Thigh-worn accelerometry: a comparative study of two no-code classification methods for identifying physical activity types.
Background: The more accurate we can assess human physical behaviour in free-living conditions the better we can understand its relationship with health and wellbeing. Thigh-worn accelerometry can be used to identify basic activity types as well as different postures with high accuracy. User-friendl...
| Publicado en: | International Journal of Behavioral Nutrition & Physical Activity Vol. 21; no. 1; pp. 1 - 12 |
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| Autores principales: | , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
BioMed Central
7/17/2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178776018&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178776018 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14795868 1CR7 jtl: International Journal of Behavioral Nutrition & Physical Activity issn: 14795868 maglogo: N pubinfo: dt: 7/17/2024 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 178776018 178776018 178776018 10.1186/s12966-024-01627-1 178776018 ppf: 1 ppct: 11 formats: tig: atl: Thigh-worn accelerometry: a comparative study of two no-code classification methods for identifying physical activity types. aug: au: Lendt, Claas Braun, Theresa Biallas, Bianca Froböse, Ingo Johansson, Peter J. affil: Institute for Movement Therapy and Movement-Oriented Prevention and Rehabilitation, German Sport University Cologne, Cologne, Germany sug: subj: Wearable Sensors Accelerometry Physical Activity Classification Physical Activity Evaluation Sedentary Behavior In Adulthood Thigh Software Health Behavior Information Needs Human Male Female Adult Protocols Algorithms Comparative Studies Walking Running Cycling Standing Videorecording Questionnaires Descriptive Statistics kappa Statistic Posture Physical Activity Adult: 19-44 years Male Female ab: Background: The more accurate we can assess human physical behaviour in free-living conditions the better we can understand its relationship with health and wellbeing. Thigh-worn accelerometry can be used to identify basic activity types as well as different postures with high accuracy. User-friendly software without the need for specialized programming may support the adoption of this method. This study aims to evaluate the classification accuracy of two novel no-code classification methods, namely SENS motion and ActiPASS. Methods: A sample of 38 healthy adults (30.8 ± 9.6 years; 53% female) wore the SENS motion accelerometer (12.5 Hz; ±4 g) on their thigh during various physical activities. Participants completed standardized activities with varying intensities in the laboratory. Activities included walking, running, cycling, sitting, standing, and lying down. Subsequently, participants performed unrestricted free-living activities outside of the laboratory while being video-recorded with a chest-mounted camera. Videos were annotated using a predefined labelling scheme and annotations served as a reference for the free-living condition. Classification output from the SENS motion software and ActiPASS software was compared to reference labels. Results: A total of 63.6 h of activity data were analysed. We observed a high level of agreement between the two classification algorithms and their respective references in both conditions. In the free-living condition, Cohen's kappa coefficients were 0.86 for SENS and 0.92 for ActiPASS. The mean balanced accuracy ranged from 0.81 (cycling) to 0.99 (running) for SENS and from 0.92 (walking) to 0.99 (sedentary) for ActiPASS across all activity types. Conclusions: The study shows that two available no-code classification methods can be used to accurately identify basic physical activity types and postures. Our results highlight the accuracy of both methods based on relatively low sampling frequency data. The classification methods showed differences in performance, with lower sensitivity observed in free-living cycling (SENS) and slow treadmill walking (ActiPASS). Both methods use different sets of activity classes with varying definitions, which may explain the observed differences. Our results support the use of the SENS motion system and both no-code classification methods. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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