Quang Viet Vo

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Most existing mobile devices nowadays are powered by a limited energy resource. With the tendency using machine learning on mobile devices for activity recognition (AR), recent achievements still remain restrictions including low accuracy and lacking of evidences about power consumption of feature extraction and classification. Moreover, keeping constantly(More)
In this paper, we propose a novel gait authentication mechanism by mining sensor resources on mobile phone. Unlike previous works, both built-in accelerometer and magnetometer are used to handle mobile installation issues, including but not limited to disorientation, and misplacement errors. The authentication performance is improved by executing deep(More)
Fall injury is a health-threatening incident that may cause instant death. There are many research interests aimed to detect fall incidents as early as possible. Fall detection is envisioned critical on ICT-assisted healthcare future. In this paper, we study fall indicators from smartphone perspective. We use smartphone built in accelerometer and(More)
Nowadays, recognizing human activities is an important subject; it is exploited widely and applied to many fields in real-life, especially health care or context aware application. Research achievements are mainly focused on activities of daily living which are useful for suggesting advises to health care applications. Falling event is one of the biggest(More)
Many achievements have been announced with real time running capability for activity recognition (AR) using mobile accelerometer. However, they also have weak points including low accuracies especially in multiple-subject activity recognition and lacking of evidences about power consumption. In this paper, we contribute a novel method for extracting(More)
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(More)
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