Real-time embedded EMG analysis for human-computer interaction

To address these challenges, this thesis proposes a novel approach for detecting wrist-hand movements with low-complexity EMG signal analysis system executes in real-time. A novel biometric system to provide security in EMG control HCI applications is also proposed.<br><br>This thesis first investigates the novel EMG signal analysis approach which allows detection of a wide range of wrist-hand movements with accuracy exceeding 99% without constraining the accurate sensor placement.

This is followed by an investigation of low-complexity real-time EMG signal processing system which maintains state-of-the-art wrist- hand detection performance with 48% lower execution time on an embedded ARM processor and 122% on Arduino ATmega controller. It also investigates the novel biometric systems which allow authentication of a person from different wrist-hand movement EMG signals with accuracy exceeding 99% in real-time on the embedded processor for secured EMG-controlled HCI applications.

Moreover, an optimised approach for detecting wrist-hand movements after person identification from its EMG analysis is presented. This approach shows 3% improvement in wrist-hand movement classification accuracy than the traditional baseline systems in real-time on the embedded processor.