Information technology for active ageing: A review of theory and practice

Active Ageing aims to foster a physically, mentally and socially active lifestyle as a person ages. It is a complex, multi-faceted problem that involves a variety of different actors, such as policy makers, doctors, care givers, family members, friends and, of course, older adults. This review aims to understand the role of a new actor, which increasingly plays the role of enabler and facilitator, i.e., that of the technology provider. The review specifically focuses on Information Technology (IT), with a particular emphasis on software applications, and on how IT can prevent decline, compensate for lost capabilities, aid care, and enhance existing capabilities. The analysis confirms the crucial role of IT in Active Ageing, shows that Active Ageing requires a multidisciplinary approach, and identifies the need for better integration of hardware, software, the environment and the involved actors

Fall prevention intervention technologies: A conceptual framework and survey of the state of the art

In recent years, an ever increasing range of technology-based applications have been developed with the goal of assisting in the delivery of more effective and efficient fall prevention interventions. Whilst there have been a number of studies that have surveyed technologies for a particular sub-domain of fall prevention, there is no existing research which surveys the full spectrum of falls prevention interventions and characterises the range of technologies that have augmented this landscape. This study presents a conceptual framework and survey of the state of the art of technology-based fall prevention systems which is derived from a systematic template analysis of studies presented in contemporary research literature. The framework proposes four broad categories of fall prevention intervention system: Pre-fall prevention; Post-fall prevention; Fall injury prevention; Cross-fall prevention. Other categories include, Application type, Technology deployment platform, Information sources, Deployment environment, User interface type, and Collaborative function. After presenting the conceptual framework, a detailed survey of the state of the art is presented as a function of the proposed framework. A number of research challenges emerge as a result of surveying the research literature, which include a need for: new systems that focus on overcoming extrinsic falls risk factors; systems that support the environmental risk assessment process; systems that enable patients and practitioners to develop more collaborative relationships and engage in shared decision making during falls risk assessment and prevention activities. In response to these challenges, recommendations and future research directions are proposed to overcome each respective challenge.The Royal Society, grant Ref: RG13082

SHELDON Smart habitat for the elderly.

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

Progress in ambient assisted systems for independent living by the elderly

One of the challenges of the ageing population in many countries is the efficient delivery of health and care services, which is further complicated by the increase in neurological conditions among the elderly due to rising life expectancy. Personal care of the elderly is of concern to their relatives, in case they are alone in their homes and unforeseen circumstances occur, affecting their wellbeing. The alternative; i.e. care in nursing homes or hospitals is costly and increases further if specialized care is mobilized to patients’ place of residence. Enabling technologies for independent living by the elderly such as the ambient assisted living systems (AALS) are seen as essential to enhancing care in a cost-effective manner. In light of significant advances in telecommunication, computing and sensor miniaturization, as well as the ubiquity of mobile and connected devices embodying the concept of the Internet of Things (IoT), end-to-end solutions for ambient assisted living have become a reality. The premise of such applications is the continuous and most often real-time monitoring of the environment and occupant behavior using an event-driven intelligent system, thereby providing a facility for monitoring and assessment, and triggering assistance as and when needed. As a growing area of research, it is essential to investigate the approaches for developing AALS in literature to identify current practices and directions for future research. This paper is, therefore, aimed at a comprehensive and critical review of the frameworks and sensor systems used in various ambient assisted living systems, as well as their objectives and relationships with care and clinical systems. Findings from our work suggest that most frameworks focused on activity monitoring for assessing immediate risks while the opportunities for integrating environmental factors for analytics and decision-making, in particular for the long-term care were often overlooked. The potential for wearable devices and sensors, as well as distributed storage and access (e.g. cloud) are yet to be fully appreciated. There is a distinct lack of strong supporting clinical evidence from the implemented technologies. Socio-cultural aspects such as divergence among groups, acceptability and usability of AALS were also overlooked. Future systems need to look into the issues of privacy and cyber security

Hidden work and the challenges of scalability and sustainability in ambulatory assisted living

Assisted living technologies may help people live independently while also—potentially—reducing health and care costs. But they are notoriously difficult to implement at scale and many devices are abandoned following initial adoption.We report findings from a study of global positioning system (GPS) tracking devices intended to support the independent living of people with cognitive impairment. Our aims were threefold: to understand (through ethnography) such individuals’ lived experience of GPS tracking; to facilitate (through action research) the customization and adaptation of technologies and care services to provide effective, ongoing support; and to explore the possibilities for a co-production methodology that would enable people with cognitive impairment and their families to work with professionals and technical designers to shape these devices and services to meet their particular needs in a sustainable way.We found that the articulation work needed for maintaining the GPS technology in “working order” was extensive and ongoing. This articulation work does not merely supplement formal procedures, a lot of it is needed to get round them, but it is also often invisible and thus its importance goes largely unrecognized. If GPS technologies are to be implemented at scale and sustainably, methods must be found to capitalize on the skills and tacit knowledge held within the care network (professional and lay) to resolve problems, improve device design, devise new service solutions, and foster organizational learning

Active and assisted living ecosystem for the elderly

A novel ecosystem to promote the physical, emotional and psychic health and well-being of the elderly is presented. Our proposal was designed to add several services developed to meet the needs of the senior population, namely services to improve social inclusion and increase contribution to society. Moreover, the solution monitors the vital signs of elderly individuals, as well as environmental parameters and behavior patterns, in order to seek eminent danger situations and predict potential hazardous issues, acting in accordance with the various alert levels specified for each individual. The platform was tested by seniors in a real scenario. The experimental results demonstrated that the proposed ecosystem was well accepted and is easy to use by seniors

Multimedia sensors embedded in smartphones for ambient assisted living and e-health

The final publication is available at link.springer.com[EN] Nowadays, it is widely extended the use of smartphones to make human life more comfortable. Moreover, there is a special interest on Ambient Assisted Living (AAL) and e-Health applications. The sensor technology is growing and amount of embedded sensors in the smartphones can be very useful for AAL and e-Health. While some sensors like the accelerometer, gyroscope or light sensor are very used in applications such as motion detection or light meter, there are other ones, like the microphone and camera which can be used as multimedia sensors. This paper reviews the published papers focused on showing proposals, designs and deployments of that make use of multimedia sensors for AAL and e-health. We have classified them as a function of their main use. They are the sound gathered by the microphone and image recorded by the camera. We also include a comparative table and analyze the gathered information.Parra-Boronat, L.; Sendra, S.; Jimenez, JM.; Lloret, J. (2016). Multimedia sensors embedded in smartphones for ambient assisted living and e-health. Multimedia Tools and Applications. 75(21):13271-13297. doi:10.1007/s11042-015-2745-8S13271132977521Acampora G, Cook DJ, Rashidi P, Vasilakos AV (2013) A survey on ambient intelligence in healthcare. Proc IEEE 101(12):2470–2494Al-Attas R, Yassine A, Shirmohammadi S (2012) Tele-Medical Applications in Home-Based Health Care. In proceeding of the 2012 I.E. International Conference on Multimedia and Expo Workshops (ICMEW 2012). Jul. 9–13, 2012. Melbourne, Australia. (pp. 441–446)Alemdar H, Ersoy C (2010) Wireless sensor networks for healthcare: a survey. Comput Netw 54(15):2688–2710Alqassim S, Ganesh M, Khoja S, Zaidi M, Aloul F, Sagahyroon A (2012) Sleep apnea monitoring using mobile phones. In proceedings of the 14th International Conference on e-Health Networking, Applications and Services (Healthcom 2012). Oct. 10 – 13, 2012. Beijing, China. (pp. 443–446)Anderson G, Horvath J (2004) The growing burden of chronic disease in America. Public Health Rep 119(3):263–270Aquilano M, Cavallo F, Bonaccorsi M, Esposito R, Rovini E, Filippi M, Carrozza MC (2012) Ambient assisted living and ageing: Preliminary results of RITA project. In proceedings of 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2012), Aug. 28-Sept. 1, 2012. San Diego USA. (pp. 5823–5826)Bellini P, Bruno I, Cenni D, Fuzier A, Nesi P, Paolucci M (2012) Mobile Medicine: semantic computing management for health care applications on desktop and mobile devices. Multimed Tools Appl 58(1):41–79Boulos MN, Wheeler S, Tavares C, Jones R (2011) How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX. Biomed Eng Online 10(1):24Bourouis A, Feham M, Hossain MA, Zhang L (2014) An intelligent mobile based decision support system for retinal disease diagnosis. Decis Support Syst 59(2014):341–350Bourouis A, Zerdazi A, Feham M, Bouchachia A (2013) M-health: skin disease analysis system using Smartphone’s camera. Procedia Comput Sci 19(2013):1116–1120M.W. Brault, (2010). Americans With Disabilities: 2010. Household Economic Studies. In United States Census Bureau website. Available at: www.census.gov/prod/2012pubs/p70-131.pdf Last Access 16 Dec 2014Breath Counter App. In Google Play website. Available at: https://play.google.com/store/apps/details?id=com.softrove.app.bc Last Access 30 Nov 2014Cardinaux F, Bhowmik D, Abhayaratne C, Hawley MS (2011) Video based technology for ambient assisted living: a review of the literature. J Ambient Intell Smart Environ 3(3):253–269Cardiograph App. In Google Play website. Available at: https://play.google.com/store/apps/details?id=com.macropinch.hydra.android . Last Access 30 Nov 2014Chaaraoui AA, Climent-Pérez P, Flórez-Revuelta F (2012) A review on vision techniques applied to human behaviour analysis for ambient-assisted living. Expert Syst Appl 39(12):10873–10888Chen NC, Wang KC, Chu HH (2012) Listen-to-nose: a low-cost system to record nasal symptoms in daily life. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (UBIComp 2012). Sep. 05–08, 2012. Pittsburgh, USA. (pp. 590–591)Chiarini G, Ray P, Akter S, Masella C, Ganz A (2013) mHealth technologies for chronic diseases and elders: a systematic review. IEEE J Sel Areas Commun 31(9):6–18Color Detector App In Google Play website. Available at: //play.google.com/store/apps/details?id = com.mobialia.colordetector. Last Access 30 Nov 2014Colorblind Assitant App. In Google Play website. Available at: https://play.google.com/store/apps/details?id=com.unclechromedome.colorblindassistant . Last Access 30 Nov 2014Dale O, Solheim I, Halbach T, Schulz T, Spiru L, Turcu I (2013) What seniors want in a mobile Help-On-Demand service. In proceedings of the Fifth International Conference on eHealth, Telemedicine, and Social Medicine (eTELEMED 2013). Feb. 24 – Mar. 1, 2013. Nice, France. (pp. 96–101)Estepa AJ, Estepa R, Vozmediano J, Carrillo P (2014) Dynamic VoIP codec selection on smartphones. Netw Protoc Algoritm 6(2):22–37Falk TH, Maier M (2013) Context awareness in WBANs: a survey on medical and non-medical applications. IEEE Wirel Commun 20(4):30–37Franco C, Fleury A, Guméry PY, Diot B, Demongeot J, Vuillerme N (2013) iBalance-ABF: a smartphone-based audio-biofeedback balance system. IEEE Trans Biomed Eng 60(1):211–215García M, Lloret J, Bellver I, Tomás J (2013) Intelligent IPTV Distribution for Smart Phones (Book Chapter 13). In Intelligent Multimedia Technologies for Networking Applications. IGI GlobalGregoski MJ, Mueller M, Vertegel A, Shaporev A, Jackson BB, Frenzel RM, Treiber FA (2012) Development and validation of a smartphone heart rate acquisition application for health promotion and wellness telehealth applications. Int J Telemed Appl 2012, 1. Article ID 696324Grimaldi D, Kurylyak Y, Lamonaca F, Nastro A (2011) Photoplethysmography detection by smartphone’s videocamera. In proceedings of the 6th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IEEE IDAACS 2011), Sep. 15–17, 2011. Prague, Czech Republic. (Vol. 1, pp. 488–491)Gurrin C, Qiu Z, Hughes M, Caprani N, Doherty AR, Hodges SE, Smeaton AF (2013) The smartphone as a platform for wearable cameras in health research. Am J Prev Med 44(3):308–313Haché G, Lemaire ED, Baddour N (2011) Wearable mobility monitoring using a multimedia smartphone platform. IEEE Trans Instrum Meas 60(9):3153–3161Heathers JA (2013) Smartphone-enabled pulse rate variability: an alternative methodology for the collection of heart rate variability in psychophysiological research. Int J Psychophysiol 89(3):297–304Hoseini-Tabatabaei SA, Gluhak A, Tafazolli R (2013) A survey on smartphone-based systems for opportunistic user context recognition. ACM Comput Surv (CSUR) 45(3):1–51, Paper No. 27Illiger K, Hupka M, von Jan U, Wichelhaus D, Albrecht UV (2014) Mobile technologies: expectancy, usage, and acceptance of clinical staff and patients at a University Medical Center. JMIR mHealth uHealth 2(4), e42Kanjo E (2012) Tools and architectural support for mobile phones based crowd control systems. Netw Protoc Algoritm 4(3):4–14Kawano Y, Yanai K (2014) FoodCam: a real-time food recognition system on a smartphone. Multimedia Tools and Applications,Published online:April 2014: 1–25Khan FH, Khan ZH (2010) A systematic approach for developing mobile information system based on location based services. Netw Protoc Algoritm 2(2):54–65Kochanov D, Jonas S, Hamadeh N, Yalvac E, Slijp H, Deserno TM (2014) Urban Positioning Using Smartphone-Based Imaging. In Bildverarbeitung für die Medizin, 2014: 186–191Kurniawan S (2008) Older people and mobile phones: a multi-method investigation. Int J Human-Comput Stud 66(12):889–901Lacuesta R, Lloret J, Sendra S, Peñalver L (2014) Spontaneous Ad Hoc mobile cloud computing network. Sci World J 2014:1–19Lakens D (2013) Using a Smartphone to measure heart rate changes during relived happiness and anger. IEEE Trans Affect Comput 5(3):217–226Larson EC, Goel M, Boriello G, Heltshe S, Rosenfeld M, Patel SN (2012) Spirosmart: using a microphone to measure lung function on a mobile phone, In proceedings of the 2012 ACM Conference on Ubiquitous Computing (UBIComp 2012). Sep. 05–08, 2012. Pittsburgh, USA. (pp. 280–289)Lee J, Reyes BA, McManus DD, Mathias O, Chon KH (2012) Atrial fibrillation detection using a smart phone. In proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2012). Aug.28-Sep.1, 2012. San Diego, (pp. 1177–1180)Lloret J, Garcia M, Bri D, Diaz JR (2009) A cluster-based architecture to structure the topology of parallel wireless sensor networks. Sensors (Basel) 9(12):10513–10544Lu H, Frauendorfer D, Rabbi M, Mast MS, Chittaranjan GT, Campbell AT, Gatica-Perez D, Choudhury T (2012) StressSense: detecting stress in unconstrained acoustic environments using smartphones. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (UBIComp 2012). Sep. 05–08, 2012. Pittsburgh, USA. (pp. 351–360)Macías E, Abdelfatah H, Suárez A, Cánovas A (2011) Full geo-localized mobile video in Android mobile telephones. Netw Protoc Algoritm 3(1):64–81Macias E, Lloret J, Suarez A, Garcia M (2012) Architecture and protocol of a semantic system designed for video tagging with sensor data in mobile devices. Sensors 12(2):2062–2087Macias E, Suarez A, Lloret J (2013) Mobile sensing systems. Sensors 13(12):17292–17321MedCam App. In Google Play website. Available at: https://play.google.com/store/apps/details?id=com.cupel.MedCam . Last Access 30 Nov 2014Monteiro DM, Rodrigues JJ, Lloret J, Sendra S (2014) A hybrid NFC–Bluetooth secure protocol for Credit Transfer among mobile phones. Secur Commun Netw 7(2):325–337Mosa ASM, Yoo I, Sheets L (2012) A systematic review of healthcare applications for smartphones. BMC Med Inform Decis Mak 12(1):67MyEarDroid App. In Google Play website. Available at: https://play.google.com/store/apps/details?id=com.tecnalia.health.myeardroid . Last Access 30 Nov 2014O’Grady MJ, Muldoon C, Dragone M, Tynan R, O’Hare GM (2010) Towards evolutionary ambient assisted living systems. J Ambient Intell Humaniz Comput 1(1):15–29Poppe R (2010) A survey on vision-based human action recognition. Image Vis Comput 28(6):976–990Quit Snoring App. In Google Play website. Available at: https://play.google.com/store/apps/details?id=com.ptech_hm.qs . Last Access 30 Nov 2014Rahman MA, Hossain MS, El Saddik A (2013) Context-aware multimedia services modeling: an e-Health perspective. Multimed Tools Appl 73(3):1147–1176Sendra S, Granell E, Lloret J, Rodrigues JJPC (2014) Smart collaborative mobile system for taking care of disabled and elderly people. Mob Netw Appl 19(3):287–302Smartphone Milestone: Half of Mobile Subscribers Ages 55+ Own Smartphones Mobile. Online report.(April 22,2014). In the Nielsen Company website. Available at: http://www.nielsen.com/us/en/insights/news/2014/smartphone-milestone-half-of-americans-ages-55-own-smartphones.html Last Access 25 Nov 2014Smith A (2013) Smartphone Ownership 2013. On-line Report June 5, 2013. In Pew Research Center’s Internet & American Life Project website. Available at: http://www.pewinternet.org/2013/06/05/smartphone-ownership-2013/ Last Access 25 Nov 2014SnoreClock App. In Google Play website. Available at: https://play.google.com/store/apps/details?id=de.ralphsapps.snorecontrol Last Access 30 Nov 2014Storf H, Kleinberger T, Becker M, Schmitt M, Bomarius F, Prueckner S (2009) An event-driven approach to activity recognition in ambient assisted living. Lect Notes Comput Sci 5859:123–132Su X, Tong H, Ji P (2014) Activity recognition with smartphone sensors. Tsinghua Sci Technol 19(3):235–249Tapu R, Mocanu B, Bursuc A, Zaharia T (2013) A smartphone-based obstacle detection and classification system for assisting visually impaired people. In proceedings of the 2013 I.E. International Conference on Computer Vision Workshops (ICCVW 2013). Dec. 2–8, 2013. Sydney, Australia. (pp. 444–451)The vOICe for Android App. In Google Play website. Available at: https://play.google.com/store/apps/details?id=vOICe.vOICe . Last Access 30 Nov 2014Tudzarov A, Janevski T (2011) Protocols and algorithms for the next generation 5G mobile systems. Netw Protoc Algoritm 3(1):94–114Tyagi A, Miller K, Cockburn M (2012) e-Health tools for targeting and improving melanoma screening: a review. J Skin Cancer 2012, Article ID 437502Voice Cam for Blind App. In Google Play website. Available at: https://play.google.com/store/apps/details?id=com.prod.voice.cam Last Access 30 Nov 2014Wadhawan T, Situ N, Rui H, Lancaster K, Yuan X, Zouridakis G (2011) Implementation of the 7-point checklist for melanoma detection on smart handheld devices. In proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (EMBC 2011). Aug. 30- Sep 03, 2011. Boston, MA, USA (pp. 3180–3183)Xiong H, Zhang D, Zhang D, Gauthier V (2012) Predicting mobile phone user locations by exploiting collective behavioral patterns. In proceedings of the 9th International Conference on Ubiquitous Intelligence & Computing and 9th International Conference on Autonomic & Trusted Computing (UIC/ATC). 4–7 Sept. 2012. Fukuoka, Japan. (pp. 164–171)Xu X, Shu L, Guizani M, Liu M, Lu J (2014) A survey on energy harvesting and integrated data sharing in wireless body area networks. Int J Distrib Sens Netw. Article ID 438695Yu W, Su X, Hansen J (2012) A smartphone design approach to user communication interface for administering storage system network. Netw Protoc Algoritm 4(4):126–155Zhang D, Vasilakos AV, Xiong H (2012) Predicting location using mobile phone calls. ACM SIGCOMM Comput Commun Rev 42(4):295–296Zhang D, Xiong H, Yang L, Gauither V (2013) NextCell: predicting location using social interplay from cell phone traces. EEE Trans Comput 64(2):452–46

An unsupervised behavioral modeling and alerting system based on passive sensing for elderly care

Artificial Intelligence in combination with the Internet of Medical Things enables remote healthcare services through networks of environmental and/or personal sensors. We present a remote healthcare service system which collects real-life data through an environmental sensor package, including binary motion, contact, pressure, and proximity sensors, installed at households of elderly people. Its aim is to keep the caregivers informed of subjects’ health-status progressive trajectory, and alert them of health-related anomalies to enable objective on-demand healthcare service delivery at scale. The system was deployed in 19 households inhabited by an elderly person with post-stroke condition in the Emilia–Romagna region in Italy, with maximal and median observation durations of 98 and 55 weeks. Among these households, 17 were multi-occupancy residences, while the other 2 housed elderly patients living alone. Subjects’ daily behavioral diaries were extracted and registered from raw sensor signals, using rule-based data pre-processing and unsupervised algorithms. Personal behavioral habits were identified and compared to typical patterns reported in behavioral science, as a quality-of-life indicator. We consider the activity patterns extracted across all users as a dictionary, and represent each patient’s behavior as a ‘Bag of Words’, based on which patients can be categorized into sub-groups for precision cohort treatment. Longitudinal trends of the behavioral progressive trajectory and sudden abnormalities of a patient were detected and reported to care providers. Due to the sparse sensor setting and the multi-occupancy living condition, the sleep profile was used as the main indicator in our system. Experimental results demonstrate the ability to report on subjects’ daily activity pattern in terms of sleep, outing, visiting, and health-status trajectories, as well as predicting/detecting 75% hospitalization sessions up to 11 days in advance. 65% of the alerts were confirmed to be semantically meaningful by the users. Furthermore, reduced social interaction (outing and visiting), and lower sleep quality could be observed during the COVID-19 lockdown period across the cohort