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 …

Smart Homes are generally considered the final solution for living problem, especially for the
health care of the elderly and disabled, power saving, etc. Human activity recognition in …

Abstract Human Activity Recognition (HAR) is a highly promising research area meant to
automatically identify and interpret human behaviour using data received from sensors in …

Human-robot teams collaborating to achieve tasks under various conditions, especially in
unstructured, dynamic environments will require robots to adapt autonomously to a human …

Deep learning architectures have been applied increasingly in multi-modal problems which
has empowered a large number of application domains needing much less human …

In clinical settings, most automatic recognition systems use visual or sensory data to
recognize activities. These systems cannot recognize activities that rely on verbal …

Trauma is a leading cause of mortality worldwide with about 20% of these deaths being
preventable. Most of these preventable deaths result from errors during the initial …

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 …

Emergency medical services (EMS) teams are first responders providing urgent medical
care to severely ill or injured patients in the field. Despite their criticality, EMS work is one of …

Radio-Frequency Identification (RFID) technology has the potential to provide inexpensive,
wireless, battery-free connectivity and interactivity for objects that are traditionally not …