Handwritten documents recognition is a challenging task in the field of pattern recognition. It has an array of applications wherein recognition of words, alphabets, digits and other characters are ...

Skeletal graphs can represent concise and reliable features for Human-activity recognition in recent years. However they have to be acquired by Kinect sensors or regular cameras, which rely on ...

Aiming at realizing on-chip real-time human activity recognition based on the miniaturized and integrated radar with limited computational resources, hyperdimensional computing (HDC) is a promising ...

Gesture recognition is the most intuitive form of human computer-interface. Gesture sensing can replace interfaces such as touch and clicks needed for interacting with a device. Gesture recognition in ...

Wearable sensor based Human Activity Recognition (HAR) has been widely used these years. This paper proposed a novel deep learning model for HAR using inertial sensors. First, a wearable device ...

In EMG based pattern recognition (EMG-PR), deep learning-based techniques have become more prominent for their self-regulating capability to extract discriminant features from large data-sets.

The proposed method first converts the level sets corresponding to the micro-Doppler signature into contour point clouds using the MATLAB embedded contour () function. Secondly, the point cloud and ...

In this research, we propose a hybrid CNN and LSTM model for word-level Ethiopian sign language recognition. The recognition system has four major components: preprocessing, feature extraction, ...