Energy Saving in Forward Fall Detection using Mobile Accelerometer

Energy Saving in Forward Fall Detection using Mobile Accelerometer

Vo Quang Viet (Electronic & Computer Engineering Department, Chonnam National University, Gwangju, Korea), Ali Fahmi Perwira Negera (Electronic & Computer Engineering Department, Chonnam National University, Gwangju, Korea), Hoang Minh Thang (Electronic & Computer Engineering Department, Chonnam National University, Gwangju, Korea) and Deokjai Choi (Electronic & Computer Engineering DepartmentChonnam National University, Gwangju, Korea)
Copyright: © 2013 |Pages: 17
DOI: 10.4018/jdst.2013010106
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Fall injury is one of the biggest risks to health and well-being of the elderly especially in independent living because falling accidents may cause instant death. There are many research interests aimed to detect fall incidents. Fall detection is envisioned critical on ICT-assisted healthcare future. In addition, mobile battery is currently another serious problem in which performance feasibility is considered as a standard to verify an effective method. In this paper, the authors study forward fall detection method from mobile phone perspective using accelerometer only without sacrificing accuracy to save energy. Using peak threshold algorithm in axes of mobile accelerometer, transition from activity of daily living (ADL) to forward fall event is recognized. In collected templates, Dynamic Time Warping (DTW) was applied to compute difference among them with new unlabeled samples. Results implemented on mobile phone easily show the feasibility of the method hence contribute significantly to fall detection in healthcare.
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State Of The Art

The current fall detection methods can be basically categorized as following:

  • Video data: The video-based system captures the images of human movement. A fall event is detected based variations of images (Toreyin, 2006);

  • Acoustic data: Detecting a fall via audio signals analysis;

  • Wearable sensor data: Embedding some micro sensors into clothes, or girdle, shoes, plugged on foot, etc. to monitor the human activities in real-time, and finding the occurrence of a fall based on the changes of some movement parameters (Winters, 2002).

However, these approaches have some weak points. For example, the narrow scope of video system, notably inaccurate of acoustic data, the inconvenience of wearable sensors system, and the limited range of application where they can be deployed at this moment. Accelerometer has been basically used in research (Wiliams, 1998). Since then, many studies have been presented about various methods using heuristic classifier (Bussman, 2001), while others used multiple sensors such as accelerometer and sound data (Charalampos, 2008). The common and simple methodology for fall detection is using threshold algorithm based on decision whether an event crosses predefined thresholds (AlertOne, 2008). Fall detector is also implemented on waist-worn accelerometer fixed on human body (Zhang, 2006) using SVM classifier. Mobile devices have some limitations on computational resources unlike full-fledged processing devices with wearable sensors as detection engine. Directly adopting wearable sensors research to mobile phone is not suitable as they are different in daily usage context (Korpipää, 2008). Proposing a fall detection algorithm that enables application to be lightweight for guaranteeing mobile phone resource’s consumption and also maintaining excellent accuracy is considered as a necessity at the moment.

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