دورية أكاديمية
Real-world gait detection using a wrist-worn inertial sensor: validation study
| العنوان: | Real-world gait detection using a wrist-worn inertial sensor: validation study |
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| المؤلفون: | Kluge, F., Brand, Y.E., Micó-Amigo, M.E., Bertuletti, S., D'Ascanio, I., Gazit, E., Bonci, T., Kirk, C., Küderle, A., Palmerini, L., Paraschiv-Ionescu, A., Salis, F., Soltani, A., Ullrich, M., Alcock, L., Aminian, K., Becker, C., Brown, P., Buekers, J., Carsin, A.-E., Caruso, M., Caulfield, B., Cereatti, A., Chiari, L., Echevarria, C., Eskofier, B., Evers, J., Garcia-Aymerich, J., Hache, T., Hansen, C., Hausdorff, J.M., Hiden, H., Hume, E., Keogh, A., Koch, S., Maetzler, W., Megaritis, D., Niessen, M., Perlman, O., Schwickert, L., Scott, K., Sharrack, B., Singleton, D., Vereijken, B., Vogiatzis, I., Yarnall, A., Rochester, L., Mazzà, C., Del Din, S., Mueller, A. |
| بيانات النشر: | JMIR Publications Inc. |
| سنة النشر: | 2024 |
| المجموعة: | White Rose Research Online (Universities of Leeds, Sheffield & York) |
| الوصف: | Background: Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. Objective: The aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back–worn inertial sensors. Methods: Participants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back–worn inertial sensors. Results: The best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | text |
| اللغة: | English |
| العلاقة: | https://eprints.whiterose.ac.uk/213253/1/Real-World%20Gait%20Detection%20Using%20a%20Wrist-Worn%20Inertial%20Sensor%20Validation%20Study.pdfTest; Kluge, F. orcid.org/0000-0003-4921-6104 , Brand, Y.E. orcid.org/0000-0002-4214-0699 , Micó-Amigo, M.E. orcid.org/0000-0002-8968-6373 et al. (47 more authors) (2024) Real-world gait detection using a wrist-worn inertial sensor: validation study. JMIR Formative Research, 8. e50035. ISSN 2561-326X |
| DOI: | 10.2196/50035 |
| الإتاحة: | https://doi.org/10.2196/50035Test https://eprints.whiterose.ac.uk/213253Test/ https://eprints.whiterose.ac.uk/213253/1/Real-World%20Gait%20Detection%20Using%20a%20Wrist-Worn%20Inertial%20Sensor%20Validation%20Study.pdfTest |
| حقوق: | cc_by_4 |
| رقم الانضمام: | edsbas.6AD9178D |
| قاعدة البيانات: | BASE |
| DOI: | 10.2196/50035 |
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