KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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A Study on Meal Time Estimation and Eating Behavior Recognition Considering Movement Using Wrist-Worn Accelerometer with Its Frequency

손목 움직임과 동작 빈도를 고려한 손목형 가속도계의 식사 행위 및 식사 시간 추론 기법

  • Received : 2016.07.01
  • Accepted : 2016.09.07
  • Published : 2017.01.31
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Abstract

In this paper, we propose a method for recognizing eating behavior with almost no motion acceleration. First, by using the acceleration of gravity acting on the wrist direction, we calculate the angle between the gravity and the wrist direction. After that, detect wrist reciprocating motion when peak and vally exist in specific angle band. And then, when accumulate the number of wrist reciprocating motion occurrences are up to 10, then regard as the meal time 5 minutes before the detection time. Also, estimate the meal time only if its duration is more than 7 minutes. Using the data of 2128 minutes, which was collected from four graduate student, the result of the meal time estimation shows 95.63% accuracy.

본 논문에서는 손목형 가속도계를 이용하여 운동 가속도가 거의 없는 식사 행동을 인식하기 위한 방법을 제안한다. 먼저 손목 방향에 작용하는 중력 가속도를 이용하여 중력 방향과 손목 방향 간의 각도를 구하고, 특정 각도 영역에서 첨두값과 첨미값이 존재하는 경우 손목이 왕복하는 동작을 검출한다. 손목 왕복 동작 발생 횟수를 누적하여 그 횟수가 10회 이상일 경우 검출 시점으로 5분 전까지 식사 시간으로 간주하며, 그 지속시간이 7분 이상인 경우에만 식사 시간으로 추론한다. 대학원생 4명으로부터 수집한 2128분 데이터를 통해 식사 시간을 추론한 결과 95.63%의 정확도를 보인다.

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