Automatic initial contact detection during overground walking for clinical use |
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| Affiliation: | 1. Julius Wolff Institute, Charité – Universitätsmedizin Berlin, Germany;2. Center for Sports Medicine and Sport Sciences Berlin, Germany;3. Center for Musculoskeletal Surgery, Charité – Universitätsmedizin Berlin, Germany;1. Cardiovascular Imaging Department, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom;2. The Lupus Unit, Rayne’s Institute, King’s College London, London, United Kingdom;3. Department of Medical Physics and Bioengineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom;4. Cardiovascular Division, King’s College London, London, United Kingdom;1. Physical Therapy Department, Faculdade Ciências Médicas de Minas Gerais, Belo Horizonte, MG, Brazil;2. Graduate Program in Rehabilitation Sciences, School of Physical Education, Physical Therapy and Occupational Therapy, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil;1. Centre for Mental Health Nursing Innovation, Institute for Health and Social Science Research, School of Nursing and Midwifery, Central Queensland University, Bruce Highway Rockhampton QLD 4702, Australia;2. Centre for Physical Activity Studies, Institute for Health and Social Science Research, School of Human, Health and Social Sciences, Central Queensland University, Bruce Highway, Rockhampton QLD 4702, Australia;1. Department of Applied Science, UIET, Kurukshetra University, Kurukshetra-136 119, India;2. Department of Physics, Kurukshetra University, Kurukshetra-136 119, India;1. DOD/VA Extremity Trauma and Amputation Center of Excellence, USA;2. Research & Development Section, Department of Rehabilitation, Walter Reed National Military Medical Center, Bethesda, MD 20889, USA;3. Department of Physical Medicine and Rehabilitation, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA |
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| Abstract: | The division of gait into cycles is crucial for identifying deficits in locomotion, particularly to monitor disease progression or rehabilitative recovery. Initial contact (IC) events are often used to separate movement into repetitive cycles yet automatic methods for IC identification in pathological gait are limited in both number and capacity. The aim of this work was to develop a more precise algorithm in IC detection. A projected heel markers distance (PHMD) algorithm is presented here and compared for accuracy to the high pass algorithm (HPA) in IC identification. Kinematic gait data from two clinical cohorts were analyzed and processed automatically for IC detection: (1) unilateral total hip arthroplasty (THA) patients (n = 27) and (2) cerebral palsy pediatric (CPP) patients (n = 20). IC events determined by the two algorithms were benchmarked against the IC events detected manually and from force plates. The PHMD method detected 96.6% IC events in THA patients and 99.1% in CPP patients with an average error of 5.3 ms and 18.4 ms. The HPA method detected 99.1% IC events in THA patients and 97.3% IC events in CPP patients, with an average error of 57.5 ms and 10.2 ms. PHMD identified no superfluous IC events, whereas 51.5% of all THA IC and 47.6% of CPP IC were superfluous events requiring manual deletion with HPA. With the superior comparison against the current gold standard, the PHMD algorithm appears valid for a wide spectrum of clinical data sets and allows for precise, fully automatic processing of kinematic gait data without additional sensors, triggers, or force plates. |
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| Keywords: | Gait Event detection Algorithm Overground walking |
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