Abstract-The global population is aging; projections show that by 2050, over 20% of the population will be aged over 64. This will lead to an increase in aging related illness, a decrease in informal support, and ultimately issues with providing care for these individuals. Assistive Smart Homes provide a promising solution to some of these issues. Nevertheless, they currently have issues hindering their adoption. To help address some of these issues, this study introduces a novel approach to implementing assistive Smart Homes. The devised approach is based upon an Intention Recognition mechanism incorporated into an intelligent agent architecture. This approach is detailed and evaluated. Evaluation was performed across three scenarios. Scenario 1 involved a web interface, focusing on testing the Intention Recognition mechanism. Scenarios 2 and 3 involved retrofitting a home with sensors and providing assistance with activities over a period of 3 months. The average accuracy for these three scenarios was 100%, 64.4%, and 83.3%, respectively. Future will extend and further evaluate this approach by implementing advanced sensor-filtering rules and evaluating more complex activities. Index Terms-Intention Recognition, Ambient Assisted Living, Smart Homes, Intelligent Agents, Goal Recognition, Activity Recognition I. INTRODUCTIONhe worldwide population is ageing and is resulting in an uneven demographic composition [1], [2]. This is expected to reach a situation where by 2050 over 20% of the population will be aged over 64 [1], [2]. This growth in the aging population is expected to produce an increase in agerelated illness which, in turn, will place additional burdens on healthcare provision [2]. In addition, the amount of informal support available will decrease due to a reduction in the global Potential Support Ratio (PSR). The PSR is the ratio of people that comprise the working age (15-64) to those older than 64 [1]. The PSR is expected to continue on a downward trend reaching a low of 4:1 by 2050. The PSR was previously 12:1 in 1950 and more recently 9:1 in 2009 [1].Ambient Assisted Living (AAL) has been widely viewed as a promising approach to address some of the problems associated with supporting the ageing population [3], [4].Within the context of AAL, technology-based solutions are used to support independent living and subsequently alleviate a portion of the problems associated with ageing. Such an approach offers the potential of enhancing the quality of life of older people. The notion of Smart Homes (SH), namely residential environments augmented with sensor technology and assistive services have emerged as a dominant realization of the AAL approach.Typically, SHs operate in a 'bottom-up' process, as presented in Fig. 1. In this paradigm, sensors monitor an inhabitant's activities/environment. Data from these sensors are collected and processed to identify Activities of Daily Living (ADL), such as washing or preparing a meal. By monitoring ADLs in this manner, it is possible to detect difficulties in t...

The increase of mental illness cases around the world can be described as an urgent and serious global health threat. Around 500 million people suffer from mental disorders, among which depression, schizophrenia, and dementia are the most prevalent. Revolutionary technological paradigms such as the Internet of Things (IoT) provide us with new capabilities to detect, assess, and care for patients early. This paper comprehensively survey works done at the intersection between IoT and mental health disorders. We evaluate multiple computational platforms, methods and devices, as well as study results and potential open issues for the effective use of IoT systems in mental health. We particularly elaborate on relevant open challenges in the use of existing IoT solutions for mental health care, which can be relevant given the potential impairments in some mental health patients such as data acquisition issues, lack of self-organization of devices and service level agreement, and security, privacy and consent issues, among others. We aim at opening the conversation for future research in this rather emerging area by outlining possible new paths based on the results and conclusions of this work.

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This document specifies an Internet standards track protocol for the Internet community, and requests discussion and suggestions for improvements. Please refer to the current edition of the "Internet Official Protocol Standards" (STD 1) for the standardization state and status of this protocol. Distribution of this memo is unlimited.

Accidental falls can cause serious injury to at risk individuals. This is especially true in the elderly community where falls are the leading cause of hospitalization, injury-related deaths and loss of independence. Detecting and rapidly responding to falls has shown to reduce the long-term impact of and risks associated with falls. A number of real time fall detection solutions exist, however, these have some deficiencies relating to privacy, maintenance, and correct usage. This study introduces a novel fall detection approach that aims to address some of these deficiencies through use of computer vision processes and ceiling mounted thermal vision sensors. A preliminary evaluation has been performed on this process showing promising results, with an accuracy of 68%, however, highlighting a number of issues related to false positives. Future work will improve this approach and provide extended evaluation.