The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Bayesian Onset Measure of sEMG for Fall Prediction

베이지안 기반의 근전도 발화 측정을 이용한 낙상의 예측

  • Seongsik Park (Division of Advanced Engineering, Korea National Open University) ;
  • Keehoon Kim (Department of Mechanical Engineering, POSTECH)
  • Received : 2024.03.26
  • Accepted : 2024.05.07
  • Published : 2024.05.31
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Abstract

Fall detection and prevention technologies play a pivotal role in ensuring the well-being of individuals, particularly those living independently, where falls can result in severe consequences. This paper addresses the challenge of accurate and quick fall detection by proposing a Bayesian probability-based measure applied to surface electromyography (sEMG) signals. The proposed algorithm based on a Bayesian filter that divides the sEMG signal into transient and steady states. The ratio of posterior probabilities, considering the inclusion or exclusion of the transient state, serves as a scale to gauge the dominance of the transient state in the current signal. Experimental results demonstrate that this approach enhances the accuracy and expedites the detection time compared to existing methods. The study suggests broader applications beyond fall detection, anticipating future research in diverse human-robot interface benefiting from the proposed methodology.

Keywords

References

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