Distributed recognition of patterns in time series data
Communications of the ACM
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Journal of Ambient Intelligence and Smart Environments
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IEEE Communications Magazine
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GPC'11 Proceedings of the 6th international conference on Grid and Pervasive Computing
Automatic Segmentation and Recognition in Body Sensor Networks Using a Hidden Markov Model
ACM Transactions on Embedded Computing Systems (TECS) - Special Section on CAPA'09, Special Section on WHS'09, and Special Section VCPSS' 09
Capacitive indoor positioning and contact sensing for activity recognition in smart homes
Journal of Ambient Intelligence and Smart Environments
USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Journal of Ambient Intelligence and Smart Environments
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We propose a distributed recognition framework to classify continuous human actions using a low-bandwidth wearable motion sensor network, called distributed sparsity classifier (DSC). The algorithm classifies human actions using a set of training motion sequences as prior examples. It is also capable of rejecting outlying actions that are not in the training categories. The classification is operated in a distributed fashion on individual sensor nodes and a base station computer. We model the distribution of multiple action classes as 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 sparse representation. Fast linear solvers are provided to compute such representation via ℓ 1-minimization. To validate the accuracy of the framework, a public wearable action recognition database is constructed, called wearable action recognition database (WARD). The database is comprised of 20 human subjects in 13 action categories. Using up to five motion sensors in the WARD database, DSC achieves state-of-the-art performance. We further show that the recognition precision only decreases gracefully using smaller subsets of active sensors. It validates the robustness of the distributed recognition framework on an unreliable wireless network. It also demonstrates the ability of DSC to conserve sensor energy for communication while preserve accurate global classification. This work was partially supported by ARO MURI W911NF-06-1-0076, NSF TRUST Center, and the startup funding from the University of Texas and Texas Instruments.