ActiveHARNet-OnDevice-Deep-Bayesian-Active-Learning-for-HAR

This repository contains code to our paper, ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity Recognition which was presented at ACM MobiSys 2019 (3rd International Workshop on Embedded and Mobile Deep Learning, EMDL '19).

Abstract

Various health-care applications such as assisted living, fall detection etc., require modeling of user behavior through Human Activity Recognition (HAR). HAR using mobile- and wearable-based deep learning algorithms have been on the rise owing to the advancements in pervasive computing. However, there are two other challenges that need to be addressed: first, the deep learning model should support on-device incremental training (model updation) from real-time incoming data points to learn user behavior over time, while also being resource-friendly; second, a suitable ground truthing technique (like Active Learning) should help establish labels on-the-fly while also selecting only the most informative data points to query from an oracle. Hence, in this paper, we propose ActiveHARNet, a resource-efficient deep ensembled model which supports on-device Incremental Learning and inference, with capabilities to represent model uncertainties through approximations in Bayesian Neural Networks using dropout. This is combined with suitable acquisition functions for active learning. Empirical results on two publicly available wrist-worn HAR and fall detection datasets indicate that ActiveHARNet achieves considerable efficiency boost during inference across different users, with a substantially low number of acquired pool points (at least 60% reduction) during incremental learning on both datasets experimented with various acquisition functions, thus demonstrating deployment and Incremental Learning feasibility.

Link: https://dl.acm.org/doi/10.1145/3325413.3329790, arXiv: https://arxiv.org/abs/1906.00108

Authors

Authors: Gautham Krishna Gudur, Prahalathan Sundaramoorthy, Venkatesh Umaashankar

Citation

@inproceedings{10.1145/3325413.3329790,
  author = {Gudur, Gautham Krishna and Sundaramoorthy, Prahalathan and Umaashankar, Venkatesh},
  title = {ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity Recognition},
  year = {2019},
  booktitle = {The 3rd International Workshop on Deep Learning for Mobile Systems and Applications},
  pages = {7–12},
  numpages = {6},
  series = {EMDL '19}
}

Acknowledgements

Our code structure is inspired from Deep Bayesian Active Learning.