Deep Activity Recognition Models with Triaxial Accelerometers

TL;DR

This paper demonstrates that deep learning models significantly improve human activity recognition accuracy using triaxial accelerometers, leveraging unlabeled data and combining with HMMs for sequential modeling.

Contribution

It introduces deep activity recognition models that outperform traditional methods, utilize unlabeled data, and combines with hidden Markov models for better sequential activity recognition.

Findings

Deep models achieve higher recognition accuracy.

Unsupervised feature extraction from unlabeled data.

Hybrid DL-HMM approach improves sequential recognition.

Abstract

Despite the widespread installation of accelerometers in almost all mobile phones and wearable devices, activity recognition using accelerometers is still immature due to the poor recognition accuracy of existing recognition methods and the scarcity of labeled training data. We consider the problem of human activity recognition using triaxial accelerometers and deep learning paradigms. This paper shows that deep activity recognition models (a) provide better recognition accuracy of human activities, (b) avoid the expensive design of handcrafted features in existing systems, and (c) utilize the massive unlabeled acceleration samples for unsupervised feature extraction. Moreover, a hybrid approach of deep learning and hidden Markov models (DL-HMM) is presented for sequential activity recognition. This hybrid approach integrates the hierarchical representations of deep activity recognition models with the stochastic modeling of temporal sequences in the hidden Markov models. We show substantial recognition improvement on real world datasets over state-of-the-art methods of human activity recognition using triaxial accelerometers.