Ningjiang Chen

Wearable accelerometer based extendable activity recognition system

Recognizing the human activities of daily living (ADL) is an important research issue in the pervasive environment. Activity recognition is treated as a classification problem and the multi-class classifier is often used. Though the multi-class classifier can obtain high classification accuracy, it can not detect the noise activities and unknown activities, and the system has no extendable recognition capability. In this paper, we proposed a recognition system which can recognize known activities and detect unknown activities simultaneously. For each known activity, one one-class classification model is built up and the combined one-class classification models are used to judge whether a test sample belongs to known activities. For the known samples, the multi-class classifier is used to recognize their types. For the continuous unknown samples, based on segmentation algorithm, training samples of new activities are extracted and added into the recognition system to extend the systems recognition capability.

LWOS: a localization method without on-body sensor in wireless sensor networks

In many applications of wireless sensor networks (WSNs), the location information of users is very important. In this paper we present a localization method without on-body sensor (LWOS). The basic idea is that when a person is standing between a pair of transceivers, the human body will attenuate the received signal. From the detected attenuation of Received Signal Strength Indication (RSSI), LWOS can detect and localize people directly utilizing the wireless communication in WSNs. No additional sensor is needed and users do not need to wear a sensor node any more. A signal-shielding device is used at the transmitter side to minify the interference of RSSI variability from multi-path effects. Experiment results show a good capability of localizing a single user in an indoor environment.

Multimedia Distance Learning Application on Embedded End Device

Distance Learning application is one of the most important and complicated applications of multimedia technologies. Distance learning end devices mainly consist of PC-like devices and embedded end devices, which target on two different groups of users respectively. This paper presents a multimedia distance learning application on an embedded end device, which is based on the Nexperia PNX1300 processor and characterized by low-cost, easy-to-use and stability. A demo system and several technical issues of this application are addressed. A comparison with other distance-learning solutions is also provided.