ArticleSensors (Basel, Switzerland)2020
Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson's Disease.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Meta-analysis of the quantitative assessment of lower extremity motor function in elderly individuals based on objective detection.Journal of neuroengineering and rehabilitation · 2024Pooled it
- Laboratory-based turning performance during walking in people with mild cognitive impairment and dementia.Journal of Alzheimer's disease : JAD · 2026Article
- Beyond en-bloc turning: dynamics of head-pelvis coordination in 360° turns in people with Parkinson's.Journal of neuroengineering and rehabilitation · 2026Article
- Turning Slowly Predicts Future Diagnosis of Parkinson's Disease: A Decade-Long Longitudinal Analysis.Annals of neurology · 2026Article
- Effects of Levodopa on Gait in People With Parkinson's Disease With and Without a History of Falls.Movement disorders clinical practice · 2025Article
- Deriving Motor States and Mobility Metrics from Gamified Augmented Reality Rehabilitation Exercises in People with Parkinson's Disease.Sensors (Basel, Switzerland) · 2025Article
- Your turn: At home turning angle estimation for Parkinson's disease severity assessment.Artificial intelligence in medicine · 2025Article
- Evaluating the Accuracy and Reliability of Real-World Digital Mobility Outcomes in Older Adults After Hip Fracture: Cross-Sectional Observational Study.JMIR formative research · 2025Observational
- Artificial Intelligence in the Diagnosis of Neurological Diseases Using Biomechanical and Gait Analysis Data: A Scopus-Based Bibliometric Analysis.Neurology international · 2025Review
- Predictive potential of circular walking in prodromal Parkinson's disease.Journal of Parkinson's disease · 2025Article
- Collider-based movement detection and control of wearable soft robots for visually augmenting dance performance.Frontiers in robotics and AI · 2024Article
- An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review.Journal of medical Internet research · 2023Article
- Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium.Journal of neuroengineering and rehabilitation · 2023Article
- A Machine Learning Pipeline for Gait Analysis in a Semi Free-Living Environment.Sensors (Basel, Switzerland) · 2023Article
- Effect of Levodopa and Environmental Setting on Gait and Turning Digital Markers Related to Falls in People with Parkinson's Disease.Movement disorders clinical practice · 2023Article
- An investigation of the contribution of different turn speeds during standing turns in individuals with and without Parkinson's disease.Scientific reports · 2022Article
- Understanding how people with Parkinson's disease turn in gait from a real-world in-home dataset.Parkinsonism & related disorders · 2022Article
- Suitability of a Low-Cost Wearable Sensor to Assess Turning in Healthy Adults.Sensors (Basel, Switzerland) · 2022Article
- An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks.Frontiers in bioengineering and biotechnology · 2022Article
- Investigating the Impact of Environment and Data Aggregation by Walking Bout Duration on Parkinson's Disease Classification Using Machine Learning.Frontiers in aging neuroscience · 2022Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Parkinson's disease (PD) is a common neurodegenerative disorder resulting in a range of mobility deficits affecting gait, balance and turning. In this paper, we present: (i) the development and validation of an algorithm to detect turns during gait; (ii) a method to extract turn characteristics; and (iii) the classification of PD using turn characteristics. Thirty-seven people with PD and 56 controls performed 180-degree turns during an intermittent walking task. Inertial measurement units were attached to the head, neck, lower back and ankles. A turning detection algorithm was developed and validated by two raters using video data. Spatiotemporal and signal-based characteristics were extracted and used for PD classification. There was excellent absolute agreement between the rater and the algorithm for identifying turn start and end (ICC ≥ 0.99). Classification modeling (partial least square discriminant analysis (PLS-DA)) gave the best accuracy of 97.85% when trained on upper body and ankle data. Balanced sensitivity (97%) and specificity (96.43%) were achieved using turning characteristics from the neck, lower back and ankles. Turning characteristics, in particular angular velocity, duration, number of steps, jerk and root mean square distinguished mild-moderate PD from controls accurately and warrant future examination as a marker of mobility impairment and fall risk in PD.
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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.