Video-based activity level recognition for assisted living using motion features

Abstract

Activities of daily living of the elderly is often monitored using passive sensor networks. With the reduction of camera prices, there is a growing interest of video-based approaches to provide a smart, safe and independent living environment for the elderly. In this paper, activity level in context of tracking the movement pattern of an individual as a metric to monitor the daily living of the elderly is explored. Activity levels can be an effective indicator that would denote the amount of busyness of an individual by modelling motion features over time. The novel framework uses two different variants of the motion features captured from two camera angles and classifies them into different activity levels using neural networks. A new dataset for assisted living research called the Sheffield Activities of Daily Living (SADL) dataset is used where each activity is simulated by 6 subjects and is captured under two different illumination conditions within a simulated assisted living environment. The experiments show that the overall detection rate using a single camera setup and a dual camera setup is above 80%.

Ambient assisted living and European funds: a bibliometric approach

“…Seminal work presented in Fleck and Straßer (2008), Cardinaux et al (2011) designed an automated 24/7 video surveillance and video cameras to ensure the safety of the elderly. The reduction of camera prices increased interest in video-based approaches that provide a smart, safe and independent living environment for the elderly (Pal and Abhayaratne, 2015).…”

Activity Recognition from Video Data Using Spatial and Temporal Features

“…In particular, time of the day, day of the week, and seasons were used to improve the accuracy [15]. Neural network and images from two orthogonal cameras were used to identify human activities based on two feature vectors [16].…”

Handcrafted Localized Phase Features for Human Action Recognition

“…Human action recognition (HAR) is an important topic in computer vision due to its applications in assisted living, smart surveillance systems, human-computer interaction, computer gaming and affective computing [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Depending on the target application, an action recognition system can be used to either recognize full body behavior [1], or to recognize partial body like gesture recognition [17] and facial recognition [18].…”