Activity detection and classification are very important for autonomous monitoring of humans
for applications, including assistive living, rehabilitation, and surveillance. Wearable sensors …

Data preprocessing, feature selection and classification algorithms usually occupy the bulk
of surveys on human activity recognition (HAR). This paper instead gives a brief review on …

In this paper, we propose a multimodal multi-stream deep learning framework to tackle the
egocentric activity recognition problem, using both the video and sensor data. First, we …

A smartphone can be utilized as a cost-effective device for the purposes of intelligent
transportation system. To detect the movement and the stationary statuses in the motorized …

With the increasing availability of wearable devices, research on egocentric activity
recognition has received much attention recently. In this paper, we build a Multimodal …

We introduce a novel, efficient methodology for the automatic recognition of major vertical
displacements in human activities. It is based exclusively on barometric pressure measured …

We investigate the possibility of using a combination of a smartphone and a smartwatch,
carried by a shopper, to get insights into the shopper's behavior inside a retail store. The …

Smartphones have ubiquitously integrated into our home and work environments. It is now a
common practice for people to store their sensitive and confidential information on their …

Recent emergence of small, lightweight, batteryless (passive), and therefore maintenance
free, wearable computing platforms such as sensor enabled RFID (Radio Frequency …

This paper addresses approaches to Human Activity Recognition (HAR) with the aim of
monitoring the physical activity of people in the workplace, by means of a smartphone …