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Emerging wearable interfaces and algorithms for hand gesture recognition: A survey
Hands are vital in a wide range of fundamental daily activities, and neurological diseases
that impede hand function can significantly affect quality of life. Wearable hand gesture …
that impede hand function can significantly affect quality of life. Wearable hand gesture …
Gesture recognition using surface electromyography and deep learning for prostheses hand: state-of-the-art, challenges, and future
W Li, P Shi, H Yu - Frontiers in neuroscience, 2021 - frontiersin.org
Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life,
and limits their performance in activities of daily life. The realization of natural control for …
and limits their performance in activities of daily life. The realization of natural control for …
Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM
Deep learning models are efficient in learning the features that assist in understanding
complex patterns precisely. This study proposed a computerized process of classifying skin …
complex patterns precisely. This study proposed a computerized process of classifying skin …
Active upper limb prostheses: A review on current state and upcoming breakthroughs
The journey of a prosthetic user is characterized by the opportunities and the limitations of a
device that should enable activities of daily living (ADL). In particular, experiencing a bionic …
device that should enable activities of daily living (ADL). In particular, experiencing a bionic …
Deep learning for EMG-based human-machine interaction: A review
Electromyography (EMG) has already been broadly used in human-machine interaction
(HMI) applications. Determining how to decode the information inside EMG signals robustly …
(HMI) applications. Determining how to decode the information inside EMG signals robustly …
Dynamic gesture recognition using surface EMG signals based on multi-stream residual network
Gesture recognition technology is widely used in the flexible and precise control of
manipulators in the assisted medical field. Our MResLSTM algorithm can effectively perform …
manipulators in the assisted medical field. Our MResLSTM algorithm can effectively perform …
Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals
Designing efficient and labor-saving prosthetic hands requires powerful hand gesture
recognition algorithms that can achieve high accuracy with limited complexity and latency. In …
recognition algorithms that can achieve high accuracy with limited complexity and latency. In …
Surface electromyography and artificial intelligence for human activity recognition—a systematic review on methods, emerging trends applications, challenges, and …
Human activity recognition (HAR) has become increasingly popular in recent years due to its
potential to meet the growing needs of various industries. Electromyography (EMG) is …
potential to meet the growing needs of various industries. Electromyography (EMG) is …
Surface electromyography as a natural human–machine interface: a review
Surface electromyography (sEMG) is a non-invasive method of measuring neuromuscular
potentials generated when the brain instructs the body to perform both fine and coarse …
potentials generated when the brain instructs the body to perform both fine and coarse …
Ultragesture: Fine-grained gesture sensing and recognition
With the rising of AR/VR technology and miniaturization of mobile devices, gesture
recognition is becoming increasingly popular in the research area of human-computer …
recognition is becoming increasingly popular in the research area of human-computer …