• 한국멀티미디어학회논문지
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• 제17권4호
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• pp.506-513
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• 2014

In recent years, research on user's activity recognition using a smart phone has attracted a lot of attentions. A smart phone has various sensors, such as camera, GPS, accelerometer, audio, etc. In addition, smart phones are carried by many people throughout the day. Therefore, we can collect log data from smart phone sensors. The log data can be used to analyze user activities. This paper proposes an approach to inferring a user's physical activities based on the tri-axis accelerometer of smart phone. We propose recognition method for four activity which is physical activity; sitting, standing, walking, running. We have to convert accelerometer raw data so that we can extract features to categorize activities. This paper introduces a recognition method that is able to high detection accuracy for physical activity modes. Using the method, we developed an application system to recognize the user's physical activity mode in real-time. As a result, we obtained accuracy of over 80%.

• 한국콘텐츠학회논문지
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• 제11권12호
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• pp.103-111
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• 2011

최근 만성질환을 예방하고 건강을 증진시킬 목적으로 신체활동에 대한 중요성이 인식되면서 신체활동 연구가 활발히 진행되고 있다. 본 연구에서는 3축 가속도 동작감지기 x, y, z축에 대한 $cm/s^2$의 가속도 합인 SVM(Signal Vector Magnitude)를 이용하여 신체활동 에너지 소비량 알고리즘을 구현하였다. 기존 실험을 통해 타당도가 입증된 COUNT 방식의 Freedson, Hendelman, Leenders, Yngve 알고리즘에 SVM 방식을 적용하여 구현 하였다. COUNT와 SVM 상관관계 분석을 위하여 총 10명의(성인 남성 5명, 여성 5명, 20 ~ 30 대) 피험자를 대상으로 실험을 진행하였다. 피험자는 트레드밀위에서 3단계 신체활동 (걷기: 3km/h, 빨리 걷기: 5km/h, 러닝: 8km/h)을 1주 간격으로 4주 간 반복 실험을 진행하였다. 실험결과 얻어진 COUNT와 SVM의 간의 상관관계를 분석하여 다양한 신체활동에 따른 맞춤형 에너지 측정 알고리즘을 구현하였다.

• 대한안전경영과학회지
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• 제14권1호
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• pp.87-93
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• 2012

The purpose of this study suggests a personalized algorithm of physical activity energy expenditure prediction through comparison and analysis of individual physical activity. The research for a 3-axial accelerometer sensor has increased the role of physical activity in promoting health and preventing chronic disease has long been established. Estimating algorithm of physical activity energy expenditure was implemented by using a tri-axial accelerometer motion detector of the SVM(Signal Vector Magnitude) of 3-axis(x, y, z). A total of 10 participants(5 males and 5 females aged between 20 and 30 years). The activities protocol consisted of three types on treadmill; participants performed three treadmill activity at three speeds(3, 5, 8 km/h). These activities were repeated four weeks.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2008년도 추계종합학술대회 B
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• pp.265-269
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• 2008

Home health care with compact wearable units sounds to be a convenient solution for the elderly people living independently. This paper presents a method to detect fall from the other activities of daily living and also to classify those activities. This kind of ambulatory monitoring enables them to get an emergency help in the case of the fatal fall event and can provide their general health status by observing the activities being performed in daily life. A tri-axial accelerometer sensor is used to get the acceleration anomalies associated with the user's movements. The three axis acceleration data are transferred to the base station sensor node via an IEEE 802.15.4 compliant zigbee module. The base station sensor node sends the data to base station PC for an offline processing. This work shows the feature set preparation using the principal component analysis (PCA) for the designing of neural network. The work includes the most common activities of daily living (ADL) like Rest, Walk and Run along with the detection of fall events from ADL. The angle from the vertical is found to be the most significant feature parameter for classification of fall while mean, standard deviation and FFT coefficients were used as the feature parameter for classifying the other activities under consideration. The accuracy for detection of fall events is 86%. The overall accuracy for ADL and fall is 94%.