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An Intelligent Body Posture Analysis Model Using Multi-Sensors for Long-Term Physical Rehabilitation

  • Mobile & Wireless Health
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Sensors can be installed on various body parts to provide information for computer diagnosis to identify the current body state. However, as human posture is subject to gravity, the direction of the force on each limb differs. For example, the directions of gravitational force on legs and trunk differ. In addition, each person’s height and structure of limbs differs, hence, the acceleration and rotation resulted from such differences on force and length of the limbs of a person in motion would be different, and be presented by cases of different postures. Thus, how to present body postures through skeleton system equations, and achieve an long-term physical rehabilitation, according to the different limb characteristics of each person, is a challenging research issue. This paper proposes a novel scheme named as “Intelligent Body Posture Analysis Model”, which uses multiple acceleration sensors and gyroscopes to detect body motion patterns. The effectiveness of the proposed scheme is proved by conducting a large number of practical experiments and tests.

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Authors and Affiliations

  1. Department of Engineering Science, National Cheng Kung University, Tainan, 701, Taiwan, Republic of China

    Chin-Feng Lai

  2. Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, 621, Taiwan, Republic of China

    Ren-Hung Hwang

  3. Smart Network System Institute, Institute for Information Industry, Taipei, 105, Taiwan, Republic of China

    Ying-Hsun Lai

Authors
  1. Chin-Feng Lai
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  2. Ren-Hung Hwang
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  3. Ying-Hsun Lai
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Correspondence to Chin-Feng Lai.

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This article is part of the Topical Collection on Mobile & Wireless Health.

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Lai, CF., Hwang, RH. & Lai, YH. An Intelligent Body Posture Analysis Model Using Multi-Sensors for Long-Term Physical Rehabilitation. J Med Syst 41, 71 (2017). https://doi.org/10.1007/s10916-017-0708-5

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