Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring

Wireless sensor network (WSN) technologies are considered one of the key research areas in computer science and the healthcare application industries for improving the quality of life. The purpose of this paper is to provide a snapshot of current developments and future direction of research on wearable and implantable body area network systems for continuous monitoring of patients. This paper explains the important role of body sensor networks in medicine to minimize the need for caregivers and help the chronically ill and elderly people live an independent life, besides providing people with quality care. The paper provides several examples of state of the art technology together with the design considerations like unobtrusiveness, scalability, energy efficiency, security and also provides a comprehensive analysis of the various benefits and drawbacks of these systems. Although offering significant benefits, the field of wearable and implantable body sensor networks still faces major challenges and open research problems which are investigated and covered, along with some proposed solutions, in this paper

A STUDY ON HEALTH MONITORING SYSTEM: RECENT ADVANCEMENTS

ABSTRACT: A proliferating interest has been observed over the past years in the development of an accurate system for monitoring continuous human activities in the health care sectors, especially for the elderly. This paper conducts a survey of the various techniques and methods that are proposed to monitor the movements and activities of the elderly people. These techniques promise a useful and dependable detection system to give support and lessen the medical expenses of health care for the elderly. The detection approaches are divided into five main categories: wearable device based, wireless based, ambience device based, vision based and floor sensor / electric field sensors based. These techniques have focused on the pros and cons of the existing methods for recognizing the prospective scope of research in the domain of health monitoring systems. Apart from highlighting and analyzing the features of the existing techniques, perspectives on probable future studies have been detailed. ABSTRAK: Dewasa ini, pembangunan sistem yang tepat untuk memantau aktiviti berterusan terutamanya dalam sektor kesihatan warga tua mula mendapat tempat. Kaji selidik telah dijalankan dengan pelbagai teknik dan kaedah untuk meninjau pergerakan dan aktiviti golongan warga tua. Kaedah-kaedah ini memberikan sistem pengesanan yang berguna dan dipercayai untuk memberikan sokongan serta mengurangkan kos perubatan kesihatan bagi golongan tua. Pendekatan pengesanan dibahagikan kepada lima kategori utama; alatan yang dapat dipakai, alatan tanpa wayar, alatan berdasarkan persekitaran, alatan berasaskan penglihatan dan alatan berdasarkan pengesan pada lantai / medan elektrik.  Teknik-teknik ini memfokuskan kepada pro dan kontra kaedah yang sedia ada untuk mengenalpasti skop prospektif penyelidikan dalam domain sistem pengawasan kesihatan.  Selain daripada mengetengah dan menganalisa ciri-ciri teknik yang sedia ada, perspektif kajian akan datang juga diperincikan. KEYWORDS: health monitoring; elderly; wearable device; wireless device; ambience device, vision analysis; floor sensor

A critical analysis of an IoT—aware AAL system for elderly monitoring

Abstract A growing number of elderly people (65+ years old) are affected by particular conditions, such as Mild Cognitive Impairment (MCI) and frailty, which are characterized by a gradual cognitive and physical decline. Early symptoms may spread across years and often they are noticed only at late stages, when the outcomes remain irrevocable and require costly intervention plans. Therefore, the clinical utility of early detecting these conditions is of substantial importance in order to avoid hospitalization and lessen the socio-economic costs of caring, while it may also significantly improve elderly people's quality of life. This work deals with a critical performance analysis of an Internet of Things aware Ambient Assisted Living (AAL) system for elderly monitoring. The analysis is focused on three main system components: (i) the City-wide data capturing layer, (ii) the Cloud-based centralized data management repository, and (iii) the risk analysis and prediction module. Each module can provide different operating modes, therefore the critical analysis aims at defining which are the best solutions according to context's needs. The proposed system architecture is used by the H2020 City4Age project to support geriatricians for the early detection of MCI and frailty conditions

Advances in transfer learning methods based on computational intelligence

Traditional machine learning and data mining have made tremendous progress in many knowledge-based areas, such as clustering, classification, and regression. However, the primary assumption in all of these areas is that the training and testing data should be in the same domain and have the same distribution. This assumption is difficult to achieve in real-world applications due to the limited availability of labeled data. Associated data in different domains can be used to expand the availability of prior knowledge about future target data. In recent years, transfer learning has been used to address such cross-domain learning problems by using information from data in a related domain and transferring that data to the target task. The transfer learning methodology is utilized in this work with unsupervised and supervised learning methods. For unsupervised learning, a novel transfer-learning possibilistic c-means (TLPCM) algorithm is proposed to handle the PCM clustering problem in a domain that has insufficient data. Moreover, TLPCM overcomes the problem of differing numbers of clusters between the source and target domains. The proposed algorithm employs the historical cluster centers of the source data as a reference to guide the clustering of the target data. The experimental studies presented here were thoroughly evaluated, and they demonstrate the advantages of TLPCM in both synthetic and real-world transfer datasets. For supervised learning, a transfer learning (TL) technique is used to pre-train a CNN model on posture data and then fine-tune it on the sleep stage data. We used a ballistocardiography (BCG) bed sensor to collect both posture and sleep stage data to provide a non-invasive, in-home monitoring system that tracks changes in the subjects' health over time. The quality of sleep has a significant impact on health and life. This study adopts a hierarchical and none-hierarchical classification structure to develop an automatic sleep stage classification system using ballistocardiogram (BCG) signals. A leave-one-subject-out cross-validation (LOSO-CV) procedure is used for testing classification performance in most of the experiments. Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Deep Neural Networks DNNs are complementary in their modeling capabilities, while CNNs have the advantage of reducing frequency variations, LSTMs are good at temporal modeling. Polysomnography (PSG) data from a sleep lab was used as the ground truth for sleep stages, with the emphasis on three sleep stages, specifically, awake, rapid eye movement (REM), and non-REM sleep (NREM). Moreover, a transfer learning approach is employed with supervised learning to address the cross-resident training problem to predict early illness. We validate our method by conducting a retrospective study on three residents from TigerPlace, a retirement community in Columbia, MO, where apartments are fitted with wireless networks of motion and bed sensors. Predicting the early signs of illness in older adults by using a continuous, unobtrusive nursing home monitoring system has been shown to increase the quality of life and decrease care costs. Illness prediction is based on sensor data and uses algorithms such as support vector machine (SVM) and k-nearest neighbors (kNN). One of the most significant challenges related to the development of prediction algorithms for sensor networks is the use of knowledge from previous residents to predict new ones' behaviors. Each day, the presence or absence of illness was manually evaluated using nursing visit reports from a homegrown electronic medical record (EMR) system. In this work, the transfer learning SVM approach outperformed three other methods, i.e., regular SVM, one-class SVM, and one-class kNN.Includes bibliographical references (pages 114-127)

SHELDON Smart habitat for the elderly.

An insightful document concerning active and assisted living under different perspectives: Furniture and habitat, ICT solutions and Healthcare