Remote patient monitoring: a comprehensive study

Malasinghe, Lakmini; Ramzan, Naeem; Dahal, Keshav

doi:10.1007/s12652-017-0598-x

Cited by 384 publications

(213 citation statements)

References 112 publications

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“…Wearables are recently seeing a tremendous success, spreading not only for sport training and fitness purposes, but also in healthcare, thanks to their userfriendly interfaces, their unobtrusiveness and, even, the current trend. Among others, smart clothing and accessories (e.g., watches and bracelets) equipped with several tens of micro-biosensors can measure both vitals and physiological signals [11], [13], offering a promising m-health scenario.…”

Section: Wearablesmentioning

confidence: 99%

Requirements and Enablers of Advanced Healthcare Services over Future Cellular Systems

2020

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The fifth generation (5G) of communication systems has ambitious targets of data rate, end-to-end latency, and connection availability, while the deployment of a new flexible network architecture will spur new applications. E-health and mobile health (m-health) solutions will meet the increasing demand of new, sustainable, and more accessible services beneficial to both practitioners and the rapidly aging population. This paper aims at defining the technical requirements of future cellular networks to support a variety of advanced healthcare services (e.g., smart hospital, telesurgery, connected ambulances, and monitoring). While 5G will be able to satisfy these requirements, it will also pave the way for future e-and m-health in the sixth-generation (6G) cellular networks.

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Section: Wearablesmentioning

confidence: 99%

Requirements and Enablers of Advanced Healthcare Services over Future Cellular Systems

2020

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“…Several surveys in the literature describe recent trends in smart homes aimed at assisted living systems [6][7][8][9]. These monitoring systems can use exclusively ambient sensors (i.e., RGB and/or infrared cameras) to limit user discomfort as much as possible [10][11][12], can use wearable sensors if health parameters need to be monitored [13], or can exploit different modalities at the same time [6][7][8].…”

Section: Ambient Assisted Living Systemsmentioning

confidence: 99%

A Vision-Based System for Monitoring Elderly People at Home

2020

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Assisted living technologies can be of great importance for taking care of elderly people and helping them to live independently. In this work, we propose a monitoring system designed to be as unobtrusive as possible, by exploiting computer vision techniques and visual sensors such as RGB cameras. We perform a thorough analysis of existing video datasets for action recognition, and show that no single dataset can be considered adequate in terms of classes or cardinality. We subsequently curate a taxonomy of human actions, derived from different sources in the literature, and provide the scientific community with considerations about the mutual exclusivity and commonalities of said actions. This leads us to collecting and publishing an aggregated dataset, called ALMOND (Assisted Living MONitoring Dataset), which we use as the training set for a vision-based monitoring approach.We rigorously evaluate our solution in terms of recognition accuracy using different state-of-the-art architectures, eventually reaching 97% on inference of basic poses, 83% on alerting situations, and 71% on daily life actions. We also provide a general methodology to estimate the maximum allowed distance between camera and monitored subject. Finally, we integrate the defined actions and the trained model into a computer-vision-based application, specifically designed for the objective of monitoring elderly people at their homes.

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“…The work presented the survey of deep learning based unsupervised and incremental learning based techniques for activity recognition from three different aspects: the application of study, deep model, and sensory modality. Malasinghe et al [12] presented a study based on remote health monitoring of patients. The study provided the review of advancements occurred in the field of remote health monitoring with the use of contactbased and contactless devices.…”

Section: Related Workmentioning

confidence: 99%

A Unified Framework for Behaviour Monitoring and Abnormality Detection for Smart Home

Grewal

Kaur

Park

2019

Wireless Communications and Mobile Computing

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With the revolution in smart infrastructure in the recent past, the smart healthcare system has been paid more considerable attention. The continuous upgradation of electricity meters to smart electricity devices has probed into a new market of intelligent data analysis services, providing aid to the health care systems. This paper presents a unified framework for extracting user behaviour patterns from home-based smart electricity meter data. The structure allows exploration and integration of frequent pattern growth algorithm for pattern mining and application of a variety of machine learning algorithms for categorizing the activities into manually labelled classes along with the implementation of Local Outlier Factor method for detection of an abnormal pattern of the inhabitant of smart homes. To evaluate the proposed framework, the work is implemented on the smart electricity dataset from the United Kingdom by separating the data into four distinct data files meant for the morning, afternoon, evening, and night energy utilization records. The results show a remarkable performance of Support Vector Machine (SVM) and Multilayer Perceptron (MLP) classifiers with kappa statics greater than 0.95 for all time slots data. The resultant frequent device utilization patterns with anomaly score more than the threshold value, reflecting abnormal activity patterns, are found more in evening time data in comparison to other time slots, requiring the immediate attention of concerned healthcare authorities.

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Remote patient monitoring: a comprehensive study

Cited by 384 publications

References 112 publications

Requirements and Enablers of Advanced Healthcare Services over Future Cellular Systems

Requirements and Enablers of Advanced Healthcare Services over Future Cellular Systems

A Vision-Based System for Monitoring Elderly People at Home

A Unified Framework for Behaviour Monitoring and Abnormality Detection for Smart Home

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