Big data, smart homes and ambient assisted living

Definition of Guideline-Based Metrics to Evaluate AAL Ecosystem’s Usability

The elderly population growth has posed several challenges which the current healthcare systems are incapable of handling. In the past few years, there has been a close collaboration between both the scientific and industry communities to provide feasible solutions capable of addressing the growing demands from people with special needs, namely, in terms of assistance and improvement of their overall life quality, which promoted to the development of the ambient assisted living (AAL). Despite the general consensus regarding its positive impact in the user’s daily life, several challenges compromise their overall adoption. As a consequence, the research undertaken so far focused over the mitigation of technical-related limitations, overshadowing user-related limitations, namely, the ecosystem’s usability. This article presents a parametrization of the literature guidelines, which provides the end-users a consistent and accurate way of using the heuristic methodology to assert the interface’s usability without relying in external entities with specialized know-how.

The Convergence and Mainstreaming of Integrated Home Technologies for People with Disability

If human rights begin in small places close to home, technologies that enable people with disability to access and control their home environments are an important human rights instrument. Smart homes exemplify recent advances in design, building construction, and integration of technologies within the built environment. They draw on multiple social and technical disciplines that share a broad vision but lack a common language, creating ambiguity and limiting the usefulness of the evidence base in determining optimal ways to integrate technologies and housing design to meet diverse needs. The convergence of mainstream and assistive technologies offers the potential of accessible and affordable strategies for inclusion, but also risks further exclusion of marginalized sections of the population. Coordination of efforts might accelerate translation of knowledge and diffusion of innovations into the practices of planning, designing, building, and sustaining housing that promotes independent living. This conceptual paper reviews the theoretical frameworks and terminology from fields of research involved in the design and use of technologies in the home environment to enable people with disability and older people. It considers approaches to design and interventions that could inform policies and practices as well as further research and development activities.

AI-Based Early Change Detection in Smart Living Environments

In the smart environments we live today, a great variety of heterogeneous sensors are being increasingly deployed with the aim of providing more and more value-added services. This huge availability of sensor data, together with emerging Artificial Intelligence (AI) methods for Big Data analytics, can yield a wide array of actionable insights to help older adults continue to live independently with minimal support of caregivers. In this regard, there is a growing demand for technological solutions able to monitor human activities and vital signs in order to early detect abnormal conditions, avoiding the caregivers’ daily check of the care recipient. The aim of this study is to compare state-of-the-art machine and deep learning techniques suitable for detecting early changes in human behavior. At this purpose, specific synthetic data are generated, including activities of daily living, home locations in which such activities take place, and vital signs. The achieved results demonstrate the superiority of unsupervised deep-learning techniques over traditional supervised/semi-supervised ones in terms of detection accuracy and lead-time of prediction.

Evaluating the Use of Smart Home Technology by People With Brain Impairment: Protocol for a Single-Case Experimental Design

Background

Smart home technologies are emerging as a useful component of support delivery for people with brain impairment. To promote their successful uptake and sustained use, focus on technology support services, including training, is required.

Objective

The objective of this paper is to present a systematic smart home technology training approach for people with brain impairment. In addition, the paper outlines a multiple-baseline, single-case experimental design methodology to evaluate training effectiveness.

Methods

Adult participants experiencing acquired brain impairment who can provide consent to participate and who live in housing where smart home technology is available will be recruited. Target behaviors will be identified in consultation with each participant based on his or her personal goals for technology use. Target behaviors may include participant knowledge of the number and type of technology functions available, frequency of smart home technology use, and number of function types used. Usage data will be gathered via log-on smart home technology servers. A smart technology digital training package will also be developed and left on a nominated device (smartphone, tablet) with each participant to use during the trial and posttrial, as desired. Measures of the target behavior will be taken throughout the baseline, intervention, and postintervention phases to provide the evidence of impact of the training on the target behaviors and ascertain whether utilization rates are sustained over time. In addition, trial results will be analyzed using structured visual analysis, supplemented with statistical analysis appropriate to single-case methodology.

Results

While ascertaining the effectiveness of this training protocol, study results will offer new insights into technology-related training approaches for people with brain impairment. Preliminary data collection has been commenced at one supported housing site, with further scoping work continuing to recruit participants from additional sites.

Conclusions

Evaluation evidence will assist in planning for the smart technology set-up as well as training and support services necessary to accompany the provision of new devices and systems.

International Registered Report Identifier (IRRID)

RR1-10.2196/10451

Ambient Sensors for Elderly Care and Independent Living: A Survey

Elderly care at home is a matter of great concern if the elderly live alone, since unforeseen circumstances might occur that affect their well-being. Technologies that assist the elderly in independent living are essential for enhancing care in a cost-effective and reliable manner. Elderly care applications often demand real-time observation of the environment and the resident’s activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the elderly care system in the literature to identify current practices for future research directions. Therefore, this work is aimed at a comprehensive survey of non-wearable (i.e., ambient) sensors for various elderly care systems. This research work is an effort to obtain insight into different types of ambient-sensor-based elderly monitoring technologies in the home. With the aim of adopting these technologies, research works, and their outcomes are reported. Publications have been included in this survey if they reported mostly ambient sensor-based monitoring technologies that detect elderly events (e.g., activities of daily living and falls) with the aim of facilitating independent living. Mostly, different types of non-contact sensor technologies were identified, such as motion, pressure, video, object contact, and sound sensors. Besides, multicomponent technologies (i.e., combinations of ambient sensors with wearable sensors) and smart technologies were identified. In addition to room-mounted ambient sensors, sensors in robot-based elderly care works are also reported. Research that is related to the use of elderly behavior monitoring technologies is widespread, but it is still in its infancy and consists mostly of limited-scale studies. Elderly behavior monitoring technology is a promising field, especially for long-term elderly care. However, monitoring technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of elderly people.

Informatics for Precision Medicine and Healthcare

The past decade has witnessed great advances in biomedical informatics. Biomedical informatics is an emerging field of healthcare that aims to translate the laboratory observation into clinical practice. Smart healthcare has also developed rapidly with ubiquitous sensor and communication technologies. It is able to capture the online patient-centric phenotypic variables, thus providing a rich information base for translational biomedical informatics. Biomedical informatics and smart healthcare represent two interrelated disciplines. On one hand, biomedical informatics translates the bench discoveries into bedside, and, on the other hand, it is reciprocally informed by clinical data generated from smart healthcare. In this chapter, we will introduce the major strategies and challenges in the application of biomedical informatics technology in precision medicine and healthcare. We highlight how the informatics technology will promote the precision medicine and therefore promise the improvement of healthcare.

Health-Enabling and Ambient Assistive Technologies: Past, Present, Future

BACKGROUND

During the last decades, health-enabling and ambient assistive technologies became of considerable relevance for new informatics-based forms of diagnosis, prevention, and therapy.

OBJECTIVES

To describe the state of the art of health-enabling and ambient assistive technologies in 1992 and today, and its evolution over the last 25 years as well as to project where the field is expected to be in the next 25 years. In the context of this review, we define health-enabling and ambient assistive technologies as ambiently used sensor-based information and communication technologies, aiming at contributing to a person's health and health care as well as to her or his quality of life.

METHODS

Systematic review of all original articles with research focus in all volumes of the IMIA Yearbook of Medical Informatics. Surveying authors independently on key projects and visions as well as on their lessons learned in the context of health-enabling and ambient assistive technologies and summarizing their answers. Surveying authors independently on their expectations for the future and summarizing their answers.

RESULTS

IMIA Yearbook papers containing statements on health-enabling and ambient assistive technologies appear first in 2002. These papers form a minor part of published research articles in medical informatics. However, during recent years the number of articles published has increased significantly. Key projects were identified. There was a clear progress on the use of technologies. However proof of diagnostic relevance and therapeutic efficacy remains still limited. Reforming health care processes and focussing more on patient needs are required.

CONCLUSIONS

Health-enabling and ambient assistive technologies remain an important field for future health care and for interdisciplinary research. More and more publications assume that a person's home and their interaction therein, are becoming important components in health care provision, assessment, and management.

Towards exergaming commons: composing the exergame ontology for publishing open game data

BACKGROUND

It has been shown that exergames have multiple benefits for physical, mental and cognitive health. Only recently, however, researchers have started considering them as health monitoring tools, through collection and analysis of game metrics data. In light of this and initiatives like the Quantified Self, there is an emerging need to open the data produced by health games and their associated metrics in order for them to be evaluated by the research community in an attempt to quantify their potential health, cognitive and physiological benefits.

METHODS

We have developed an ontology that describes exergames using the Web Ontology Language (OWL); it is available at http://purl.org/net/exergame/ns#. After an investigation of key components of exergames, relevant ontologies were incorporated, while necessary classes and properties were defined to model these components. A JavaScript framework was also developed in order to apply the ontology to online exergames. Finally, a SPARQL Endpoint is provided to enable open data access to potential clients through the web.

RESULTS

Exergame components include details for players, game sessions, as well as, data produced during these game-playing sessions. The description of the game includes elements such as goals, game controllers and presentation hardware used; what is more, concepts from already existing ontologies are reused/repurposed. Game sessions include information related to the player, the date and venue where the game was played, as well as, the results/scores that were produced/achieved. These games are subsequently played by 14 users in multiple game sessions and the results derived from these sessions are published in a triplestore as open data.

CONCLUSIONS

We model concepts related to exergames by providing a standardized structure for reference and comparison. This is the first work that publishes data from actual exergame sessions on the web, facilitating the integration and analysis of the data, while allowing open data access through the web in an effort to enable the concept of Open Trials for Active and Healthy Ageing.

Big Data Technologies: New Opportunities for Diabetes Management

The so-called big data revolution provides substantial opportunities to diabetes management. At least 3 important directions are currently of great interest. First, the integration of different sources of information, from primary and secondary care to administrative information, may allow depicting a novel view of patient's care processes and of single patient's behaviors, taking into account the multifaceted nature of chronic care. Second, the availability of novel diabetes technologies, able to gather large amounts of real-time data, requires the implementation of distributed platforms for data analysis and decision support. Finally, the inclusion of geographical and environmental information into such complex IT systems may further increase the capability of interpreting the data gathered and extract new knowledge from them. This article reviews the main concepts and definitions related to big data, it presents some efforts in health care, and discusses the potential role of big data in diabetes care. Finally, as an example, it describes the research efforts carried on in the MOSAIC project, funded by the European Commission.