Yushin Lee1, Yong Min Kim2, Sujin Pyo3, Myung Hwan Yun3. 1. Major Program in Industrial Data Science & Engineering, Department of Industrial and Data Engineering, Pukyong National University, Busan, South Korea. 2. Department of Big Data and AI, Hoseo University, Asan, South Korea. 3. Department of Industrial Engineering, Seoul National University, Seoul, South Korea.
Abstract
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.
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.
Authors: Anna Brzęk; Andrzej Knapik; Bogusław Brzęk; Paweł Niemiec; Piotr Przygodzki; Ryszard Plinta; Karol Szyluk Journal: Biomed Res Int Date: 2022-06-26 Impact factor: 3.246