ArticleScientific reports2020
Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 21 citations in OpenAlex.
- Enhancing mobile brain and body imaging: Open-source solutions for real-world research applications.iScience · 2026Article
- Research progress on digital-twin-driven full life-cycle design and manufacturing of rehabilitation assistive devices.Frontiers in bioengineering and biotechnology · 2026Review
- Exploring the electrical robustness of conductive textile fasteners for wearable devices in different human motion conditions.Scientific reports · 2024Article
- Thoughts and Considerations of Studies on Smart Devices for Cerebrovascular Disease Detection and Progression Monitoring.Mayo Clinic proceedings. Digital health · 2023Article
- Sensitiveness of Variables Extracted from a Fitness Smartwatch to Detect Changes in Vertical Impact Loading during Outdoors Running.Sensors (Basel, Switzerland) · 2023Article
- Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation.Frontiers in bioengineering and biotechnology · 2023Article
- Identifying and characterising sources of variability in digital outcome measures in Parkinson's disease.NPJ digital medicine · 2022Article
- Optimizing Sensor Position with Virtual Sensors in Human Activity Recognition System Design.Sensors (Basel, Switzerland) · 2021Article
- Neuromechanical Biomarkers for Robotic Neurorehabilitation.Frontiers in neurorobotics · 2021Review
- Self-Powered Non-Contact Triboelectric Rotation Sensor with Interdigitated Film.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a fundamentally new approach to design and assess wearable motion systems based on biomechanical simulation and sensor data synthesis. We devise a methodology of personal biomechanical models and virtually attach sensor models to body parts, including sensor positions frequently considered for wearable devices. The simulation enables us to synthesise motion sensor data, which is subsequently considered as input for gait marker estimation algorithms. We evaluated our methodology in two case studies, including running athletes and hemiparetic patients. Our analysis shows that running speed affects gait marker estimation performance. Estimation error of stride duration varies between athletes across 834 simulated sensor positions and can soar up to 54%, i.e. 404 ms. In walking patients after stroke, we show that gait marker performance differs between affected and less-affected body sides and optimal sensor positions change over a period of movement therapy intervention. For both case studies, we observe that optimal gait marker estimation performance benefits from personally selected sensor positions and robust algorithms. Our methodology enables wearable designers and algorithm developers to rapidly analyse the design options and create personalised systems where needed, e.g. for patients with movement disorders.
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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.