Key result
A light-weight neural network using maximal overlap discrete wavelet transform and smoothing techniques enabled EMG signal classification on edge devices like FPGAs with low power consumption.
Why the study?
Analyzing bio-signal time series directly increases neural network size, creating a need for light-weight neural networks implementable on edge devices for wearable applications.
A light-weight neural network using MODWT and 1D convolution can efficiently classify EMG signals on FPGA edge devices for wearable applications.
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Neural networks may reduce analyst variability in bio-signal analysis; leaves open clinical validation for health, HCI, and authentication uses.
Recently, the application of bio-signals in the fields of health management, human–computer interaction (HCI), and user authentication has increased. This is because of the development of artificial intelligence technology, which can analyze bio-signals in numerous fields. In the case of the analysis of bio-signals, the results tend to vary depending on the analyst, owing to a large amount of noise. However, when a neural network is used, feature extraction is possible, enabling a more accurate analysis. However, if the bio-signal time series is analyzed as is, the total neural network increases in size. In this study, to accomplish a light-weight neural network, a maximal overlap discrete wavelet transform (MODWT) and a smoothing technique are used for better feature extraction. Moreover, the learning efficiency is increased using an augmentation technique. In designing the neural network, a one-dimensional convolution layer is used to ensure that the neural network is simple and light-weight. Consequently, the light-weight attribute can be achieved, and neural networks can be implemented in edge devices such as the field programmable gate array (FPGA), yielding low power consumption, high security, fast response times, and high user convenience for wearable applications. The electromyogram (EMG) signal represents a typical bio-signal in this study.
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Hyun‐Sik Choi (2023) studied Electromyogram (EMG) signal classification. Light-weight neural network with MODWT and smoothing technique on FPGA was evaluated on EMG signal classification. A light-weight neural network using maximal overlap discrete wavelet transform and smoothing techniques enabled EMG signal classification on edge devices like FPGAs with low power consumption.
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