MFCC CNN-LSTM model on TENG-based sensor glove data achieves 93.33% accuracy across 11 sign classes, outperforming random forest by 23 percentage points.
Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition
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MicroBi-ConvLSTM is a convolutional-recurrent model with 11.4K parameters that delivers competitive accuracy on eight HAR benchmarks and full INT8 deployment coverage on Raspberry Pi Pico 2 and ESP32.
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Development of ML model for triboelectric nanogenerator based sign language detection system
MFCC CNN-LSTM model on TENG-based sensor glove data achieves 93.33% accuracy across 11 sign classes, outperforming random forest by 23 percentage points.
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MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices
MicroBi-ConvLSTM is a convolutional-recurrent model with 11.4K parameters that delivers competitive accuracy on eight HAR benchmarks and full INT8 deployment coverage on Raspberry Pi Pico 2 and ESP32.