Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Determination of Fall Direction Before Impact Using Support Vector Machine

서포트벡터머신을 이용한 충격전 낙상방향 판별

  • Lee, Jung Keun (Department of Mechanical Engineering, Hankyong National University)
  • 이정근 (한경대학교 기계공학과)
  • Received : 2014.11.12
  • Accepted : 2015.01.15
  • Published : 2015.01.31
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Abstract

Fall-related injuries in elderly people are a major health care problem. This paper introduces determination of fall direction before impact using support vector machine (SVM). Once a falling phase is detected, dynamic characteristic parameters measured by the accelerometer and gyroscope and then processed by a Kalman filter are used in the SVM to determine the fall directions, i.e., forward (F), backward (B), rightward (R), and leftward (L). This paper compares the determination sensitivities according to the selected parameters for the SVM (velocities, tilt angles, vs. accelerations) and sensor attachment locations (waist vs. chest) with regards to the binary classification (i.e., F vs. B and R vs. L) and the multi-class classification (i.e., F, B, R, vs. L). Based on the velocity of waist which was superior to other parameters, the SVM in the binary case achieved 100% sensitivities for both F vs. B and R vs. L, while the SVM in the multi-class case achieved the sensitivities of F 93.8%, B 91.3%, R 62.3%, and L 63.6%.

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References

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