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We propose a distributed recognition framework to classify human actions using a wearable motion sensor network. Each sensor node consists of an integrated triaxial accelerometer and biaxial gyroscope. Given a set of pre-segmented actions as training examples, the algorithm simultaneously segments and classifies human actions from a motion sequence, and it also rejects unknown actions that are not in the training set. The classification is distributedly operated on individual sensor nodes and a base station computer. Due to rapid advances in the integration of mobile processors and heterogeneous sensors, a distributed recognition system likely outperforms traditional centralized recognition methods. In this paper, we assume the distribution of multiple action classes satisfies a mixture subspace model, one subspace for each action class. Given a new test sample, we seek the sparsest linear representation of the sample w.r.t. all training examples. We show that the dominant coefficients in the representation only correspond to the action class of the test sample, and hence its membership is encoded in the representation. We provide fast linear solvers to compute such representation via 1 -minimization.

CareNet is an integrated wireless sensor environment for remote healthcare that uses a two-tier wireless network and an extensible software platform. CareNet provides both highly reliable and privacy-aware patient data collection, transmission and access. This paper describes our system architecture, software development, and the results of our field studies.

In this paper, we present an open-source platform for wireless body sensor networks called DexterNet. The system is motivated by shifting research paradigms to support real-time, persistent human monitoring in both indoor and outdoor environments. The platform utilizes a three-layer architecture to control heterogeneous body sensors. The first layer, called the body sensor layer (BSL), deals with design of different wireless body sensors and their instrumentation on the body. We detail two custom-built body sensors: one measuring body motions and the other measuring the ECG and respiratory patterns. At the second layer, called the personal network layer (PNL), the wireless body sensors on a single subject communicate with a mobile base station, which supports Linux OS and the IEEE 802.15.4 protocol. The BSL and PNL functions are abstracted and implemented as an opensource software library, called Signal Processing In Node Environment (SPINE). A DexterNet network is scalable, and can be reconfigured on-the-fly via SPINE. At the third layer, called the global network layer (GNL), multiple PNLs communicate with a remote Internet server to permanently log the sensor data and support higher-level applications. We demonstrate the versatility of the DexterNet platform via three applications: avatar visualization, human activity recognition, and integration of DexterNet with global positioning sensors and air pollution sensors for asthma studies.