ArticleAnnals of biomedical engineering2025
Automatic Detection of Fatigued Gait Patterns in Older Adults: An Intelligent Portable Device Integrating Force and Inertial Measurements with Machine Learning.
Article in Annals of biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- Smart Sensor-Driven Gait Rehabilitation Walker Using Machine Learning for Predictive Home-Based Therapy.Sensors (Basel, Switzerland) · 2026Article
- Biomechanical Monitoring of Exercise Fatigue Using Wearable Devices: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Effects of excess bodyweight and flexible Flatfoot on female stance-phase lower-limb biomechanics assessed by statistical non-parametric mapping.Scientific reports · 2025Article
- Identifying Fatigue-Related Gait Patterns Using Multiple Inertial Measurement Units and Statistical Parametric Mapping: A Continuous Analysis of an Outdoor Full Marathon in Male Recreational Runners.Sports medicine - open · 2025Article
- A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model.Sensors (Basel, Switzerland) · 2025Article
- A machine learning approach for saddle height classification in cycling.Frontiers in sports and active living · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
purposeThis study aimed to assess the feasibility of early detection of fatigued gait patterns for older adults through the development of a smart portable device.
methodsThe smart device incorporated seven force sensors and a single inertial measurement unit (IMU) to measure regional plantar forces and foot kinematics. Data were collected from 18 older adults walking briskly on a treadmill for 60 min. The optimal feature set for each recognition model was determined using forward sequential feature selection in a wrapper fashion through fivefold cross-validation. The recognition model was selected from four machine learning models through leave-one-subject-out cross-validation.
resultsFive selected characteristics that best represented the state of fatigue included impulse at the medial and lateral arches (increased, p = 0.002 and p < 0.001), contact angle and rotation range of angle in the sagittal plane (increased, p < 0.001), and the variability of the resultant swing angular acceleration (decreased, p < 0.001). The detection accuracy based on the dual signal source of IMU and plantar force was 99%, higher than the 95% accuracy based on the single source. The intelligent portable device demonstrated excellent generalization (ranging from 93 to 100%), real-time performance (2.79 ms), and portability (32 g).
conclusionThe proposed smart device can detect fatigue patterns with high precision and in real time. SIGNIFICANCE: The application of this device possesses the potential to reduce the injury risk for older adults related to fatigue during gait.
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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.