The Design and Implementation of a Pocket-based Fall Accident Detector on Android Mobile Platforms

• Main Authors: Chih-Sheng Chen, 陳智聖
• Other Authors: Lih-Jen Kau
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/962435

碩士 === 國立臺北科技大學 === 電腦與通訊研究所 === 101 === We propose in this dissertation a portable pocket fall accident detection system on Android-based mobile devices. When a fall accident event is detected, the system will automatically generate a flash light as well as a sound continuously, and the emergency center will be notified so that injuries can get assistance immediately. Unlike the other existing fall accident detectors that have to be worn or fastened on the user''s body in a particular way, we just put the mobile device in the pocket, resulting in a better convenience and feasible for practical usage. With the built-in tri-axial accelerometer and electronic compass in the Android-based mobile device, the information about the user''s activity can be easily retrieved, and then analyzed by the proposed algorithm. When a fall accidents event is detected, the user’s current position acquired by the global positioning system (GPS) will be sent to the rescue center via the 3G communication system so that the user can get medical help immediately. Considering the limited computing resources in a mobile device, we propose in this dissertation an algorithm by using a finite state machine cascaded with a support vector machines for the detection of a fall accident event. Based on the concept of a finite sate machine, the features acquired in the proposed system will be examined in a sequential manner. Once the corresponding feature is verified by the current state, it can proceed to next stage; otherwise, the system will reset to the initial state and waiting for the appearance of another feature sequence. With the proposed approach, the computational burden can be alleviated significantly. Moreover, as we will see in the experiment that the interference caused by putting the device in the pocket can be successfully conquered and a distinguished fall accident detection accuracy up to 96% on the sensitivity and 99.71% on the specificity can be obtained when a set of 400 test actions in eight different kinds of activities are estimated by using the proposed approach which justifies the superiority of the proposed algorithm.