This is Part II of the two-part comprehensive survey devoted to a computing framework most
commonly known under the names Hyperdimensional Computing and Vector Symbolic …

Tiny machine learning (TinyML) is a new frontier of machine learning. By squeezing deep
learning models into billions of IoT devices and microcontrollers (MCUs), we expand the …

Wearable devices that monitor muscle activity based on surface electromyography could be
of use in the development of hand gesture recognition applications. Such devices typically …

With advances in artificial intelligence (AI)-based algorithms, gesture recognition accuracy
from sEMG signals has continued to increase. Spatiotemporal multichannel-sEMG signals …

Hand paralysis is one of the most common complications in stroke patients, which severely
impacts their daily lives. This article presents a wearable hand rehabilitation system that …

In the last few years, research and development on Deep Learning models & techniques for
ultra-low-power devices–in a word, TinyML–has mainly focused on a train-then-deploy …

Convolutional neural network (CNN) has been widely exploited for simultaneous and
proportional myoelectric control due to its capability of deriving informative, representative …

This paper proposed a wearable smart sEMG recorder integrated gradient boosting decision
tree (GBDT) based hand gesture recognition. A hydrogel-silica gel based flexible surface …

The electromyogram (EMG) signals from an individual's muscles can reflect the
biomechanics of human movement. The accurate classification of individual and combined …

EMG-based motion estimation is required for applications such as myoelectric control,
where the simultaneous estimation of kinematic information, namely joint angle and velocity …