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Classification of human walking context using a single-point accelerometer.
Baroudi, Loubna; Barton, Kira; Cain, Stephen M; Shorter, K Alex.
Afiliación
  • Baroudi L; Mechanical Engineering, University of Michigan, Ann Arbor, 48109, USA. lbaroudi@umich.edu.
  • Barton K; Mechanical Engineering, University of Michigan, Ann Arbor, 48109, USA.
  • Cain SM; Robotics, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Shorter KA; Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, 26505, USA.
Sci Rep ; 14(1): 3039, 2024 02 06.
Article en En | MEDLINE | ID: mdl-38321039
ABSTRACT
Real-world walking data offers rich insights into a person's mobility. Yet, daily life variations can alter these patterns, making the data challenging to interpret. As such, it is essential to integrate context for the extraction of meaningful information from real-world movement data. In this work, we leveraged the relationship between the characteristics of a walking bout and context to build a classification algorithm to distinguish between indoor and outdoor walks. We used data from 20 participants wearing an accelerometer on the thigh over a week. Their walking bouts were isolated and labeled using GPS and self-reporting data. We trained and validated two machine learning models, random forest and ensemble Support Vector Machine, using a leave-one-participant-out validation scheme on 15 subjects. The 5 remaining subjects were used as a testing set to choose a final model. The chosen model achieved an accuracy of 0.941, an F1-score of 0.963, and an AUROC of 0.931. This validated model was then used to label the walks from a different dataset with 15 participants wearing the same accelerometer. Finally, we characterized the differences between indoor and outdoor walks using the ensemble of the data. We found that participants walked significantly faster, longer, and more continuously when walking outdoors compared to indoors. These results demonstrate how movement data alone can be used to obtain accurate information on important contextual factors. These factors can then be leveraged to enhance our understanding and interpretation of real-world movement data, providing deeper insights into a person's health.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Caminata / Aprendizaje Automático Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Caminata / Aprendizaje Automático Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos