The vast proliferation of sensor devices and Internet of Things enables the applications of
sensor-based activity recognition. However, there exist substantial challenges that could …

Sensor-based activity recognition seeks the profound high-level knowledge about human
activities from multitudes of low-level sensor readings. Conventional pattern recognition …

We present a system for activity recognition from passive RFID data using a deep
convolutional neural network. We directly feed the RFID data into a deep convolutional …

With the increasing popularity of wearable sensors, deep-learning-based human activity
recognition (HAR) has attracted great interest from both academic and industrial fields in …

High-performance computing systems are moving towards 2.5 D and 3D memory
hierarchies, based on High Bandwidth Memory (HBM) and Hybrid Memory Cube (HMC) to …

For the effective application of thriving human-assistive technologies in healthcare services
and human–robot collaborative tasks, computing devices must be aware of human …

Sensor-based human activity recognition (HAR) requires to predict the action of a person
based on sensor-generated time series data. HAR has attracted major interest in the past …

Artificial Intelligence of Things (AIoT) is an emerging frontier based on the deep fusion of
Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The fundamental goal of …

With the massive use of computers, the growth and explosion of data has greatly promoted
the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such …

We introduce a system that recognizes concurrent activities from real-world data captured by
multiple sensors of different types. The recognition is achieved in two steps. First, we extract …