ArticleFrontiers in neurology2017
Validation of a Step Detection Algorithm during Straight Walking and Turning in Patients with Parkinson's Disease and Older Adults Using an Inertial Measurement Unit at the Lower Back.
Article in Frontiers in neurology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 1 of them a synthesis that pooled it.
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Who cites it
61 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications.Sensors (Basel, Switzerland) · 2022Pooled it
- Validation of an Algorithmic Pipeline for Wrist-Worn Devices to Estimate Walking Speed in People with Multiple Long-Term Conditions.Sensors (Basel, Switzerland) · 2026Article
- Validation of Inertial Sensor-Based Step Detection Algorithms for Edge Device Deployment.Sensors (Basel, Switzerland) · 2026Article
- Real-World Wrist-Derived Digital Mobility Outcomes in People with Multiple Long-Term Conditions: A Comparison of Algorithms.Bioengineering (Basel, Switzerland) · 2025Article
- Comparison of Wearable and Depth-Sensing Technologies with Electronic Walkway for Comprehensive Gait Analysis.Sensors (Basel, Switzerland) · 2025Article
- Extensive digital health technology assessment detects subtle motor impairment in mild and asymptomatic Pompe disease.Scientific reports · 2025Article
- Detection of Gait Events Using Ear-Worn IMUs During Functional Movement Tasks.Sensors (Basel, Switzerland) · 2025Article
- Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approach.PeerJ. Computer science · 2025Article
- Validity of Wearable Inertial Sensors for Gait Analysis: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Enhanced gait tracking measures for individuals with stroke using leg-worn inertial sensors.Journal of neuroengineering and rehabilitation · 2024Article
- Quantitative Gait and Balance Outcomes for Ataxia Trials: Consensus Recommendations by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers.Cerebellum (London, England) · 2024Review
- Spectral peak analysis and intrinsic neural timescales as markers for the state of consciousness.NeuroImage. Clinical · 2024Article
- Clinical and device-based predictors of improved experience of activities of daily living after a multidisciplinary inpatient treatment for people with Parkinson's disease: a cohort study.Therapeutic advances in neurological disorders · 2024Article
- Measurement properties of device-based physical activity instruments in ambulatory adults with physical disabilities and/or chronic diseases: a scoping review.BMC sports science, medicine & rehabilitation · 2023Article
- Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium.Journal of neuroengineering and rehabilitation · 2023Article
- Comparison of Three Motion Capture-Based Algorithms for Spatiotemporal Gait Characteristics: How Do Algorithms Affect Accuracy and Precision of Clinical Outcomes?Sensors (Basel, Switzerland) · 2023Article
- Peripheral neuropathy in Parkinson's disease: prevalence and functional impact on gait and balance.Brain : a journal of neurology · 2023Article
- Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases.Frontiers in neurology · 2023Article
- Unsupervised but not supervised gait parameters are related to fatigue in Parkinson's disease: a pilot study.Frontiers in aging neuroscience · 2023Article
- Design and validation of a multi-task, multi-context protocol for real-world gait simulation.Journal of neuroengineering and rehabilitation · 2022Article
1 more citing papers are in PubMed but not listed here.
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Authors and funding
19 authors.
Funding
Abstract
introductionInertial measurement units (IMUs) positioned on various body locations allow detailed gait analysis even under unconstrained conditions. From a medical perspective, the assessment of vulnerable populations is of particular relevance, especially in the daily-life environment. Gait analysis algorithms need thorough validation, as many chronic diseases show specific and even unique gait patterns. The aim of this study was therefore to validate an acceleration-based step detection algorithm for patients with Parkinson's disease (PD) and older adults in both a lab-based and home-like environment.
methodsIn this prospective observational study, data were captured from a single 6-degrees of freedom IMU (APDM) (3DOF accelerometer and 3DOF gyroscope) worn on the lower back. Detection of heel strike (HS) and toe off (TO) on a treadmill was validated against an optoelectronic system (Vicon) (11 PD patients and 12 older adults). A second independent validation study in the home-like environment was performed against video observation (20 PD patients and 12 older adults) and included step counting during turning and non-turning, defined with a previously published algorithm.
resultsA continuous wavelet transform (cwt)-based algorithm was developed for step detection with very high agreement with the optoelectronic system. HS detection in PD patients/older adults, respectively, reached 99/99% accuracy. Similar results were obtained for TO (99/100%). In HS detection, Bland-Altman plots showed a mean difference of 0.002 s [95% confidence interval (CI) -0.09 to 0.10] between the algorithm and the optoelectronic system. The Bland-Altman plot for TO detection showed mean differences of 0.00 s (95% CI -0.12 to 0.12). In the home-like assessment, the algorithm for detection of occurrence of steps during turning reached 90% (PD patients)/90% (older adults) sensitivity, 83/88% specificity, and 88/89% accuracy. The detection of steps during non-turning phases reached 91/91% sensitivity, 90/90% specificity, and 91/91% accuracy.
conclusionThis cwt-based algorithm for step detection measured at the lower back is in high agreement with the optoelectronic system in both PD patients and older adults. This approach and algorithm thus could provide a valuable tool for future research on home-based gait analysis in these vulnerable cohorts.
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