ArticleWearable technologies2024
A wearable gait lab powered by sensor-driven digital twins for quantitative biomechanical analysis post-stroke.
Article in Wearable technologies, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Digital twin for neurological conditions: a systematic scoping review.Biomedical engineering letters · 2026Review
- Digital Twins in Orthopedics and Trauma: Concepts, Emerging Evidence, and Barriers to Clinical Translation.Journal of clinical medicine · 2026Review
- A predictive model of joint dynamics and ground reaction force using only leg length, body mass, and walking cadence.PloS one · 2026Article
- Graphomotor performance as a quantitative marker of Alzheimer's disease: a tablet-based case-control ROC study.Frontiers in neuroscience · 2026Article
- Reproducible candidate kinematic-electromyographic waveform markers of post-stroke gait from public multimodal waveform exports.Frontiers in medical technology · 2026Article
- Joint Function and Movement Variability During Daily Living Activities Performed Throughout the Home Setting: A Digital Twin Modeling Study.Sensors (Basel, Switzerland) · 2025Article
- The Relation Between Hemiparetic Gait Patterns and Walking Function After Stroke, as Measured with Wearable Sensors.Annals of biomedical engineering · 2025Observational
- Using Wearable Sensors for Sex Classification and Age Estimation from Walking Patterns.Sensors (Basel, Switzerland) · 2025Article
- Temporal neural coupling: reconciling the Bobath concept with task-oriented training for post-stroke upper limb recovery-a perspective.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Commonly, quantitative gait analysis post-stroke is performed in fully equipped laboratories housing costly technologies for quantitative evaluation of a patient's movement capacity. Combining such technologies with an electromyography (EMG)-driven musculoskeletal model can estimate muscle force properties non-invasively, offering clinicians insights into motor impairment mechanisms. However, lab-constrained areas and time-demanding sensor setup and data processing limit the practicality of these technologies in routine clinical care. We presented wearable technology featuring a multi-channel EMG-sensorized garment and an automated muscle localization technique. This allows unsupervised computation of muscle-specific activations, combined with five inertial measurement units (IMUs) for assessing joint kinematics and kinetics during various walking speeds. Finally, the wearable system was combined with a person-specific EMG-driven musculoskeletal model (referred to as human digital twins), enabling the quantitative assessment of movement capacity at a muscle-tendon level. This human digital twin facilitates the estimation of ankle dorsi-plantar flexion torque resulting from individual muscle-tendon forces. Results demonstrate the wearable technology's capability to extract joint kinematics and kinetics. When combined with EMG signals to drive a musculoskeletal model, it yields reasonable estimates of ankle dorsi-plantar flexion torques (
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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.