Design and implementation of a convolutional neural network on an edge computing smartphone for human activity recognition

Zebin, Tahmina ORCID:, Scully, Patricia J., Peek, Niels, Casson, Alexander J. and Ozanyan, Krikor B. (2019) Design and implementation of a convolutional neural network on an edge computing smartphone for human activity recognition. IEEE Access, 7. pp. 133509-133520. ISSN 2169-3536

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Edge computing aims to integrate computing into everyday settings, enabling the system to be context-aware and private to the user. With the increasing success and popularity of deep learning methods, there is an increased demand to leverage these techniques in mobile and wearable computing scenarios. In this paper, we present an assessment of a deep human activity recognition system’s memory and execution time requirements, when implemented on a mid-range smartphone class hardware and the memory implications for embedded hardware. This paper presents the design of a convolutional neural network (CNN) in the context of human activity recognition scenario. Here, layers of CNN automate the feature learning and the influence of various hyper-parameters such as the number of filters and filter size on the performance of CNN. The proposed CNN showed increased robustness with better capability of detecting activities with temporal dependence compared to models using statistical machine learning techniques. The model obtained an accuracy of 96.4% in a five-class static and dynamic activity recognition scenario. We calculated the proposed model memory consumption and execution time requirements needed for using it on a mid-range smartphone. Per-channel quantization of weights and per-layer quantization of activation to 8-bits of precision post-training produces classification accuracy within 2% of floating-point networks for dense, convolutional neural network architecture. Almost all the size and execution time reduction in the optimized model was achieved due to weight quantization. We achieved more than four times reduction in model size when optimized to 8-bit, which ensured a feasible model capable of fast on-device inference.

Item Type: Article
Uncontrolled Keywords: deep learning,wearable sensors,edge computing,computer science(all) ,/dk/atira/pure/subjectarea/asjc/1700
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Smart Emerging Technologies
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Depositing User: LivePure Connector
Date Deposited: 20 Sep 2019 14:30
Last Modified: 06 May 2024 00:52
DOI: 10.1109/ACCESS.2019.2941836


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