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Hardware-Based Hopfield Neuromorphic Computing for Fall Detection

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 Int...

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Autores principales: Yu, Zheqi, Zahid, Adnan, Ansari, Shuja, Abbas, Hasan, Abdulghani, Amir M., Heidari, Hadi, Imran, Muhammad A., Abbasi, Qammer H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7766472/
https://www.ncbi.nlm.nih.gov/pubmed/33348587
http://dx.doi.org/10.3390/s20247226
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author Yu, Zheqi
Zahid, Adnan
Ansari, Shuja
Abbas, Hasan
Abdulghani, Amir M.
Heidari, Hadi
Imran, Muhammad A.
Abbasi, Qammer H.
author_facet Yu, Zheqi
Zahid, Adnan
Ansari, Shuja
Abbas, Hasan
Abdulghani, Amir M.
Heidari, Hadi
Imran, Muhammad A.
Abbasi, Qammer H.
author_sort Yu, Zheqi
collection PubMed
description 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.
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spelling pubmed-77664722020-12-28 Hardware-Based Hopfield Neuromorphic Computing for Fall Detection Yu, Zheqi Zahid, Adnan Ansari, Shuja Abbas, Hasan Abdulghani, Amir M. Heidari, Hadi Imran, Muhammad A. Abbasi, Qammer H. Sensors (Basel) Article 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. MDPI 2020-12-17 /pmc/articles/PMC7766472/ /pubmed/33348587 http://dx.doi.org/10.3390/s20247226 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yu, Zheqi
Zahid, Adnan
Ansari, Shuja
Abbas, Hasan
Abdulghani, Amir M.
Heidari, Hadi
Imran, Muhammad A.
Abbasi, Qammer H.
Hardware-Based Hopfield Neuromorphic Computing for Fall Detection
title Hardware-Based Hopfield Neuromorphic Computing for Fall Detection
title_full Hardware-Based Hopfield Neuromorphic Computing for Fall Detection
title_fullStr Hardware-Based Hopfield Neuromorphic Computing for Fall Detection
title_full_unstemmed Hardware-Based Hopfield Neuromorphic Computing for Fall Detection
title_short Hardware-Based Hopfield Neuromorphic Computing for Fall Detection
title_sort hardware-based hopfield neuromorphic computing for fall detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7766472/
https://www.ncbi.nlm.nih.gov/pubmed/33348587
http://dx.doi.org/10.3390/s20247226
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