ArticleJournal of neuroengineering and rehabilitation2024
Enhanced gait tracking measures for individuals with stroke using leg-worn inertial sensors.
Article in Journal of neuroengineering and rehabilitation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- [Personalized lower-limb gait assessment method based on musculoskeletal modeling and machine learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Concurrent Validity and Reliability of Inertial Sensor-Based Wearables for Quantifying Spatial-Temporal Gait Parameters After Stroke: A Systematic Review.Brain sciences · 2026Review
- A Review of Gait Analysis Using Gyroscopes and Inertial Measurement Units.Sensors (Basel, Switzerland) · 2025Review
- An Open-Source Wearable System for Real-Time Human Biomechanical Analysis.Sensors (Basel, Switzerland) · 2025Article
- Decade-long insights into AI for orthopedic rehabilitation mapping research networks and future trajectories.Digital healthArticle
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4 authors.
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Abstract
backgroundClinical gait analysis plays a pivotal role in diagnosing and treating walking impairments. Inertial measurement units (IMUs) offer a low-cost, portable, and practical alternative to traditional gait analysis equipment, making these techniques more accessible beyond specialized clinics. Previous work and algorithms developed for specific clinical populations, like in individuals with Parkinson's disease, often do not translate effectively to other groups, such as stroke survivors, who exhibit significant variability in their gait patterns. The Salarian gait segmentation algorithm (SGSA) has demonstrated the potential to detect gait events and subsequently estimate clinical measures of gait speed, stride time, and other temporal parameters using two leg-worn IMUs in individuals with Parkinson's disease. However, the distinct gait impairments in stroke survivors, including hemiparesis, spasticity, and muscle weakness, can interfere with SGSA performance. Thus, the objective of this study was to develop and test an enhanced gait segmentation algorithm (EGSA) to capture temporal gait parameters in individuals with stroke.
methodsForty-one individuals with stroke were recruited from two acute rehabilitation settings and completed brief walking bouts with two leg-worn IMUs. We compared foot-off (FO), foot contact (FC), and temporal gait parameters computed from the SGSA and EGSA against ground truth measurements from an instrumented mat.
resultsThe EGSA demonstrated greater accuracy than the SGSA when detecting gait events within one second, for both FO (96% vs. 90%) and FC (94% vs. 91%). The EGSA also demonstrated lower error than the SGSA when detecting paretic FC, and FO events in slow, asymmetrical, and non-paretic footfalls. Temporal gait parameters from the EGSA had high reliability (ICC > 0.90) for stride time, step time, stance time, and double support time across gait speeds and levels of asymmetry.
conclusionThis approach has the potential to enhance the accuracy and validity of IMU-based gait analysis in individuals with stroke, thereby enhancing clinicians' ability to monitor and intervene for gait impairments in a rehabilitation setting and beyond.
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