Developing a systems and informatics based approach to lifestyle monitoring within eHealth:part I - technology and data management

Lifestyle monitoring forms a subset of telecare in which data derived from sensors located in the home is used to identify variations in behaviour which are indicative of a change in care needs. Key to this is the performance of the sensors themselves and the way in which the information from multiple sources is integrated within the decision making process. The paper therefore considers the functions of the key sensors currently deployed and places their operation within the context of a proposed multi-level system structure which takes due cognisance of the requisite informatics framework

Unobtrusive Health Monitoring in Private Spaces: The Smart Home

With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

An Ambient Assisted Living Approach in Designing Domiciliary Services Combined With Innovative Technologies for Patients With Alzheimer’s Disease: A Case Study

Background: Alzheimer’s disease (AD) is one of the most disabling diseases to affect large numbers of elderly people worldwide. Because of the characteristics of this disease, patients with AD require daily assistance from service providers both in nursing homes and at home. Domiciliary assistance has been demonstrated to be cost effective and efficient in the first phase of the disease, helping to slow down the course of the illness, improve the quality of life and care, and extend independence for patients and caregivers. In this context, the aim of this work is to demonstrate the technical effectiveness and acceptability of an innovative domiciliary smart sensor system for providing domiciliary assistance to patients with AD which has been developed with an Ambient Assisted Living (AAL) approach. Methods: The design, development, testing, and evaluation of the innovative technological solution were performed by a multidisciplinary team. In all, 15 sociomedical operators and 14 patients with AD were directly involved in defining the endusers’ needs and requirements, identifying design principles with acceptability and usability features and evaluating the technological solutions before and after the real experimentation. Results: A modular technological system was produced to help caregivers continuously monitor the health status, safety, and daily activities of patients with AD. During the experimentation, the acceptability, utility, usability, and efficacy of this system were evaluated as quite positive. Conclusion: The experience described in this article demonstrated that AAL technologies are feasible and effective nowadays and can be actively used in assisting patients with AD in their homes. The extensive involvement of caregivers in the experimentation allowed to assess that there is, through the use of the technological system, a proven improvement in care performance and efficiency of care provision by both formal and informal caregivers and consequently an increase in the quality of life of patients, their relatives, and their caregivers

Fall prevention intervention technologies: A conceptual framework and survey of the state of the art

In recent years, an ever increasing range of technology-based applications have been developed with the goal of assisting in the delivery of more effective and efficient fall prevention interventions. Whilst there have been a number of studies that have surveyed technologies for a particular sub-domain of fall prevention, there is no existing research which surveys the full spectrum of falls prevention interventions and characterises the range of technologies that have augmented this landscape. This study presents a conceptual framework and survey of the state of the art of technology-based fall prevention systems which is derived from a systematic template analysis of studies presented in contemporary research literature. The framework proposes four broad categories of fall prevention intervention system: Pre-fall prevention; Post-fall prevention; Fall injury prevention; Cross-fall prevention. Other categories include, Application type, Technology deployment platform, Information sources, Deployment environment, User interface type, and Collaborative function. After presenting the conceptual framework, a detailed survey of the state of the art is presented as a function of the proposed framework. A number of research challenges emerge as a result of surveying the research literature, which include a need for: new systems that focus on overcoming extrinsic falls risk factors; systems that support the environmental risk assessment process; systems that enable patients and practitioners to develop more collaborative relationships and engage in shared decision making during falls risk assessment and prevention activities. In response to these challenges, recommendations and future research directions are proposed to overcome each respective challenge.The Royal Society, grant Ref: RG13082

A Study on Human Fall Detection Systems: Daily Activity Classification and Sensing Techniques

Fall detection for elderly is a major topic as far as assistive technologies are concerned. This is due to the high demand for the products and technologies related to fall detection with the ageing population around the globe. This paper gives a review of previous works on human fall detection devices and a preliminary results from a developing depth sensor based device. The three main approaches used in fall detection devices such as wearable based devices, ambient based devices and vision based devices are identified along with the sensors employed.  The frameworks and algorithms applied in each of the approaches and their uniqueness is also illustrated. After studying the performance and the shortcoming of the available systems a future solution using depth sensor is also proposed with preliminary results

Effectiveness of a batteryless and wireless wearable sensor system for identifying bed and chair exits in healthy older people

Aging populations are increasing worldwide and strategies to minimize the impact of falls on older people need to be examined. Falls in hospitals are common and current hospital technological implementations use localized sensors on beds and chairs to alert caregivers of unsupervised patient ambulations; however, such systems have high false alarm rates. We investigate the recognition of bed and chair exits in real-time using a wireless wearable sensor worn by healthy older volunteers. Fourteen healthy older participants joined in supervised trials. They wore a batteryless, lightweight and wireless sensor over their attire and performed a set of broadly scripted activities. We developed a movement monitoring approach for the recognition of bed and chair exits based on a machine learning activity predictor. We investigated the effectiveness of our approach in generating bed and chair exit alerts in two possible clinical deployments (Room 1 and Room 2). The system obtained recall results above 93% (Room 2) and 94% (Room 1) for bed and chair exits, respectively. Precision was >78% and 67%, respectively, while F-score was >84% and 77% for bed and chair exits, respectively. This system has potential for real-time monitoring but further research in the final target population of older people is necessary.Roberto Luis Shinmoto Torres, Renuka Visvanathan, Stephen Hoskins, Anton van den Hengel and Damith C. Ranasingh

INBED: A Highly Specialized System for Bed-Exit-Detection and Fall Prevention on a Geriatric Ward

OBJECTIVE:In geriatric institutions, the risk of falling of patients is very high and frequently leads to fractures of the femoral neck, which can result in serious consequences and medical costs. With regard to the current numbers of elderly people, the need for smart solutions for the prevention of falls in clinical environments as well as in everyday life has been evolving. METHODS:Hence, in this paper, we present the Inexpensive Node for bed-exit Detection (INBED), a comprehensive, favourable signaling system for bed-exit detection and fall prevention, to support the clinical efforts in terms of fall reduction. The tough requirements for such a system in clinical environments were gathered in close cooperation with geriatricians. RESULTS:The conceptional efforts led to a multi-component system with a core wearable device, attached to the patients, to detect several types of movements such as rising, restlessness and-in the worst case-falling. Occurring events are forwarded to the nursing staff immediately by using a modular, self-organizing and dependable wireless infrastructure. Both, the hardware and software of the entire INBED system as well as the particular design process are discussed in detail. Moreover, a trail test of the system is presented. CONCLUSIONS:The INBED system can help to relieve the nursing staff significantly while the personal freedom of movement and the privacy of patients is increased compared to similar systems

Technology for Successful Aging

With our partners at the University of Virginia we are developing a system of sensors, to monitor the activity of seniors in their residences. We measure motion, footfalls, sleep and restlessness, we have stove sensors and sensing mats, all connected wirelessly to a computer which performs an initial evaluation and data transfer to a secure server for further study. Based upon the monitor data we will implement an intervention to ameliorate functional decline. Focus group studies determine the attitudes, concerns and impressions of the residents and staff. We find that senior's attitude to technology is healthy and they will try helpful approaches. In addition to the statistical comparisons, we model the data using hidden Markov models, integrate or fuse the monitor data with video images, and reason about behavior using fuzzy logic. The results of this work will additionally reduce the workload on caregivers, foster communication between residents and family,and give these seniors independence.The authors are grateful for the support from NSF ITR grant IIS-0428420 and the U.S. Administration on Aging, under grant 90AM3013