Hardware-Based Hopfield Neuromorphic Computing for Fall Detection
| Autor: | Qammer H. Abbasi, Muhammad Imran, Adnan Zahid, Hasan T. Abbas, Zheqi Yu, Hadi Heidari, Amir M. Abdulghani, Shuja Ansari |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
neuro-inspired model
Computer science Data classification 02 engineering and technology lcsh:Chemical technology 01 natural sciences Biochemistry Article Analytical Chemistry Software 0202 electrical engineering electronic engineering information engineering Humans lcsh:TP1-1185 Electrical and Electronic Engineering signal processing Instrumentation Signal processing Artificial neural network business.industry Computers 020208 electrical & electronic engineering 010401 analytical chemistry Content-addressable memory neural networks artificial intelligence Atomic and Molecular Physics and Optics 0104 chemical sciences Hebbian theory Neuromorphic engineering Accidental Falls Data pre-processing Neural Networks Computer business Computer hardware Algorithms |
| Zdroj: | Sensors Volume 20 Issue 24 Sensors (Basel, Switzerland) Sensors, Vol 20, Iss 7226, p 7226 (2020) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s20247226 |
| Popis: | 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&rsquo 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. |
| Databáze: | OpenAIRE |
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