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Enabling accurate and low-cost classification of a range of motion activities is important for numerous applications, ranging from disease treatment and in-community rehabilitation of patients to athlete training. This paper proposes a novel contextual online learning method for activity classification based on data captured by low-cost, body-worn inertial sensors, and smartphones. The proposed method...
In hyperconnectivity scenario, managing the amount of data acquired from sensors in the Body Area Networks (BANs) is one of the major issues. In this paper we propose an on-chip context predictor based sparse sensing technology with smart transmission architecture which makes use of confidence interval calculation from the features that present in the data, thereby achieving statistical guarantee...
Cloud computing is still surfing the hype wave of emerging technologies. Delivering hardware, software and resources as a service over the network, the paradigm is also attracting a good deal of excitement in the mobile computing space. In this work, we challenge the proclaimed benefits of mobile cloud computing using smartphones as thin clients to access remote running applications, especially for...
In this paper, a new traffic monitoring technique is introduced which works based on the emitted RF noise from the vehicles. In comparison with the current traffic sensing systems, our light-weight technique has simpler structure in both terms of hardware and software. An antenna installed to the roadside receives the signal generated during electrical activity of the vehicles' sub-systems. This signal...
With the challenge of healthcare for the increasing number of elderly people and the prevalence of chronic disease, research has been carried out on the development of assistive technologies and devices. This paper proposes a framework of context-aware physiological analysis for remote and efficient healthcare. With the relationship between the physiological function and daily activities, the online...
We propose DiaWear, a novel assistive mobile phone-based calorie monitoring system to improve the quality of life of diabetes patients and individuals with unique nutrition management needs. Our goal is to achieve improved daily semi-automatic food recognition using a mobile wearable cell phone. DiaWear currently uses a neural network classification scheme to identify food items from a captured image...
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