Existing research on the recognition of Activities of Daily Living (ADL) from simple sensor networks assumes that only a single person is present in the home. In real life there will be situations where the inhabitant receives visits from family members or professional health care givers. In such cases activity recognition is unreliable. In this paper, we investigate the problem of detecting multiple persons in an environment equipped with a sensor network consisting of binary sensors. We conduct a real-life experiment for detection of visits in the oce of the supervisor where the oce is equipped with a video camera to record the ground truth. We collected data during two months and used two models, a Naive Bayes Classier and a Hidden Markov Model for a visitor detection. An evaluation of these two models shows that we achieve an accuracy of 83% with the NBC and an accuracy of 92% with a HMM, respectively.
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Purpose – In the domain of healthcare, both process efficiency and the quality of care can be improved through the use of dedicated pervasive technologies. Among these applications are so-called real-time location systems (RTLS). Such systems are designed to determine and monitor the location of assets and people in real time through the use of wireless sensor networks. Numerous commercially available RTLS are used in hospital settings. The nursing home is a relatively unexplored context for the application of RTLS and offers opportunities and challenges for future applications. The paper aims to discuss these issues. Design/methodology/approach – This paper sets out to provide an overview of general applications and technologies of RTLS. Thereafter, it describes the specific healthcare applications of RTLS, including asset tracking, patient tracking and personnel tracking. These overviews are followed by a forecast of the implementation of RTLS in nursing homes in terms of opportunities and challenges. Findings – By comparing the nursing home to the hospital, the RTLS applications for the nursing home context that are most promising are asset tracking of expensive goods owned by the nursing home in orderto facilitate workflow and maximise financial resources, and asset tracking of personal belongings that may get lost due to dementia. Originality/value – This paper is the first to provide an overview of potential application of RTLS technologies for nursing homes. The paper described a number of potential problem areas that can be addressed by RTLS. Published by Emerald Publishing Limited Original article: https://doi.org/10.1108/JET-11-2017-0046 For this paper Joost van Hoof received the Highly Recommended Award from Emerald Publishing Ltd. in October 2019: https://www.emeraldgrouppublishing.com/authors/literati/awards.htm?year=2019
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The shift toward prevention and self-management in health hinges upon a massive realization of behavior change, which involves the use of virtual coaches. Increasing availability of data from mobile devices and techniques for real time analysis provide new opportunities for personalizing virtual coaches. We propose an architecture that takes advantage of those developments. We identify the required knowledge and methods to develop a flexible ecosystem for rapid prototyping of personalized virtual coaches.
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There is a growing number of eHealth interventionsaiming at enhancing lifestyle to address obesity. However, theexisting interventions do not take the emotional aspects ofobesity into account. Forty percent of the overweight populationis an emotional eater. Emotional eaters gain weight because ofpoor emotion regulation, not just due to bad eating habits. Weaim at developing a personalised virtual coach ‘Denk je zèlf!’providing support for self-regulation of emotions for obeseemotional eaters. This paper presents a research study protocolon validating persuasive coaching strategies in emotionregulation, based on Dialectical Behaviour Therapy, ultimatelytargeting behaviour change. Our goal is to design a personalisedeCoaching framework, allowing us to optimally translatesuccessful behaviour change mechanisms and techniques, suchas dialectical strategies, into personalised persuasive coachingstrategies.
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BACKGROUND: There is a growing interest in empowering older adults to age in place by deploying various types of technology (ie, eHealth, ambient assisted living technology, smart home technology, and gerontechnology). However, initiatives aimed at implementing these technologies are complicated by the fact that multiple stakeholder groups are involved. Goals and motives of stakeholders may not always be transparent or aligned, yet research on convergent and divergent positions of stakeholders is scarce. OBJECTIVE: To provide insight into the positions of stakeholder groups involved in the implementation of technology for aging in place by answering the following questions: What kind of technology do stakeholders see as relevant? What do stakeholders aim to achieve by implementing technology? What is needed to achieve successful implementations? METHODS: Mono-disciplinary focus groups were conducted with participants (n=29) representing five groups of stakeholders: older adults (6/29, 21%), care professionals (7/29, 24%), managers within home care or social work organizations (5/29, 17%), technology designers and suppliers (6/29, 21%), and policy makers (5/29, 17%). Transcripts were analyzed using thematic analysis. RESULTS: Stakeholders considered 26 different types of technologies to be relevant for enabling independent living. Only 6 out of 26 (23%) types of technology were mentioned by all stakeholder groups. Care professionals mentioned fewer different types of technology than other groups. All stakeholder groups felt that the implementation of technology for aging in place can be considered a success when (1) older adults' needs and wishes are prioritized during development and deployment of the technology, (2) the technology is accepted by older adults, (3) the technology provides benefits to older adults, and (4) favorable prerequisites for the use of technology by older adults exist. While stakeholders seemed to have identical aims, several underlying differences emerged, for example, with regard to who should pay for the technology. Additionally, each stakeholder group mentioned specific steps that need to be taken to achieve successful implementation. Collectively, stakeholders felt that they need to take the leap (ie, change attitudes, change policies, and collaborate with other organizations); bridge the gap (ie, match technology with individuals and stimulate interdisciplinary education); facilitate technology for the masses (ie, work on products and research that support large-scale rollouts and train target groups on how to use technology); and take time to reflect (ie, evaluate use and outcomes). CONCLUSIONS: Stakeholders largely agree on the direction in which they should be heading; however, they have different perspectives with regard to the technologies that can be employed and the work that is needed to implement them. Central to these issues seems to be the tailoring of technology or technologies to the specific needs of each community-dwelling older adult and the work that is needed by stakeholders to support this type of service delivery on a large scale. KEYWORDS: aged; eHealth; focus groups; health services for the elderly; implementation management; independent living; project and people management; qualitative research; technology
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Background: Patients with chronic obstructive pulmonary disease (COPD) demonstrate reduced levels of daily physical activity (DPA) compared to healthy controls. This results in a higher risk of hospital admission and shorter survival. Performing regular DPA reduces these risks. Objective: To develop an eHealth intervention that will support patients with COPD to improve or maintain their DPA after pulmonary rehabilitation. Methods: The design process consisted of literature research and the iterative developing and piloting phases of the Medical Research Council (MRC) model for complex clinical interventions and the involvement of end users. Participants were healthy adults and persons with COPD. Results: The mobile phone interface met all the set requirements. Participants found that the app was stimulating and that reaching their DPA goals was rewarding. The mean (SD) scores on a 7-point scale for usability, ease of use, ease of learning, and contentment were 3.8 (1.8), 5.1 (1.1), 6.0 (1.6), and 4.8 (1.3), respectively. The mean (SD) correlation between the mobile phone and a validated accelerometer was 0.88 (0.12) in the final test. The idea of providing their health care professional with their DPA data caused no privacy issues in the participants. Battery life lasted for an entire day with the final version, and readability and comprehensibility of text and colors were favorable. Conclusions: By employing a user-centered design approach, a mobile phone was found to be an adequate and feasible interface for an eHealth intervention. The mobile phone and app are easy to learn and use by patients with COPD. In the final test, the accuracy of the DPA measurement was good. The final version of the eHealth intervention is presently being tested by our group for efficacy in a randomized controlled trial in COPD patients.
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Although the scientific literature consists of over 10,000 papers on eHealth, remarkably few applications are consistently being used in the healthcare domain. Numerous reasons for this lack of progression have been noted, one of these being the objection of medical professionals to the introduction of interventions that are supposedly lacking evidence of their effectiveness. A study of existing literature and, especially, literature reviews confirms that there does not yet exist scientific evidence of the effectiveness of eHealth. But, this study also comes across insights in the reasons why scientific evidence is hard to come by and possible future directions for healthcare organisations how to take advantage of eHealth despite the current lack of interventions that are truly evidence-based and for eHealth researchers to build collectively a stronger evidence-based case for eHealth interventions.
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Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant's progress in achieving a personal step goal. The gathered step count data was used to train eight different machine learning algorithms to make hourly estimations of the probability of achieving a personalized, daily steps threshold. In 80% of the individual cases, the Random Forest algorithm was the best performing algorithm (mean accuracy = 0.93, range = 0.88–0.99, and mean F1-score = 0.90, range = 0.87–0.94). To demonstrate the practical usefulness of these models, we developed a proof-of-concept Web application that provides personalized feedback about whether a participant is expected to reach his or her daily threshold. We argue that the use of machine learning could become an invaluable asset in the process of automated personalized coaching. The individualized algorithms allow for predicting physical activity during the day and provides the possibility to intervene in time.
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The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions https://doi.org/10.3390/app10238348 LinkedIn: https://www.linkedin.com/in/john-bolte-0856134/
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