Related papers: sEMG-based Gesture-Free Hand Intention Recognition…
Electromyography is an unexplored field of study when it comes to alternate input modality while interacting with a computer. However, to make computers understand human emotions is pivotal in the area of human-computer interaction and in…
The anthropomorphism of grasping process significantly benefits the experience and grasping efficiency of prosthetic hand wearers. Currently, prosthetic hands controlled by signals such as brain-computer interfaces (BCI) and…
Artificial intelligence (AI) has made significant advances in recent years and opened up new possibilities in exploring applications in various fields such as biomedical, robotics, education, industry, etc. Among these fields, human hand…
The discrimination of human gestures using wearable solutions is extremely important as a supporting technique for assisted living, healthcare of the elderly and neurorehabilitation. This paper presents a mobile electromyography (EMG)…
Conventional electromyography (EMG) measures the continuous neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. Mechanomyography (MMG) and accelerometers only measure body surface motion,…
Intent inferral on a hand orthosis for stroke patients is challenging due to the difficulty of data collection. Additionally, EMG signals exhibit significant variations across different conditions, sessions, and subjects, making it hard for…
Human intention detection with hand motion prediction is critical to drive the upper-extremity assistive robots in neurorehabilitation applications. However, the traditional methods relying on physiological signal measurement are…
We explore surface electromyography (sEMG) as a non-invasive input modality for mapping muscle activity to keyboard inputs, targeting immersive typing in next-generation human-computer interaction (HCI). This is especially relevant for…
Gesture recognition based on surface electromyographic signal (sEMG) is one of the most used methods. The traditional manual feature extraction can only extract some low-level signal features, this causes poor classifier performance and low…
Surface electromyography (sEMG) is a widely used muscle activity monitoring technique. sEMG measures muscle activity through monopolar and bipolar, multi-electrode electrodes. The surface electrodes are placed on the surface of the skin…
Surface Electromyography (sEMG) is a non-invasive signal that is used in the recognition of hand movement patterns, the diagnosis of diseases, and the robust control of prostheses. Despite the remarkable success of recent end-to-end Deep…
Electroencephalography (EEG) signal based intent recognition has recently attracted much attention in both academia and industries, due to helping the elderly or motor-disabled people controlling smart devices to communicate with outer…
Surface Electromyography (sEMG/EMG) is to record muscles' electrical activity from a restricted area of the skin by using electrodes. The sEMG-based gesture recognition is extremely sensitive of inter-session and inter-subject variances. We…
EMG-based hand gesture recognition uses electromyographic~(EMG) signals to interpret and classify hand movements by analyzing electrical activity generated by muscle contractions. It has wide applications in prosthesis control,…
sEMG pattern recognition algorithms have been explored extensively in decoding movement intent, yet are known to be vulnerable to changing recording conditions, exhibiting significant drops in performance across subjects, and even across…
EMG-based gesture recognition shows promise for human-machine interaction. Systems are often afflicted by signal and electrode variability which degrades performance over time. We present an end-to-end system combating this variability…
In this paper, a different approach on the use of the ADS1299 (an analog front-end with features for electroencephalogram and electrocardiography signal acquisition) is considered, proposing the development of a surface electromyography…
Electromyography (EMG) signals are obtained from muscle cell activity. The recording and analysis of EMG signals has several applications. The EMG is of diagnostic importance for treating patients suffering from neurological and…
Upper limb and hand functionality is critical to many activities of daily living and the amputation of one can lead to significant functionality loss for individuals. From this perspective, advanced prosthetic hands of the future are…
Electromyography (EMG) signal analysis is a popular method for controlling prosthetic and gesture control equipment. For portable systems, such as prosthetic limbs, real-time low-power operation on embedded processors is critical, but to…