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One of the major changes in the last decade in e-health has been the development of monitoring technologies for smart homes. An important part of such a monitoring system are the sensors. Sensors measure physical variables and transform this information to numerical values as input to smart algorithms. This chapter gives an overview of different types of sensors and their applications as well as new types of sensors and trends. It aims to give a nontechnical introduction in the emerging technologies of sensor design and applications. It also discusses topics which are relevant with respect to architectural choices, communication aspects, installation, and acceptance by the end user. Finally, some projects are mentioned which use sensor information in an e-health environment as illustration of the information covered in this chapter.

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Zorg onderzocht editie 2018 biedt een overzicht van alle onderzoek aan de hogescholen verpleegkunde in Vlaanderen én een overzicht van alle doctoraten aan alle Vlaamse universiteiten.
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The combination of an ageing population and the increase in chronic disease has greatly escalated health costs. It has been estimated that up to 75% of healthcare spending is on chronic disease management (mainly cardiovascular disease, cancer, diabetes and obesity) (World Health Organization 2010). It is now widely recognised that there is a need to radically change the present Healthcare systems, historically based on costly hospital-centred acute care, and make them more appropriate for the continuous home-based management of chronic diseases. The goals of the new approach are the improved management of the chronic disease through encouraging lifestyle changes and the effective early detection and treatment of any problem before it necessitates costly emergency intervention.
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Many older persons fall and are not able to get up again. The lack of timely aid can lead to severe complications. A camera based fall detection system can provide a solution. This paper gives the current status of our IWT-TETRA project FallCam. The goal of the project is to create a camera based fall detection system. Part of the project is creating an acquisition system for recording simulated as well as real-life falls at the homes of older people. The detection system will be developed and tested using these recordings. Furthermore, we will explain the advantages and disadvantages of different cameras and processing systems. Our choice is a centralized PC platform in combination with two regular IP cameras in each room of the monitored house.
Conference Paper
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The manual assessment of activities of daily living (ADLs) is a fundamental problem in elderly care. The use of miniature sensors placed in the environment or worn by a person has great potential in effective and unobtrusive long term monitoring and recognition of ADLs. This paper presents an effective and unobtrusive activity recognition system based on the combination of the data from two different types of sensors: RFID tag readers and accelerometers. We evaluate our algorithms on non-scripted datasets of 10 housekeeping activities performed by 12 subjects. The experimental results show that recognition accuracy can be significantly improved by fusing the two different types of sensors. We analyze different acceleration features and algorithms, and based on tag detections we suggest the best tagspsila placements and the key objects to be tagged for each activity.
Conference Paper
Epileptic seizure detection is traditionally done using video/electroencephalogram (EEG) monitoring, which is not applicable in a home situation. In recent years, attempts have been made to detect the seizures using other modalities. In this paper we investigate if a combined usage of accelerometers attached to the limbs and video data would increase the performance compared to a single modality approach. Therefore, we used two existing approaches for seizure detection in accelerometers and video and combined them using a linear discriminant analysis (LDA) classifier. The results for a combined detection have a better positive predictive value (PPV) of 95.00% compared to the single modality detection and reached a sensitivity of 83.33%.
The Index of ADL was developed to study results of treatment and prognosis in the elderly and chronically ill. Grades of the Index summarize over-all performance in bathing, dressing, going to toilet, transferring, continence, and feeding. More than 2,000 evaluations of 1,001 individuals demonstrated use of the Index as a survey instrument, as an objective guide to the course of chronic illness, as a tool for studying the aging process, and as an aid in rehabilitation teaching. Of theoretical interest is the observation that the order of recovery of Index functions in disabled patients is remarkably similar to the order of development of primary functions in children. This parallelism, and similarity to the behavior of primitive peoples, suggests that the Index is based on primary biological and psychosocial function, reflecting the adequacy of organized neurological and locomotor response.
The rate of increase in the number of aging population in Korea is very rapid among OECD-member countries. And fall accident is one of the most common factors that threaten the health of the elderly. Therefore, it is needed to develop a fall detection system for the elderly. Most fall detection systems use accelerometers attached on the torso. And in various studies, it was verified that these systems have high sensitivity and high specificity. However, the elderly would feel uncomfortable when banding a sensor on the chest every day. Therefore, in this study, we attached an accelerometer on the shoes to detect fall in the elderly. This prototype system would be improved as a smaller, low-power system in the next study. Also, applying energy harvesting device to this shoe system is being developed to reduce the weight of battery.
Wearable electronic systems: applications to medical diagnostics/monitoring
  • E Mcadams
  • A Krupaviciute
  • C Gehin
  • A Dittmar
  • G Delhomme
  • P Rubel
  • J Fayn
  • J Mclaughlin