A Holistic Approach to Interpreting Human States in Smart Environments Providing High Quality of Life

We formulate a concept of a future smart environment for high quality of life (SEQUAL) that would empower humans to compensate for physical and cognitive disabilities associated with sickness and aging. In SEQUAL the assessment of the state of ‘well-being’ - from behaviors and biological signals - is holistic, meaning that the estimation of individual’s health, emotional condition, activity and wishes, are from the beginning determined in relation to each other and in (individual’s own) context, with superior results compared to when estimated independent from each other, as in common practice. Similarly, the prediction of a person’s future condition, intentions, future needs, and actions/treatment/interventions are determined holistically. SEQUAL includes robots, mobility systems and assistive devices for physical intervention, as well as remote professional caregivers, family and friends, to provide intelligent assistance and support network, aiming for higher quality of life for both patient and caregiver

A Survey on Multi-Resident Activity Recognition in Smart Environments

Human activity recognition (HAR) is a rapidly growing field that utilizes smart devices, sensors, and algorithms to automatically classify and identify the actions of individuals within a given environment. These systems have a wide range of applications, including assisting with caring tasks, increasing security, and improving energy efficiency. However, there are several challenges that must be addressed in order to effectively utilize HAR systems in multi-resident environments. One of the key challenges is accurately associating sensor observations with the identities of the individuals involved, which can be particularly difficult when residents are engaging in complex and collaborative activities. This paper provides a brief overview of the design and implementation of HAR systems, including a summary of the various data collection devices and approaches used for human activity identification. It also reviews previous research on the use of these systems in multi-resident environments and offers conclusions on the current state of the art in the field.Comment: 16 pages, to appear in Evolution of Information, Communication and Computing Systems (EICCS) Book Serie

Automatic visual detection of human behavior: a review from 2000 to 2014

Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.This work is funded by the Portuguese Foundation for Science and Technology (FCT - Fundacao para a Ciencia e a Tecnologia) under research Grant SFRH/BD/84939/2012