Fast and accurate gait phase detection is essential to achieve effective powered lower-limb
prostheses and exoskeletons. As the versatility but also the complexity of these robotic …

Myoelectric control is filled with potential to significantly change human–robot interaction
due to the ability to non-invasively measure human motion intent. However, current control …

Some recent studies use a convolutional neural network (CNN) or long short-term memory
(LSTM) to extract gait features, but the methods based on the CNN and LSTM have a high …

Correctly identifying gait phases is a prerequisite to achieve a spatial/temporal
characterization of muscular recruitment during walking. Recent approaches have …

An exact classification of different gait phases is essential to enable the control of
exoskeleton robots and detect the intentions of users. We propose a gait phase classification …

As surface electromyogram (sEMG) signals have the ability to detect human movement
intention, they are commonly used to be control inputs. However, gait sub-phase …

Despite the advances in the field of brain computer interfaces (BCI), the use of the sole
electroencephalography (EEG) signal to control walking rehabilitation devices is currently …

Machine-learning techniques are suitably employed for gait-event prediction from only
surface electromyographic (sEMG) signals in control subjects during walking. Nevertheless …

This study aims to propose a novel approach for gender recognition using best feature
subset based on recursive feature elimination (RFE) in normal walking. This study has …

The body electromyography (EMG) signals contain a large amount of information related to
the movement of the human body. Identifying the patients' movement intention from the EMG …