Health-enabling technologies for the elderly – An overview of services based on a literature review

Ludwig, Wolfram; Wolf, Klaus-Hendrik; Duwenkamp, Christopher; Gusew, Nathalie; Hellrung, Nils; Marschollek, Michael; Wagner, Markus; Haux, Reinhold

doi:10.1016/j.cmpb.2011.11.001

Cited by 75 publications

(35 citation statements)

References 34 publications

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“…With the world's aging population, health-enabling technologies and ambulatory monitoring of the elderly has become a prominent area of multi-disciplinary research [1,2]. Rapidly developing technology has made mobile and wireless devices part of daily life.…”

Section: Introductionmentioning

confidence: 99%

Detecting Falls with Wearable Sensors Using Machine Learning Techniques

Özdemir

Barshan

2014

Sensors

313

235

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Falls are a serious public health problem and possibly life threatening for people in fall risk groups. We develop an automated fall detection system with wearable motion sensor units fitted to the subjects' body at six different positions. Each unit comprises three tri-axial devices (accelerometer, gyroscope, and magnetometer/compass). Fourteen volunteers perform a standardized set of movements including 20 voluntary falls and 16 activities of daily living (ADLs), resulting in a large dataset with 2520 trials. To reduce the computational complexity of training and testing the classifiers, we focus on the raw data for each sensor in a 4 s time window around the point of peak total acceleration of the waist sensor, and then perform feature extraction and reduction. Most earlier studies on fall detection employ rule-based approaches that rely on simple thresholding of the sensor outputs. We successfully distinguish falls from ADLs using six machine learning techniques (classifiers): the k-nearest neighbor (k-NN) classifier, least squares method (LSM), support vector machines (SVM), Bayesian decision making (BDM), dynamic time warping (DTW), and artificial neural networks (ANNs). We compare the performance and the computational complexity of the classifiers and achieve the best results with the k-NN classifier and LSM, with sensitivity, specificity, and accuracy all above 99%. These classifiers also have acceptable computational requirements for training and testing. Our approach would be applicable in real-world scenarios where data records of indeterminate length, containing multiple activities in sequence, are recorded.

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Section: Introductionmentioning

confidence: 99%

Detecting Falls with Wearable Sensors Using Machine Learning Techniques

Özdemir

Barshan

2014

Sensors

313

235

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“…Telehealth technologies, for example, facilitate remote patient consultations and monitoring of chronic health conditions at a distance [10]. Remote telecare technologies offer the potential to monitor and assist older adults with routine tasks and everyday home activities while enhancing their independence and autonomy [11]. As an example, simple automated reminders help and encourage older people to take medications or follow exercise programmes [12].…”

Section: Introductionmentioning

confidence: 99%

eHealth Technology Competencies for Health Professionals Working in Home Care to Support Older Adults to Age in Place: Outcomes of a Two-Day Collaborative Workshop

Barakat¹,

Woolrych²,

Sixsmith³

et al. 2013

Med 2.0

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BackgroundThe demand for care is increasing, whereas in the near future the number of people working in professional care will not match with the demand for care. eHealth technology can help to meet the growing demand for care. Despite the apparent positive effects of eHealth technology, there are still barriers to technology adoption related to the absence of a composite set of knowledge and skills among health care professionals regarding the use of eHealth technology.ObjectiveThe objective of this paper is to discuss the competencies required by health care professionals working in home care, with eHealth technologies such as remote telecare and ambient assisted living (AAL), mobile health, and fall detection systems.MethodsA two-day collaborative workshop was undertaken with academics across multiple disciplines with experience in working on funded research regarding the application and development of technologies to support older people.ResultsThe findings revealed that health care professionals working in home care require a subset of composite skills as well as technology-specific competencies to develop the necessary aptitude in eHealth care. This paper argues that eHealth care technology skills must be instilled in health care professionals to ensure that technologies become integral components of future care delivery, especially to support older adults to age in place. Educating health care professionals with the necessary skill training in eHealth care will improve service delivery and optimise the eHealth care potential to reduce costs by improving efficiency. Moreover, embedding eHealth care competencies within training and education for health care professionals ensures that the benefits of new technologies are realized by casting them in the context of the larger system of care. These care improvements will potentially support the independent living of older persons at home.ConclusionsThis paper describes the health care professionals’ competencies and requirements needed for the use of eHealth technologies to support elderly adults to age in place. In addition, this paper underscores the need for further discussion of the changing role of health care professionals working in home care within the context of emerging eHealth care technologies. The findings are of value to local and central government, health care professionals, service delivery organizations, and commissioners of care to use this paper as a framework to conduct and develop competencies for health care professionals working with eHealth technologies.

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“…For the development of mHealth technology for older adults this means that representatives for the end users, researchers, technology developers on the hardware and software side, health care providers, and companies should be involved in the entire development process. This also increases the chances for the product to reach the market [41]. …”

Section: Development Of Mhealth Technologies For Older Adultsmentioning

confidence: 99%

Mobile Health Applications to Promote Active and Healthy Ageing

Helbostad

Vereijken

Becker

et al. 2017

Sensors

172

107

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The European population is ageing, and there is a need for health solutions that keep older adults independent longer. With increasing access to mobile technology, such as smartphones and smartwatches, the development and use of mobile health applications is rapidly growing. To meet the societal challenge of changing demography, mobile health solutions are warranted that support older adults to stay healthy and active and that can prevent or delay functional decline. This paper reviews the literature on mobile technology, in particular wearable technology, such as smartphones, smartwatches, and wristbands, presenting new ideas on how this technology can be used to encourage an active lifestyle, and discusses the way forward in order further to advance development and practice in the field of mobile technology for active, healthy ageing.

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Health-enabling technologies for the elderly – An overview of services based on a literature review

Cited by 75 publications

References 34 publications

Detecting Falls with Wearable Sensors Using Machine Learning Techniques

Detecting Falls with Wearable Sensors Using Machine Learning Techniques

eHealth Technology Competencies for Health Professionals Working in Home Care to Support Older Adults to Age in Place: Outcomes of a Two-Day Collaborative Workshop

Mobile Health Applications to Promote Active and Healthy Ageing

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