ArticleSensors (Basel, Switzerland)2016
Assessing Walking Strategies Using Insole Pressure Sensors for Stroke Survivors.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- Can wearable real-time biofeedback gait training devices improve gait speed, balance, functional mobility and activities of daily living (ADL) in individuals post-stroke? A systematic review and meta-analysis of randomized controlled trials.Journal of neuroengineering and rehabilitation · 2025Pooled it
- Effectiveness of Upper Limb Wearable Technology for Improving Activity and Participation in Adult Stroke Survivors: Systematic Review.Journal of medical Internet research · 2020Pooled it
- Wearable Sensor Technology and Biomechanical Gait Analysis in Post-Stroke Rehabilitation: A Review with Implications for the Saudi Healthcare System.Neurosciences (Riyadh, Saudi Arabia) · 2026Review
- AI Applications for Chronic Condition Self-Management: Scoping Review.Journal of medical Internet research · 2025Article
- Insole Pressure Sensors to Assess Post-Stroke Gait.Annals of rehabilitation medicine · 2024Article
- The Potential of Electrospinning to Enable the Realization of Energy-Autonomous Wearable Sensing Systems.ACS nano · 2024Review
- Accuracy, concurrent validity, and test-retest reliability of pressure-based insoles for gait measurement in chronic stroke patients.Frontiers in digital health · 2024Article
- Recent progress in flexible micro-pressure sensors for wearable health monitoring.Nanoscale advances · 2023Review
- Responsive fungal insoles for pressure detection.Scientific reports · 2023Article
- The Effects of Auditory Feedback Gait Training Using Smart Insole on Stroke Patients.Brain sciences · 2021Article
- Exploring the clinical context of adopting an instrumented insole: a qualitative study of clinicians' preferences in England.BMJ open · 2019Article
- Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis.NPJ digital medicine · 2019Article
- Wearable Movement Sensors for Rehabilitation: A Focused Review of Technological and Clinical Advances.PM & R : the journal of injury, function, and rehabilitation · 2018Review
- Knee Impedance Modulation to Control an Active Orthosis Using Insole Sensors.Sensors (Basel, Switzerland) · 2017Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Insole pressure sensors capture the different forces exercised over the different parts of the sole when performing tasks standing up such as walking. Using data analysis and machine learning techniques, common patterns and strategies from different users to achieve different tasks can be automatically extracted. In this paper, we present the results obtained for the automatic detection of different strategies used by stroke survivors when walking as integrated into an Information Communication Technology (ICT) enhanced Personalised Self-Management Rehabilitation System (PSMrS) for stroke rehabilitation. Fourteen stroke survivors and 10 healthy controls have participated in the experiment by walking six times a distance from chair to chair of approximately 10 m long. The Rivermead Mobility Index was used to assess the functional ability of each individual in the stroke survivor group. Several walking strategies are studied based on data gathered from insole pressure sensors and patterns found in stroke survivor patients are compared with average patterns found in healthy control users. A mechanism to automatically estimate a mobility index based on the similarity of the pressure patterns to a stereotyped stride is also used. Both data gathered from stroke survivors and healthy controls are used to evaluate the proposed mechanisms. The output of trained algorithms is applied to the PSMrS system to provide feedback on gait quality enabling stroke survivors to self-manage their rehabilitation.
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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.