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Despite the widespread installation of accelerometers in almost all mobilephones and wearable devices, activity recognition using accelerometers is stillimmature due to the poor recognition accuracy of existing recognition methodsand the scarcity of labeled training data. We consider the problem of humanactivity recognition using triaxial accelerometers and deep learning paradigms.This paper shows that deep activity recognition models (a) provide betterrecognition accuracy of human activities, (b) avoid the expensive design ofhandcrafted features in existing systems, and (c) utilize the massive unlabeledacceleration samples for unsupervised feature extraction. Moreover, a hybridapproach of deep learning and hidden Markov models (DL-HMM) is presented forsequential activity recognition. This hybrid approach integrates thehierarchical representations of deep activity recognition models with thestochastic modeling of temporal sequences in the hidden Markov models. We showsubstantial recognition improvement on real world datasets overstate-of-the-art methods of human activity recognition using triaxialaccelerometers.

Deep Activity Recognition Models with Triaxial Accelerometers