ArticleSensors (Basel, Switzerland)2021
Orientation-Invariant Spatio-Temporal Gait Analysis Using Foot-Worn Inertial Sensors.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Wearable Sensors in Gait Assessment for Parkinson Disease and Stroke in Real-World Environments: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- IMU-based gait analysis methods: a systematic review of techniques for different body locations.Frontiers in sports and active living · 2026Review
- Detecting cognitive impairment in cerebrovascular disease using gait, dual tasks, and machine learning.BMC medical informatics and decision making · 2025Article
- Neural control of anticipatory braking and lateral balance predicts fall risk in older adults: BMI-dependent mechanisms.Frontiers in aging neuroscience · 2025Article
- Enhancing Intelligent Shoes with Gait Analysis: A Review on the Spatiotemporal Estimation Techniques.Sensors (Basel, Switzerland) · 2024Review
- The Use of Embedded IMU Insoles to Assess Gait Parameters: A Validation and Test-Retest Reliability Study.Sensors (Basel, Switzerland) · 2023Article
- Minding your steps: a cross-sectional pilot study using foot-worn inertial sensors and dual-task gait analysis to assess the cognitive status of older adults with mobility limitations.BMC geriatrics · 2023Article
- Usability Study of a Multicomponent Exergame Training for Older Adults with Mobility Limitations.International journal of environmental research and public health · 2021Article
- A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors.Sensors (Basel, Switzerland) · 2021Article
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Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Inertial sensors can potentially assist clinical decision making in gait-related disorders. Methods for objective spatio-temporal gait analysis usually assume the careful alignment of the sensors on the body, so that sensor data can be evaluated using the body coordinate system. Some studies infer sensor orientation by exploring the cyclic characteristics of walking. In addition to being unrealistic to assume that the sensor can be aligned perfectly with the body, the robustness of gait analysis with respect to differences in sensor orientation has not yet been investigated-potentially hindering use in clinical settings. To address this gap in the literature, we introduce an orientation-invariant gait analysis approach and propose a method to quantitatively assess robustness to changes in sensor orientation. We validate our results in a group of young adults, using an optical motion capture system as reference. Overall, good agreement between systems is achieved considering an extensive set of gait metrics. Gait speed is evaluated with a relative error of -3.1±9.2 cm/s, but precision improves when turning strides are excluded from the analysis, resulting in a relative error of -3.4±6.9 cm/s. We demonstrate the invariance of our approach by simulating rotations of the sensor on the foot.
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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.