Hardware-based Hopfield Neuromorphic computing for fall detection

Yu, Z., Zahid, A., Ansari, S. , Abbas, H. , Abdulghani, A. M., Heidari, H. , Imran, M. A. and Abbasi, Q. H. (2020) Hardware-based Hopfield Neuromorphic computing for fall detection. Sensors, 20(24), 7226. (doi: 10.3390/s20247226)

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Abstract

With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, traditional machine-learning methods for data classification, especially in real time, are computationally intensive. The deployment of Artificial Intelligence algorithms on embedded hardware for fast data classification and accurate fall detection poses a huge challenge in achieving power-efficient embedded systems. Therefore, by exploiting the associative memory feature of Hopfield Neural Network, a hardware module has been designed to simulate the Neural Network algorithm which uses sensor data integration and data classification for recognizing the fall. By adopting the Hebbian learning method for training neural networks, weights of human activity features are obtained and implemented/embedded into the hardware design. Here, the neural network weight of fall activity is achieved through data preprocessing, and then the weight is mapped to the amplification factor setting in the hardware. The designs are checked with validation scenarios, and the experiment is completed with a Hopfield neural network in the analog module. Through simulations, the classification accuracy of the fall data reached 88.9% which compares well with some other results achieved by the software-based machine-learning algorithms, which verify the feasibility of our hardware design. The designed system performs the complex signal calculations of the hardware’s feedback signal, replacing the software-based method. A straightforward circuit design is used to meet the weight setting from the Hopfield neural network, which is maximizing the reusability and flexibility of the circuit design.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Ansari, Dr Shuja and Abbas, Dr Hasan and Abbasi, Dr Qammer and Imran, Professor Muhammad and Yu, Mr Zheqi and Zahid, Mr Adnan and Heidari, Dr Hadi and Abdulghani, Dr Amir Mohamed
Creator Roles:
Yu, Z.Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization
Zahid, A.Writing – review and editing
Ansari, S.Writing – review and editing
Abbas, H.Writing – review and editing
Abdulghani, A. M.Writing – review and editing
Heidari, H.Supervision
Imran, M. A.Supervision
Abbasi, Q. H.Supervision, Project administration, Funding acquisition
Authors: Yu, Z., Zahid, A., Ansari, S., Abbas, H., Abdulghani, A. M., Heidari, H., Imran, M. A., and Abbasi, Q. H.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Sensors
Publisher:MDPI
ISSN:1424-8220
ISSN (Online):1424-8220
Published Online:17 December 2020
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in Sensors 20(24): 7226
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172865EPSRC DTP 16/17 and 17/18Tania GalabovaEngineering and Physical Sciences Research Council (EPSRC)EP/N509668/1Research and Innovation Services