Remote health monitoring of elderly through wearable sensors

Due to a rapidly increasing aging population and its associated challenges in health and social care, Ambient Assistive Living has become the focal point for both researchers and industry alike. The need to manage or even reduce healthcare costs while improving the quality of service is high government agendas. Although, technology has a major role to play in achieving these aspirations, any solution must be designed, implemented and validated using appropriate domain knowledge. In order to overcome these challenges, the remote real-time monitoring of a person’s health can be used to identify relapses in conditions, therefore, enabling early intervention. Thus, the development of a smart healthcare monitoring system, which is capable of observing elderly people remotely, is the focus of the research presented in this paper. The technology outlined in this paper focuses on the ability to track a person’s physiological data to detect specific disorders which can aid in Early Intervention Practices. This is achieved by accurately processing and analysing the acquired sensory data while transmitting the detection of a disorder to an appropriate career. The finding reveals that the proposed system can improve clinical decision supports while facilitating Early Intervention Practices. Our extensive simulation results indicate a superior performance of the proposed system: low latency (96% of the packets are received with less than 1 millisecond) and low packets-lost (only 2.2% of total packets are dropped). Thus, the system runs efficiently and is cost-effective in terms of data acquisition and manipulation.

[1]  Timo Hämäläinen,et al.  Availability and End-to-end Reliability in Low Duty Cycle Multihop Wireless Sensor Networks , 2009, Sensors.

[2]  Tatsuya Yamazaki,et al.  The Ubiquitous Home , 2007 .

[3]  Jacques Demongeot,et al.  A model for the measurement of patient activity in a hospital suite , 2006, IEEE Transactions on Information Technology in Biomedicine.

[4]  Olivier Chételat,et al.  Combination of body sensor networks and on-body signal processing algorithms: the practical case of MyHeart project , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[5]  Gonzalo Mateos,et al.  Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges , 2015, 2015 IEEE International Conference on Services Computing.

[6]  N. M. Barnes,et al.  Lifestyle monitoring-technology for supported independence , 1998 .

[7]  Steven J. Miller,et al.  Using sensor networks to detect urinary tract infections in older adults , 2011, 2011 IEEE 13th International Conference on e-Health Networking, Applications and Services.

[8]  Andrea Lockerd Thomaz,et al.  Touched by a robot: An investigation of subjective responses to robot-initiated touch , 2011, 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[9]  Ahmad Ali,et al.  A Comprehensive Survey on Real-Time Applications of WSN , 2017, Future Internet.

[10]  Dieter Hayn,et al.  The Internet of Things for Ambient Assisted Living , 2010, 2010 Seventh International Conference on Information Technology: New Generations.

[11]  Luca Mottola,et al.  Programming wireless sensor networks , 2011, ACM Comput. Surv..

[12]  Fotis Foukalas,et al.  Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems , 2018, Sensors.

[13]  S G Rubin,et al.  Bed blocking by elderly patients in general-hospital wards. , 1975, Age and ageing.

[14]  Elena Mugellini,et al.  Senior Living Lab: An Ecological Approach to Foster Social Innovation in an Ageing Society , 2016, Future Internet.

[15]  P. H. Millard,et al.  A simulation modelling approach to evaluating length of stay, occupancy, emptiness and bed blocking in a hospital geriatric department , 1998, Health care management science.

[16]  Weihua Sheng,et al.  Cloud-Based Smart Home Environment (CoSHE) for home healthcare , 2016, 2016 IEEE International Conference on Automation Science and Engineering (CASE).

[17]  Rolf Dornberger,et al.  Sensor-Based Tracking and Big Data Processing of Patient Activities in Ambient Assisted Living , 2016, 2016 IEEE International Conference on Healthcare Informatics (ICHI).

[18]  Wei Hong,et al.  TinyDB: an acquisitional query processing system for sensor networks , 2005, TODS.

[19]  Ana Manzano-Santaella,et al.  From bed-blocking to delayed discharges: precursors and interpretations of a contested concept , 2010, Health services management research.

[20]  Jenny Benois-Pineau,et al.  The IMMED project: wearable video monitoring of people with age dementia , 2010, ACM Multimedia.

[21]  Danilo De Donno,et al.  An IoT-Aware Architecture for Smart Healthcare Systems , 2015, IEEE Internet of Things Journal.

[22]  R. Kumar,et al.  An IoT based patient monitoring system using raspberry Pi , 2016, 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE'16).

[23]  Roger Orpwood,et al.  The installation and support of internationally distributed equipment for people with dementia , 2004, IEEE Transactions on Information Technology in Biomedicine.

[24]  Daniel Teichmann,et al.  HeartCycle: Advanced sensors for telehealth applications , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[25]  Rosa Maria Alsina-Pagès,et al.  Real-Time Distributed Architecture for Remote Acoustic Elderly Monitoring in Residential-Scale Ambient Assisted Living Scenarios , 2018, Sensors.

[26]  Hannu Tenhunen,et al.  Smart e-Health Gateway: Bringing intelligence to Internet-of-Things based ubiquitous healthcare systems , 2015, 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC).

[27]  Lei Jing,et al.  A Healthcare System for Detection and Analysis of Daily Activity Based on Wearable Sensor and Smartphone , 2015, 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom).

[28]  S. E. Pawar,et al.  Monitoring mobile patients using predictive analysis by data from wearable sensors , 2016, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).

[29]  Shyamal Patel,et al.  A review of wearable sensors and systems with application in rehabilitation , 2012, Journal of NeuroEngineering and Rehabilitation.

[30]  Diane J. Cook,et al.  Smart environments - technology, protocols and applications , 2004 .

[31]  Lars Schmitt,et al.  Continua: The reference architecture of a personal telehealth ecosystem , 2010, The 12th IEEE International Conference on e-Health Networking, Applications and Services.

[32]  Li-Wei Ko,et al.  Review of Wireless and Wearable Electroencephalogram Systems and Brain-Computer Interfaces – A Mini-Review , 2009, Gerontology.

[33]  Sachin Bojewar,et al.  Sensor networks based healthcare monitoring system , 2016, 2016 International Conference on Inventive Computation Technologies (ICICT).

[34]  Virginia Menezes,et al.  Healthcare based on IoT using Raspberry Pi , 2015, 2015 International Conference on Green Computing and Internet of Things (ICGCIoT).

[35]  Jun Cheng,et al.  A Wearable Smartphone-Based Platform for Real-Time Cardiovascular Disease Detection Via Electrocardiogram Processing , 2010, IEEE Transactions on Information Technology in Biomedicine.

[36]  Toshiyo Tamura,et al.  E-Healthcare at an Experimental Welfare Techno House in Japan , 2007, The open medical informatics journal.

[37]  Nikolaos G. Bourbakis,et al.  A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[38]  Matt Welsh,et al.  Sensor networks for medical care , 2005, SenSys '05.

[39]  Francesc Alías,et al.  homeSound: Real-Time Audio Event Detection Based on High Performance Computing for Behaviour and Surveillance Remote Monitoring , 2017, Sensors.

[40]  T. Dall,et al.  An aging population and growing disease burden will require a large and specialized health care workforce by 2025. , 2013, Health affairs.

[41]  Abdenour Bouzouane,et al.  A KEYHOLE PLAN RECOGNITION MODEL FOR ALZHEIMER'S PATIENTS: FIRST RESULTS , 2007, Appl. Artif. Intell..

[42]  Taehun Kim,et al.  A data acquisition architecture for healthcare services in mobile sensor networks , 2016, 2016 International Conference on Big Data and Smart Computing (BigComp).

[43]  Paolo Bonato,et al.  Wearable Sensors and Systems , 2010, IEEE Engineering in Medicine and Biology Magazine.

[44]  Silvia Coradeschi,et al.  Sensor Network Infrastructure for a Home Care Monitoring System , 2014, Sensors.

[45]  Diane J. Cook,et al.  Keeping the Resident in the Loop: Adapting the Smart Home to the User , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[46]  Kyung-Sup Kwak,et al.  The Internet of Things for Health Care: A Comprehensive Survey , 2015, IEEE Access.

[47]  Ioanna Chouvarda,et al.  Temporal Variation in telemonitoring data: on the Effect of Medication and Lifestyle Compliance , 2012 .