in early detection of fall and injury risks. Supervised machine learning methods such as support vector machines (SVMs) have been previously used for classifying healthy and pathological gait patterns and also for separating old and young gait patterns. In this study we explore the classification potential of SVM in recognition of gait patterns utilizing an inertial measurement unit associated with lower extremity muscular fatigue. Both kinematic and kinetic gait patterns of 17 participants (29 ± 11 years) were recorded and analyzed in normal and fatigued state of walking. Lower extremities were fatigued by performance of a squatting exercise until the participants reached 60% of their baseline maximal voluntary exertion level. Feature selection methods were used to classify fatigue and no-fatigue conditions based on temporal and frequency information of the signals. Additionally, influences of three different kernel schemes (i.e., linear, polynomial, and radial basis function) were investigated for SVM classification. The results indicated that lower extremity muscle fatigue condition influenced gait and loading responses. In terms of the SVM classification results, an accuracy of 96% was reached in distinguishing the two gait patterns (fatigue and no-fatigue) within the same subject using the kinematic, time and frequency domain features. It is also found that linear kernel and RBF kernel were equally good to identify intra-individual fatigue characteristics. These results suggest that intra-subject fatigue classification using gait patterns from an inertial sensor holds considerable potential in identifying "at-risk" gait due to muscle fatigue.
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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.