Smart-object based reasoning system for indoor acoustic profiling of elderly inhabitants

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

Towards Unobtrusive Ambient Sound Monitoring for Smart and Assisted Environments

This paper proposes an implementation of a wireless sensor network as part of the Internet of Things using a network of distributed Raspberry Pi computers transmitting data over a ZigBee radio-mesh. The pervasive nature of noise in the modern environment makes this an ideal factor for monitoring unobtrusively in the Smart Environment. Noise monitoring using distributed Raspberry Pi computers has already been established in several other studies, but rarely in a domestic setting. The goal of this study is to evaluate the potential for using off-the-shelf, low-cost components to develop a wireless sensor network for unobtrusive sound monitoring in a domestic environment, where anomalous readings trigger alerts. The study investigates the transferability of the prototype into assisted living to enable seniors to live independently in their own homes. Assisted ambient living in a smart environment can provide support for both the elderly person and their carers through the provision of relevant data in a clear and easily accessible manner using cross-platform applications. Data can be accessed actively through an interface or passively when alerts are triggered