Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People

This paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context. The contribution also aims to analyse the usage of the models for the transfer of knowledge when different sensors with different settings contribute to the noise levels. Finally, based on the authors’ experience, a framework proposal for creating valuable and aggregated knowledge is depicted.This research was partially funded by Fundación Tecnalia Research & Innovation, and J.O.-M. also wants to recognise the support obtained from the EU RFCS program through project number 793505 ‘4.0 Lean system integrating workers and processes (WISEST)’ and from the grant PRX18/00036 given by the Spanish Secretaría de Estado de Universidades, Investigación, Desarrollo e Innovación del Ministerio de Ciencia, Innovación y Universidades

Gerontechnology acceptance of smart homes: A systematic review and meta-analysis

Advances in preventive medicine and technology have beneficially affected longevity in the past decades. Unfortunately, longer life expectancy and declining fertility are likely to trigger an increasingly aging population, posing new challenges for social systems. Since aging populations affect the healthcare industry, providing convenient solutions and user-friendly elderly healthcare services is necessary to curb the growing demand by older adults. Several studies have proposed intelligent homes as potential solutions to support old age. However, such solutions raise the question of whether or not elderly persons intend to use smart homes and benefit from them. This paper examines the gerontechnology acceptance of intelligent homes by systematically reviewing previous studies on older people\u27s intention to use innovative home technology. The review was conducted from the Web of Science, Google Scholar, and Scopus, retrieving a thousand articles. Out of these, 40 are selected for the meta-analysis and systematic review. The integrative results showed an increasing intention of older adults to use smart home technology as they believe those innovative ways may improve independent living. However, attributes and drivers like privacy and perceived security show increasing heterogeneity and should draw more attention to prospective researchers

Machine learning techniques for sensor-based household activity recognition and forecasting

Thanks to the recent development of cheap and unobtrusive smart-home sensors, ambient assisted living tools promise to offer innovative solutions to support the users in carrying out their everyday activities in a smoother and more sustainable way. To be effective, these solutions need to constantly monitor and forecast the activities of daily living carried out by the inhabitants. The Machine Learning field has seen significant advancements in the development of new techniques, especially regarding deep learning algorithms. Such techniques can be successfully applied to household activity signal data to benefit the user in several applications. This thesis therefore aims to produce a contribution that artificial intelligence can make in the field of activity recognition and energy consumption. The effective recognition of common actions or the use of high-consumption appliances would lead to user profiling, thus enabling the optimisation of energy consumption in favour of the user himself or the energy community in general. Avoiding wasting electricity and optimising its consumption is one of the main objectives of the community. This work is therefore intended as a forerunner for future studies that will allow, through the results in this thesis, the creation of increasingly intelligent systems capable of making the best use of the user's resources for everyday life actions. Namely, this thesis focuses on signals from sensors installed in a house: data from position sensors, door sensors, smartphones or smart meters, and investigates the use of advanced machine learning algorithms to recognize and forecast inhabitant activities, including the use of appliances and the power consumption. The thesis is structured into four main chapters, each of which represents a contribution regarding Machine Learning or Deep Learning techniques for addressing challenges related to the aforementioned data from different sources. The first contribution highlights the importance of exploiting dimensionality reduction techniques that can simplify a Machine Learning model and increase its efficiency by identifying and retaining only the most informative and predictive features for activity recognition. In more detail, it is presented an extensive experimental study involving several feature selection algorithms and multiple Human Activity Recognition benchmarks containing mobile sensor data. In the second contribution, we propose a machine learning approach to forecast future energy consumption considering not only past consumption data, but also context data such as inhabitants’ actions and activities, use of household appliances, interaction with furniture and doors, and environmental data. We performed an experimental evaluation with real-world data acquired in an instrumented environment from a large user group. Finally, the last two contributions address the Non-Intrusive-Load-Monitoring problem. In one case, the aim is to identify the operating state (on/off) and the precise energy consumption of individual electrical loads, considering only the aggregate consumption of these loads as input. We use a Deep Learning method to disaggregate the low-frequency energy signal generated directly by the new generation smart meters being deployed in Italy, without the need for additional specific hardware. In the other case, driven by the need to build intelligent non-intrusive algorithms for disaggregating electrical signals, the work aims to recognize which appliance is activated by analyzing energy measurements and classifying appliances through Machine Learning techniques. Namely, we present a new way of approaching the problem by unifying Single Label (single active appliance recognition) and Multi Label (multiple active appliance recognition) learning paradigms. This combined approach, supplemented with an event detector, which suggests the instants of activation, would allow the development of an end-to-end NILM approach

Co-designing design fictions:a new approach for debating and priming future healthcare technologies and services

Background Design fictions (DFs) are emerging as a tool aimed at engaging people in debating and questioning the direction of future technologies, services and possible societies. Following the challenges placed on healthcare provision by an ageing population, governments are introducing policies related to ageing that will shape future healthcare services. The exploratory ProtoPolicy project, investigated how co-created DFs might be used to help older citizens imagine the future implications of policy initiatives through the lens of technology in an ageing society. Methods A co-design research approach was employed. In collaboration with older citizens (n=21, 65-94 years old) the project team co-created two DFs based on citizen responses to government policy, which explored the issues of assisted living/smart-homes and assisted dying/ euthanasia in the UK. Feedback on the DFs was sought from citizens at a co-design workshop. Results Five themes emerged from the thematic analysis of the workshop engagements with citizens: increasing the plausibility and acceptance of future healthcare technologies and services, raising ethical concerns and questions, conceptualising new healthcare producs and services, helping with understanding and decision-making, and service/technology requirements capture. Conclusions Understanding and engaging with more complex social healthcare technologies through a co-design design fiction approach might provide added value for citizens in priming new technology introduction in healthcare services. Co-designing design fictions can also provide researchers with more in-depth insights about the preferable futures articulated by different groups within the context of technology and healthcare services

Comparison of Collaborative and Cooperative Schemes in Sensor Networks for Non-Invasive Monitoring of People at Home

This paper looks at wireless sensor networks (WSNs) in healthcare, where they can monitor patients remotely. WSNs are considered one of the most promising technologies due to their flexibility and autonomy in communication. However, routing protocols in WSNs must be energy-efficient, with a minimal quality of service, so as not to compromise patient care. The main objective of this work is to compare two work schemes in the routing protocol algorithm in WSNs (cooperative and collaborative) in a home environment for monitoring the conditions of the elderly. The study aims to optimize the performance of the algorithm and the ease of use for people while analyzing the impact of the sensor network on the analysis of vital signs daily using medical equipment. We found relationships between vital sign metrics that have a more significant impact in the presence of a monitoring system. Finally, we conduct a performance analysis of both schemes proposed for the home tracking application and study their usability from the user’s point of view

Anticipating plausible futures for innovative experimental ecosystems using foresight approach. Case: Design Factory

Change-makers are visionaries who wish to bring change in their respective fields. Design Factory at Aalto University, as an innovative experimental ecosystem with inter-disciplinary principles and new teaching methodologies has been successful and at the forefront in educating the students to be change-makers. The students learn skills, knowledge and are provided with a safe environment that guides them to become a change-maker in their respective fields such as social organizations, entrepreneurship, and careers in start-up or industry. Educating the students to be change-makers will evolve with future; the aim of the study is to holistically anticipate plausible futures for innovative experimental ecosystems utilizing foresight approach. The focus of the study is on Design Factory ways of working, spaces, and teaching methods which will support students in learning by year 20x6{x = 2, 3}. This study is about drawing virtual lines that connect the trends, future drivers, visions, and scenarios, using a contemporary approach that fuses qualitative and quantitative methods. The research on future trends and drivers were performed through semi structured interviews and environmental scanning. The drivers are evaluated through an online survey based on principles of the Delphi method. Further, the drivers are used to build mini scenarios which are further evaluated with the Design Factory stakeholders through a workshop. The results from the study are six future scenarios for the Aalto Design Factory. These results are expected to further foster or trigger new research and development experiments, directions on building radical environments, new teaching methods and ways of working

Internet of Things Adoption for Saudi Healthcare Services

Background: Recent studies in information systems have predicted that applications of the Internet of Things (IoT) innovations will revolutionise various sectors including healthcare. Besides the issues and opportunities of IoT based innovations, existing studies have shown limitations to advance the adoption of IoT-understanding and relevant interventions to benefit researchers and healthcare practitioners. Method: In this context, a systematic literature review study was conducted to re-position a qualitative, phenomenological investigation that could offer useful insights into the factors affecting IoT-adoption in a developing country’s healthcare service. In addition to it, five participants who worked in hospitals and clinics in Jazan, Saudi Arabia, took part in the semi-structured interviews developed based on the diffusion of innovation theory. Results: The study explored the relevant literature and evaluated how the outcome is used to identify the key delivers of IoT in healthcare. Conclusions: According to the findings, the capacity of the Saudi healthcare sector to accept and implement a new IT with IoT technologies is increasing and its integrations remains a debated issue

Pathways for the Development of Future Intelligent Distribution Grids

The next decade will bring several technical and organisational challenges to the electrical distribution grids, which are becoming an important pillar of the energy transition. Distribution system operators will play a crucial role and thus need to find innovative solutions that will prepare them for these changes. Allowing the variations in the size, organisation and technical characteristics of distribution grids, this paper presents the pathways for the distribution system operators developed within the scope of the UNITED-GRID project. These were developed in close cooperation with distribution grids and demonstration sites in the Netherlands, France and Sweden. Investment decision tools based on future scenarios and future-readiness assessment form the first step to steer the distribution system operators towards the necessary technical and digital innovations that increase the observability and controllability of the grid. Secondly, the guidelines present new types of business models that can be integrated into the operators’ portfolios. Thirdly, a workshop methodology is proposed to define the new internal requirements that make distribution system operators more agile to face the fast impacts of the energy transition. Case studies from the demonstration sites are used as examples in the paper