Back to Search
Start Over
Robust Real-Time Embedded EMG Recognition Framework Using Temporal Convolutional Networks on a Multicore IoT Processor
- Publication Year :
- 2020
-
Abstract
- Hand movement classification via surface electromyographic (sEMG) signal is a well-established approach for advanced Human-Computer Interaction. However, sEMG movement recognition has to deal with the long-term reliability of sEMG-based control, limited by the variability affecting the sEMG signal. Embedded solutions are affected by a recognition accuracy drop over time that makes them unsuitable for reliable gesture controller design. In this paper, we present a complete wearable-class embedded system for robust sEMG-based gesture recognition, based on Temporal Convolutional Networks (TCNs). Firstly, we developed a novel TCN topology (TEMPONet), and we tested our solution on a benchmark dataset (Ninapro), achieving 49.6% average accuracy, 7.8%, better than current State-Of-the-Art (SoA). Moreover, we designed an energy-efficient embedded platform based on GAP8, a novel 8-core IoT processor. Using our embedded platform, we collected a second 20-sessions dataset to validate the system on a setup which is representative of the final deployment. We obtain 93.7% average accuracy with the TCN, comparable with a SoA SVM approach (91.1%). Finally, we profiled the performance of the network implemented on GAP8 by using an 8-bit quantization strategy to fit the memory constraint of the processor. We reach a $\mathbf {4\times }$ lower memory footprint (460 kB) with a performance degradation of only 3% accuracy. We detailed the execution on the GAP8 platform, showing that the quantized network executes a single classification in 12.84 ms with a power envelope of 0.9 mJ, making it suitable for a long-lifetime wearable deployment.
- Subjects :
- Adult
Male
multi-layer neural networks
Computer science
Real-time computing
Internet of Things
Biomedical Engineering
Wearable computer
02 engineering and technology
0202 electrical engineering, electronic engineering, information engineering
temporal convolutional networks
Humans
Electrical and Electronic Engineering
Man-Machine Systems
Convolutional neural networks
electromyography
neural network hardware
Multi-core processor
Electromyography
Equipment Design
Gestures
Hand
Signal Processing, Computer-Assisted
Neural Networks, Computer
business.industry
020208 electrical & electronic engineering
Support vector machine
Software deployment
Gesture recognition
Memory footprint
EMG, Embedded Systems, TCN, Deep Learning
business
Gesture
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....67b05d357b86de8e00efcc53cd36fbc2