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Deep Learning-Based Obesity Identification System for Young Adults Using Smartphone Inertial Measurements

Author

Listed:
  • Gou-Sung Degbey

    (Division of Computer Science and Engineering, Sunmoon University, Asan 31460, Republic of Korea)

  • Eunmin Hwang

    (William F. Harrah College of Hotel Administration, University of Nevada Las Vegas, Las Vegas, NV 89154, USA)

  • Jinyoung Park

    (College of Nursing, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea)

  • Sungchul Lee

    (Division of Computer Science and Engineering, Sunmoon University, Asan 31460, Republic of Korea)

Abstract

Obesity recognition in adolescents is a growing concern. This study presents a deep learning-based obesity identification framework that integrates smartphone inertial measurements with deep learning models to address this issue. Utilizing data from accelerometers, gyroscopes, and rotation vectors collected via a mobile health application, we analyzed gait patterns for obesity indicators. Our framework employs three deep learning models: convolutional neural networks (CNNs), long-short-term memory network (LSTM), and a hybrid CNN–LSTM model. Trained on data from 138 subjects, including both normal and obese individuals, and tested on an additional 35 subjects, the hybrid model achieved the highest accuracy of 97%, followed by the LSTM model at 96.31% and the CNN model at 95.81%. Despite the promising outcomes, the study has limitations, such as a small sample and the exclusion of individuals with distorted gait. In future work, we aim to develop more generalized models that accommodate a broader range of gait patterns, including those with medical conditions.

Suggested Citation

  • Gou-Sung Degbey & Eunmin Hwang & Jinyoung Park & Sungchul Lee, 2024. "Deep Learning-Based Obesity Identification System for Young Adults Using Smartphone Inertial Measurements," IJERPH, MDPI, vol. 21(9), pages 1-14, September.
    • Handle: RePEc:gam:jijerp:v:21:y:2024:i:9:p:1178-:d:1471275
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