Development of a sitting posture monitoring system for children using pressure sensors: An application of convolutional neural network.
BACKGROUND: Today, sedentary lifestyles are very common for children. Therefore, maintaining a good posture while sitting is very important to prevent musculoskeletal disorders. To maintain a good posture, the formation of good postural habit must be encouraged through posture correction. However, l...
| Publicado en: | Work Vol. 72; no. 1; pp. 351 - 367 |
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| Autores principales: | , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2022
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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=157146086&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157146086 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10519815 3RC jtl: Work issn: 10519815 maglogo: N pubinfo: dt: 2022 vid: 72 iid: 1 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 157146086 156263061 157146086 157146086 10.3233/WOR-213634 157146086 ppf: 351 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Development of a sitting posture monitoring system for children using pressure sensors: An application of convolutional neural network. aug: au: Lee, Yushin Kim, Yong Min Pyo, Sujin Yun, Myung Hwan affil: Major Program in Industrial Data Science & Engineering, Department of Industrial and Data Engineering, Pukyong National University, Busan, South Korea sug: subj: Sitting Posture Classification Monitoring, Physiologic In Infancy and Childhood Pressure Neural Networks (Computer) Human Machine Learning Descriptive Statistics Child Child: 6-12 years ab: BACKGROUND: Today, sedentary lifestyles are very common for children. Therefore, maintaining a good posture while sitting is very important to prevent musculoskeletal disorders. To maintain a good posture, the formation of good postural habit must be encouraged through posture correction. However, long-term observation is required for effective posture correction. Additionally, posture correction is more effective when it is performed in real time. OBJECTIVE: The goal of this study is to classify nine representative sitting postures of children by applying a machine learning technique using pressure distribution data according to the sitting postures. METHODS: In this study, a customized film-type pressure sensor was developed and pressure distribution data from nine sitting postures was collected from seven to twelve year-old children. A convolutional neural network (CNN) was applied to classify the sitting postures and three experiments were conducted to evaluate the performance of the model in three applicable usage scenarios: usage by familiar identifiable users, usage by familiar, but unidentifiable users, and usage by unfamiliar users. RESULTS: The results of our experiments revealed model accuracies of 99.66%, 99.40%, and 77.35%, respectively. When comparing the recall values for each posture, leaning left and leaning right postures had high recall values, but good posture, leaning forward, and crossed-legs postures had low recall values. CONCLUSION: The results of experiments indicated that CNN is an excellent classification method to classify the posture when the pressure distribution data is used as input data. This study is expected to contribute a development of system to aid in observing the natural sitting behavior of children and correcting poor posture in real time. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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