ArticleSensors (Basel, Switzerland)2022
Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson's Disease.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Physical Activity in Community-Dwelling Older Adults: Which Real-World Accelerometry Measures Are Robust? A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Continuous assessment of daily-living gait using self-supervised learning of wrist-worn accelerometer data.NPJ digital medicine · 2026Article
- AI-Enabled Wearables for Motor Function Assessment and Rehabilitation in Parkinson Disease: Scoping Review.Journal of medical Internet research · 2026Review
- IMU-based gait analysis methods: a systematic review of techniques for different body locations.Frontiers in sports and active living · 2026Review
- Lumbar Acceleration Gait Estimation: "Step-by-Step" Algorithm Updates and Improvements.Journal of medical Internet research · 2025Article
- Real-World Wrist-Derived Digital Mobility Outcomes in People with Multiple Long-Term Conditions: A Comparison of Algorithms.Bioengineering (Basel, Switzerland) · 2025Article
- Quantifying arm swing in Parkinson's disease: a method accounting for arm activities during free-living gait.Journal of neuroengineering and rehabilitation · 2025Article
- Machine learning approach to gait analysis for Parkinson's disease detection and severity classification.Frontiers in robotics and AI · 2025Article
- Advances in applying somatosensory interaction technology in geriatric care: A bibliometric analysis.International journal of nursing sciences · 2024Review
- Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults.Scientific reports · 2024Article
- In-Clinic and Natural Gait Observations master protocol (I-CAN-GO) to validate gait using a lumbar accelerometer.Scientific reports · 2024Article
- Assessing the clinical utility of inertial sensors for home monitoring in Parkinson's disease: a comprehensive review.NPJ Parkinson's disease · 2024Article
- Assisted technology in Parkinson's disease gait: what's up?Arquivos de neuro-psiquiatria · 2024Review
- Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study.JMIR formative research · 2024Article
- Automated Gait Detection in Older Adults during Daily-Living using Self-Supervised Learning of Wrist-Worn Accelerometer Data: Development and Validation of ElderNet.Research square · 2024Article
- Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers?Sensors (Basel, Switzerland) · 2023Article
- Classifying Tremor Dominant and Postural Instability and Gait Difficulty Subtypes of Parkinson's Disease from Full-Body Kinematics.Sensors (Basel, Switzerland) · 2023Article
- Correlates of Person-Specific Rates of Change in Sensor-Derived Physical Activity Metrics of Daily Living in the Rush Memory and Aging Project.Sensors (Basel, Switzerland) · 2023Article
- The role of wrist-worn technology in the management of Parkinson's disease in daily life: A narrative review.Frontiers in neuroinformatics · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Remote assessment of the gait of older adults (OAs) during daily living using wrist-worn sensors has the potential to augment clinical care and mobility research. However, hand movements can degrade gait detection from wrist-sensor recordings. To address this challenge, we developed an anomaly detection algorithm and compared its performance to four previously published gait detection algorithms. Multiday accelerometer recordings from a wrist-worn and lower-back sensor (i.e., the “gold-standard” reference) were obtained in 30 OAs, 60% with Parkinson’s disease (PD). The area under the receiver operator curve (AUC) and the area under the precision−recall curve (AUPRC) were used to evaluate the performance of the algorithms. The anomaly detection algorithm obtained AUCs of 0.80 and 0.74 for OAs and PD, respectively, but AUPRCs of 0.23 and 0.31 for OAs and PD, respectively. The best performing detection algorithm, a deep convolutional neural network (DCNN), exhibited high AUCs (i.e., 0.94 for OAs and 0.89 for PD) but lower AUPRCs (i.e., 0.66 for OAs and 0.60 for PD), indicating trade-offs between precision and recall. When choosing a classification threshold of 0.9 (i.e., opting for high precision) for the DCNN algorithm, strong correlations (r > 0.8) were observed between daily living walking time estimates based on the lower-back (reference) sensor and the wrist sensor. Further, gait quality measures were significantly different in OAs and PD compared to healthy adults. These results demonstrate that daily living gait can be quantified using a wrist-worn sensor.
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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.