This paper presents a framework based on multilayer bi-LSTM network (bidirectional Long
Short-Term Memory) for multimodal sensor fusion to sense and classify daily activities' …

Activity detection and classification using different sensor modalities have emerged as
revolutionary technology for real-time and autonomous monitoring in behaviour analysis …

Abstract Human Activity Recognition (HAR) has become a crucial element for smart
healthcare applications due to the fast adoption of wearable sensors and mobile …

Recognition of human movements with radar for ambient activity monitoring is a developed
area of research that yet presents outstanding challenges to address. In real environments …

Along with the advancement of several emerging computing paradigms and technologies,
such as cloud computing, mobile computing, artificial intelligence, and big data, Internet of …

Research in the recognition of human activities of daily living has significantly improved
using deep learning techniques. Traditional human activity recognition techniques often use …

With the emerging interest in the ubiquitous sensing field, it has become possible to build
assistive technologies for persons during their daily life activities to provide personalized …

Deep learning (DL) methods receive increasing attention within the field of human activity
recognition (HAR) due to their success in other machine learning domains. Nonetheless, a …

In this paper, we perform a systematic study about the on-body sensor positioning and data
acquisition details for Human Activity Recognition (HAR) systems. We build a testbed that …

Multimodal sensors in healthcare applications have been increasingly researched because
it facilitates automatic and comprehensive monitoring of human behaviors, high-intensity …