This study presents a novel driver authentication system utilizing electrocardiogram (ECG) signals collected through dry electrodes embedded in the steering wheel. Traditional biometric authentication methods are sensitive to environmental changes and vulnerable to replication, but this study addresses these issues by leveraging the unique characteristics and forgery resistance of ECG signals. The proposed system is designed using autocorrelation profiles (ACPs) and a convolutional neural network and is optimized for real-time processing even in constrained hardware environments. Additionally, advanced signal processing algorithms were applied to refine the ECG data and minimize noise in driving environments. The system’s performance was evaluated using a public dataset of 154 participants and a real-world dataset of 10 participants, achieving F1-Scores of 96.8% and 96.02%, respectively. Furthermore, an ablation study was conducted to analyze the importance of components such as ACPs, normalization, and filtering. When all components were removed, the F1-Score decreased to 60.1%, demonstrating the critical role of each component. These findings highlight the potential of the proposed system to deliver high accuracy and efficiency not only in vehicle environments but also in various security applications.

This paper presents an interval-based LDA (Linear Discriminant Analysis) algorithm for individual verification using ECG (Electrocardiogram). In this algorithm, at first, unwanted noise and power-line interference are removed from the ECG signal. Then, the autocorrelation profile (ACP) of the ECG signal, which is a mathematical representation of the degree of similarity between a given time series and a lagged version of itself over successive time intervals, is calculated. Finally, the interval-based LDA algorithm is applied to extract unique individual feature vectors that represent distance and angle characteristics on short ACP segments. These feature vectors are used during the processes of enrollment and verification of individual identification. To validate our algorithm, we conducted experiments using the MIT-BIH ECG and achieved EERs (Equal Error Rate) of 0.143%, showing that the proposed algorithm is practically effective and robust in verifying the individual’s identity.

Neuromuscular dysfunction poses a critical health challenge, affecting both patients and caregivers. Current active rehabilitation devices relying on electromyography (EMG) face significant hurdles due to high data acquisition demands, inaccurate decoders, and model degradation from sensor distribution shifts across participants. We address these limitations with a vision language model (VLM) that pseudo-labels motor-intent predictions, enhancing decoding accuracy and reliability under sensor distribution shift across participants. Our proposed pipeline integrates visual information with EMG decoding by employing a parallel processing framework: vision data is processed through the VLM to determine user intent, object and grasp type, which in turn provides pseudo-labels for the EMG decoder’s output. The pseudo-label is then utilized to infer motor-intent, enabling precise external control of assistive devices. In our experimental analysis, our proposed pipeline demonstrated superior performance compared to an EMG-only decoder, achieving 100% decoding accuracy across four upper limb gestures in four participants despite sensor distribution shift across them. Our approach underscores the potential of integrating visual understanding with neural signal analysis to enhance the reliability and effectiveness of active rehabilitation devices, ultimately improving patient outcomes by providing more accurate and consistent assistive technologies. The project page including code can be found: https://github.com/deremustapha/VLM-EMG4Gesture.

Recent advancements in electromyogram (EMG)-based gesture decoding have enabled the development of active rehabilitation devices and enhanced human-machine interaction capabilities. While production-grade EMG sensors offer improved signal-to-noise ratios, their technical complexity necessitate innovative solutions to address inherent limitations. Additionally, EMG-based motor-intent decoders are prone to performance degradation due to factors such as fatigue, electrode shifts, and varying acquisition conditions. To address these challenges, we propose a low-cost EMG sensor grid alongside an advanced decoding strategy named AdaptiveEdge. This adaptive model update strategy integrates offline training with real-time on-device parameter updates, facilitating seamless adaptation to diverse EMG disturbance scenarios. Our comprehensive experiments demonstrated significant accuracy improvements: AdaptiveEdge yielded 10.18% higher accuracy (88.66%) when both offline and on-device update were utilized compared to 78.48% without offline training. Furthermore, AdaptiveEdge not only enhances decoding accuracy but also optimizes memory usage and energy consumption, making it particularly suitable for on-device applications such as neuroprosthetics. These advancements collectively pave the way for more effective and practical EMG-based devices, thereby improving human-machine interaction capabilities. The code associated with this study can be accessed here: https://github.com/deremustapha/AdpativeEdge.