Smart-Object-Based Reasoning System for Indoor Acoustic Profiling of Elderly Inhabitants

Abstract

Many countries are facing significant challenges in relation to providing adequate care for their elderly citizens. The roots of these issues are manifold, but include changing demographics, changing behaviours, and a shortage of resources. As has been witnessed in the health sector and many others in society, technology has much to offer in terms of supporting people’s needs. This paper explores the potential for ambient intelligence to address this challenge by creating a system that is able to passively monitor the home environment, detecting abnormal situations which may indicate that the inhabitant needs help. There are many ways that this might be achieved, but in this paper, we will describe our investigation into an approach involving unobtrusively ’listening’ to sound patterns within the home, which classifies these as either normal daily activities, or abnormal situations. The experimental system we built was composed of an innovative combination of acoustic sensing, artificial intelligence (AI), and the Internet-of-Things (IoT), which we argue in the paper that it provides a cost-effective approach to alerting care providers when an elderly person in their charge needs help. The majority of the innovation in our work concerns the AI in which we employ Machine Learning to classify the sound profiles, analyse the data for abnormal events, and to make decisions for raising alerts with carers. A Neural Network classifier was used to train and identify the sound profiles associated with normal daily routines within a given person’s home, signalling departures from the daily routines that were then used as templates to measure deviations from normality, which were used to make weighted decisions regarding calling for assistance. A practical experimental system was then designed and deployed to evaluate the methods advocated by this research. The methodology involved gathering pre-design and post-design data from both a professionally run residential home and a domestic home. The pre-design data gathered the views on the system design from 11 members of the residential home, using survey questionnaires and focus groups. These data were used to inform the design of the experimental system, which was then deployed in a domestic home setting to gather post-design experimental data. The experimental results revealed that the system was able to detect 84% of abnormal events, and advocated several refinements which would improve the performance of the system. Thus, the research concludes that the system represents an important advancement to the state-of-the-art and, when taken together with the refinements, represents a line of research which has the potential to deliver significant improvements to care provision for the elderly.

The future of care and healthcare provision to communitydwelling disa-bled elderly people in an ageing society

“…This demands urgent innovations capable of breaking through the reluctance of the elderly to accept and use new technologies, both stationary products used at home and wearable devices (wearables) for when on the move [19], and finding workarounds to constraints in the form of additional cost, inconveniences, social constraints, or privacy issues by utilizing existing standard equipment, such as in the analysis of wi-Fi signal equivalents for the movement of people [20] or even smart TV usage [21]. Passive technologies working in the background, such as the analysis of sound patterns in rooms [22], radio frequency identification (rFID) [23], floor vibration detectors [24], thermal and air quality sensors [25,26], are to be preferred to active monitoring, like video surveillance, which are often considered too intrusive [20]. The best results are achieved when several sensing technologies are combined into a single integrated system [23].…”

Acceptability of Remote Monitoring in Assisted Living/Smart Homes in the United Kingdom and Associated Use of Sounds and Vibrations—A Systematic Review

“…In answering RQ2, the searches and screening process resulted in thirteen articles to be critically appraised. Twelve [46][47][48][49][50][51][52][53][54][55][56][57] of the thirteen studies demonstrated medium to high quality, as the twelve studies had information to answer all criteria in the MMAT, with the thirteenth [56] study falling short of answering S2 and the rest of the criteria. The research hypothesis of these twelve studies was clear and relevant, focusing on vibration and/or acoustic sensing technologies in monitoring human activities.…”

“…Those involving sensor development used dummy models, dolls, or human actors for sampling. The only mixed-methods study [49] included a qualitative human focus group study to inform model development using parameters identified. Issues with missing data were not observed for the mixed-method study.…”

“…The twelve articles selected for the review comprised eleven experiments [46][47][48][50][51][52][53][54][55][56][57] and one mixed method [49]. Unlike RQ1, RQ2 resulted in three conference papers [46,52,57] as well as nine published journals [47][48][49][50][51][52][53][54][55][56].…”

“…The twelve articles selected for the review comprised eleven experiments [46][47][48][50][51][52][53][54][55][56][57] and one mixed method [49]. Unlike RQ1, RQ2 resulted in three conference papers [46,52,57] as well as nine published journals [47][48][49][50][51][52][53][54][55][56]. Although all related to applying vibration or acoustic technologies for remote sensing, only two studies [52,53] used both vibrations and sounds simultaneously, with an additional one mentioning that the hybrid use of both technologies should be considered.…”