ArticleNPJ digital medicine2024
A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Integrating Artificial Intelligence with Wearable Sensors for Advanced Health Monitoring and Diagnosis.Biosensors · 2026Review
- Continuous assessment of daily-living gait using self-supervised learning of wrist-worn accelerometer data.NPJ digital medicine · 2026Article
- Comparison of machine learning models for bradykinesia assessment using digital health technology.Frontiers in digital health · 2026Article
- Decision-centered wearable biosensors for personalized rehabilitation: integrating multimodal monitoring, artificial intelligence, and closed-loop intervention.Frontiers in bioengineering and biotechnology · 2026Review
- Step-Length Estimation in Asymmetric Gait Using a Single Lower-Back IMU Data and a Biomechanical Model Inspired by a Double Inverted Pendulum.Bioengineering (Basel, Switzerland) · 2025Article
- Tug-vimu: a GoPro-based mobility assessment bridging the gap between technology and clinical practice.Journal of neuroengineering and rehabilitation · 2025Article
- Real-World Wrist-Derived Digital Mobility Outcomes in People with Multiple Long-Term Conditions: A Comparison of Algorithms.Bioengineering (Basel, Switzerland) · 2025Article
- Digital Activity Markers in Chronic Inflammatory Demyelinating Polyneuropathy.Annals of clinical and translational neurology · 2025Observational
- Wearable devices in neurological disorders: a narrative review of status quo and perspectives.Annals of translational medicine · 2025Review
- Enhancing wearable sensor data analysis for patient health monitoring using allied data disparity technique and multi instance ensemble perceptron learning.Scientific reports · 2025Article
- Review
- Digital biomechanical assessment of gait in patients with peripheral neuropathies.Journal of neuroengineering and rehabilitation · 2025Article
- Utility of synthetic musculoskeletal gaits for generalizable healthcare applications.Nature communications · 2025Article
- Wearable Technologies for Health Promotion and Disease Prevention in Older Adults: Systematic Scoping Review and Evidence Map.Journal of medical Internet research · 2025Article
- Development of machine learning models for gait-based classification of incomplete spinal cord injuries and cauda equina syndrome.Scientific reports · 2025Article
- Using machine learning to identify Parkinson's disease severity subtypes with multimodal data.Journal of neuroengineering and rehabilitation · 2025Article
- Comparison of clinical measures of motor function with a Holter monitor in Parkinson's disease.Clinical parkinsonism & related disorders · 2025Article
- Gut-brain axis modulation in remote rehabilitation of Parkinson's disease: reconstructing the fecal metabolome and nigral network connectivity.Frontiers in neurology · 2025Review
- AI-Aided Gait Analysis with a Wearable Device Featuring a Hydrogel Sensor.Sensors (Basel, Switzerland) · 2024Article
- Mobile Spatiotemporal Gait Segmentation Using an Ear-Worn Motion Sensor and Deep Learning.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Step length is an important diagnostic and prognostic measure of health and disease. Wearable devices can estimate step length continuously (e.g., in clinic or real-world settings), however, the accuracy of current estimation methods is not yet optimal. We developed machine-learning models to estimate step length based on data derived from a single lower-back inertial measurement unit worn by 472 young and older adults with different neurological conditions, including Parkinson's disease and healthy controls. Studying more than 80,000 steps, the best model showed high accuracy for a single step (root mean square error, RMSE = 6.08 cm, ICC(2,1) = 0.89) and higher accuracy when averaged over ten consecutive steps (RMSE = 4.79 cm, ICC(2,1) = 0.93), successfully reaching the predefined goal of an RMSE below 5 cm (often considered the minimal-clinically-important-difference). Combining machine-learning with a single, wearable sensor generates accurate step length measures, even in patients with neurologic disease. Additional research may be needed to further reduce the errors in certain conditions.
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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.