ArticlePloS one2022
The placement of foot-mounted IMU sensors does affect the accuracy of spatial parameters during regular walking.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 28 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
- Measurement Properties of Wearable Kinematic-Based Data Collection Systems to Evaluate Ball Kicking in Soccer: A Systematic Review with Evidence Gap Map.Sensors (Basel, Switzerland) · 2024Pooled it
- Finite-Stroke Magnetic Quasi-Zero-Stiffness Electromagnetic Harvester for Foot-Worn Sensors: Reproducible Numerical Design Under Public Foot-IMU Excitation.Micromachines · 2026Article
- ML-based electromyography signal analysis for assessing rehabilitation exercise execution quality.Scientific reports · 2026Article
- Reliability and Discriminant Ability of an Instrumented Timed Up and Go Test in People With Postsurgical Orthopedic Conditions: Quantitative Study.JMIR rehabilitation and assistive technologies · 2026Article
- Stroke Rehabilitation, Novel Technology and the Internet of Medical Things.Brain sciences · 2026Review
- REEV SENSE IMUs for Spatiotemporal Gait Analysis in Post-Stroke Patients: Validation Against Optical Motion Capture.Sensors (Basel, Switzerland) · 2026Article
- Assessment of IMU-derived lower-limb kinematics in transtibial amputees: A practical and accessible clinical workflow.PloS one · 2026Article
- A deep learning pipeline for detecting vestibular schwannoma patients with unilateral vestibular loss based on kinematic data.Scientific reports · 2025Article
- A Dataset of Clinical Gait Signals with Wearable Sensors from Healthy, Neurological, and Orthopedic Cohorts.Scientific data · 2025Article
- Instrumented Functional Mobility Assessment in Elderly Patients Following Total Knee Arthroplasty: A Retrospective Longitudinal Study Using the Timed Up and Go Test.Life (Basel, Switzerland) · 2025Article
- Automated Sidewalk Surface Detection Using Wearable Accelerometry and Deep Learning.Sensors (Basel, Switzerland) · 2025Article
- A Review of Gait Analysis Using Gyroscopes and Inertial Measurement Units.Sensors (Basel, Switzerland) · 2025Review
- Predicting community walking after stroke.Frontiers in stroke · 2025Article
- Comparing the Ground Reaction Forces, Toe Clearances, and Stride Lengths of Young and Older Adults Using a Novel Shoe Sensor System.Sensors (Basel, Switzerland) · 2024Article
- Validity of an Inertial Measurement Unit System to Measure Lower Limb Kinematics at Point of Contact during Incremental High-Speed Running.Sensors (Basel, Switzerland) · 2024Article
- Foot orientation and trajectory variability in locomotion: Effects of real-world terrain.PloS one · 2024Article
- Gaitmap-An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking.IEEE open journal of engineering in medicine and biology · 2024Article
- Characterizing Bodyweight-Supported Treadmill Walking on Land and Underwater Using Foot-Worn Inertial Measurement Units and Machine Learning for Gait Event Detection.Sensors (Basel, Switzerland) · 2023Article
- Validity of Spatio-Temporal Gait Parameters in Healthy Young Adults Using a Motion-Sensor-Based Gait Analysis System (ORPHE ANALYTICS) during Walking and Running.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gait analysis using foot-worn inertial measurement units has proven to be a reliable tool to diagnose and monitor many neurological and musculoskeletal indications. However, only few studies have investigated the robustness of such systems to changes in the sensor attachment and no consensus for suitable sensor positions exists in the research community. Specifically for unsupervised real-world measurements, understanding how the reliability of the monitoring system changes when the sensor is attached differently is from high importance. In these scenarios, placement variations are expected because of user error or personal preferences. In this manuscript, we present the largest study to date comparing different sensor positions and attachments. We recorded 9000 strides with motion-capture reference from 14 healthy participants with six synchronized sensors attached at each foot. Spatial gait parameters were calculated using a double-integration method and compared to the reference system. The results indicate that relevant differences in the accuracy of the stride length exists between the sensor positions. While the average error over multiple strides is comparable, single stride errors and variability parameters differ greatly. We further present a physics model and an analysis of the raw sensor data to understand the origin of the observed differences. This analysis indicates that a variety of attachment parameters can influence the systems' performance. While this is only the starting point to understand and mitigate these types of errors, we conclude that sensor systems and algorithms must be reevaluated when the sensor position or attachment changes.
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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.