Evaluating a Potential Commercial Tool for Healthcare Application for People with Dementia (original) (raw)
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Frontiers in Digital Health
Clinical researchers are using mobile-based sensors to obtain detailed and objective measures of the activity and health of research participants, but many investigators lack expertise in integrating wearables and sensor technologies effectively into their studies. Here, we describe the steps taken to design a study using sensors for disease monitoring in older adults and explore the benefits and drawbacks of our approach. In this study, the Geriatric Acute and Post-acute Fall Prevention Intervention (GAPcare), we created an iOS app to collect data from the Apple Watch's gyroscope, accelerometer, and other sensors; results of cognitive and fitness tests; and participant-entered survey data. We created the study app using ResearchKit, an open-source framework developed by Apple for medical research that includes neuropsychological tests (e.g., of executive function and memory), gait speed, balance, and other health assessments. Data is transmitted via an Application Programming I...
Springer Nature , 2018
Alzheimer's is a catastrophic neuro-degenerative state in the elderly which reduces thinking skills and thereby hamper daily activity. Thus the management may be helpful for people with such condition. This work presents sensor based management system for Alzheimer's patient. The main objective of this work is to report an early prototype of an eventual wearable system that can assist in managing the health of such patients and notify the caregivers in case of necessity. A brief case study is presented which showed that the proposed prototype can detect agitated and clam states of patients. As the ultimately developed assistive system will be packaged as a wearable device, the case study also investigated the usability of wearable devices on different age groups of Alzheimer's patients. In addition, electro dermal activity for 4 patient of age group 55-60 and 60-7s years were also explored to assess the health condition of the patients.
Annales Des Télécommunications, 2010
Monitoring and timely intervention are extremely important in the continuous management of health and wellness among all segments of the population, but particularly among those with mild dementia. In relation to this, we prescribe three design principles for the construction of services and applications. These are ambient intelligence, service continuity, and micro-context. In this paper, we provide three exemplars from our research and development activities that illustrate the use of these design principles in the construction of services and applications. All the applications are drawn from the field of care for mild dementia patients in their living quarters.
Wearable Technology for Detecting Significant Moments in Individuals with Dementia
BioMed Research International, 2019
The detection of significant moments can support the care of individuals with dementia by making visible what is most meaningful to them and maintaining a sense of interpersonal connection. We present a novel intelligent assistive technology (IAT) for the detection of significant moments based on patterns of physiological signal changes in individuals with dementia and their caregivers. The parameters of the IAT are tailored to each individual’s idiosyncratic physiological response patterns through an iterative process of incorporating subjective feedback on videos extracted from candidate significant moments identified through the IAT algorithm. The IAT was tested on three dyads (individual with dementia and their primary caregiver) during an eight-week movement program. Upon completion of the program, the IAT identified distinct, personal characteristics of physiological responsiveness in each participant. Tailored algorithms could detect moments of significance experienced by eit...
Alzheimer's & Dementia, 2018
Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials.
wearable sensors: an elders need
The life expectancy of people is improving with better health care, medicines and rapid scientific development. According to the United Nations Population Division (UN 2011), India's 60 and older population is expected around 323 million people, a number greater than the total U.S. population in 2012. An important challenge of providing health care to this large number of elders is of a great concern. Restructuring health care systems toward proactive managing of wellness rather than illness, and focusing on prevention and early detection of disease emerge as the answers to these problems. Wearable systems with context awareness for continuous health monitoring are a key technology in helping the transition to more proactive and affordable and scientific healthcare. In our paper we lay a basis understanding and approaches for implementing context awareness in a Body sensor network for proactive health management for elders.
Automated Health Alerts Using In-Home Sensor Data for Embedded Health Assessment
IEEE Journal of Translational Engineering in Health and Medicine, 2015
We present an example of unobtrusive, continuous monitoring in the home for the purpose of assessing early health changes. Sensors embedded in the environment capture behavior and activity patterns. Changes in patterns are detected as potential signs of changing health. We first present results of a preliminary study investigating 22 features extracted from inhome sensor data. A one-dimensional alert algorithm was then implemented to generate health alerts to clinicians in a senior housing facility. Clinicians analyze each alert and provide a rating on the clinical relevance. These ratings are then used as ground truth for training and testing classifiers. Here, we present the methodology for four classification approaches that fuse multisensor data. Results are shown using embedded sensor data and health alert ratings collected on 21 seniors over nine months. The best results show similar performance for two techniques, where one approach uses only domain knowledge and the second uses supervised learning for training. Finally, we propose a health change detection model based on these results and clinical expertise. The system of in-home sensors and algorithms for automated health alerts provides a method for detecting health problems very early so that early treatment is possible. This method of passive in-home sensing alleviates compliance issues. Index Terms-behavioral bio-markers, eldercare monitoring, health alerts, in-home sensing I. INTRODUCTION O UR view of embedded health assessment is the ongoing assessment of health changes based on an individual's behavior and activity patterns and baseline health conditions. Sensors embedded in the environment are used to collect behavior and activity patterns for the purpose of detecting health changes. Early detection is the key to promoting health, independence, and function as people age [1] [2]. Identifying and assessing problems early, while they are still small, provides a window of opportunity for interventions to alleviate problems before they become catastrophic. Older adults will benefit from early detection and recognition of small changes in health conditions and get help early when treatment is the most effective. Most importantly, function can be restored so they can continue living independently. Recently, there has been an increased focus on technology for enabling independent living and healthy aging. A major challenge for studies in this area is the capture of ground truth
Gerontology, 2014
How does a person detect that 'I am not feeling quite right', essentially that a change in health is about to occur? Below is a description of a typical scenario for families of older people. Mary, 86 years of age, lives alone; she has a history of well-controlled hypertension, adult-onset diabetes, and osteoarthritis of both knees. She drives to church a few miles from her home twice weekly for activities and plays bridge weekly for serious but lively card games. Her daughter checks by phone daily and accompanies her to primary care appointments routinely scheduled twice yearly. Awaking earlier than usual this morning, while walking to the bathroom she experienced slight dizziness and fell -luckily there were no obstacles so she was not injured. After using the bathroom, she felt tired, a bit thirsty, but not hungry, so she decided to nap in Abstract Environmentally embedded (nonwearable) sensor technology is in continuous use in elder housing to monitor a new set of 'vital signs' that continuously measure the functional status of older adults, detect potential changes in health or functional status, and alert healthcare providers for early recognition and treatment of those changes. Older adult participants' respiration, pulse, and restlessness are monitored as they sleep. Gait speed, stride length, and stride time are calculated daily, and automatically assess for increasing fall risk. Activity levels are summarized and graphically displayed for easy interpretation. Falls are detected when they occur and alerts are sent immediately to healthcare providers, so time to rescue may be reduced. Automated health alerts are sent to healthcare staff, based on continuously running algorithms applied to the sensor data, days and weeks before typical signs or symptoms are detected by the person, family members, or healthcare providers. Discovering these new functional status 'vital signs', developing auto-
npj Digital Medicine
Using connected sensing devices to remotely monitor health is a promising way to help transition healthcare from a rather reactive to a more precision medicine oriented proactive approach, which could be particularly relevant in the face of rapid population ageing and the challenges it poses to healthcare systems. Sensor derived digital measures of health, such as digital biomarkers or digital clinical outcome assessments, may be used to monitor health status or the risk of adverse events like falls. Current research around such digital measures has largely focused on exploring the use of few individual measures obtained through mobile devices. However, especially for long-term applications in older adults, this choice of technology may not be ideal and could further add to the digital divide. Moreover, large-scale systems biology approaches, like genomics, have already proven beneficial in precision medicine, making it plausible that the same could also hold for remote-health monit...