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

Feel the Moosic: Emotion-based Music Selection and Recommendation

Digital transformation has changed all aspects of life, including the music market and listening habits. The spread of mobile devices and music streaming services has enabled the possibility to access a huge selection of music regardless of time or place. However, this access leads to the customer\u27s problem of choosing the right music for a certain situation or mood. The user is often overwhelmed while choosing music. Context information, especially the emotional state of the user, can help within this process. The possibilities of an emotional music selection are currently limited. The providers rely on predefined playlists for different situations or moods. However, the problem with these lists is, that they do not adapt to new user conditions. A simple, intuitive and automatic emotion-based music selection has so far been poorly investigated in IS practice and research. This paper describes the IS music research project Moosic , which investigates and iteratively implements an intuitive emotion-based music recommendation application. In addition, an initial evaluation of the prototype will be discussed and an outlook on further development will be given

An m-health application for cerebral stroke detection and monitoring using cloud services

[EN] Over 25 million people suffered from cerebral strokes in a span of 23 years. Many systems are being developed to monitor and improve the life of patients that suffer from different diseases. However, solutions for cerebral strokes are hard to find. Moreover, due to their widespread utilization, smartphones have presented themselves as the most appropriate devices for many e-health systems. In this paper, we propose a cerebral stroke detection solution that employs the cloud to store and analyze data in order to provide statistics to public institutions. Moreover, the prototype of the application is presented. The three most important symptoms of cerebral strokes were considered to develop the tasks that are conducted. Thus, the first task detects smiles, the second task employs voice recognition to determine if a sentence is repeated correctly and, the third task determines if the arms can be raised. Several tests were performed in order to verify the application. Results show its ability to determine whether users have the symptoms of cerebral stroke or not.This work has been partially supported by the pre-doctoral student grant "Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2014)" by the "Ministerio de Educacion, Cultura y Deporte", with reference: FPU14/02953.García-García, L.; Tomás Gironés, J.; Parra-Boronat, L.; Lloret, J. (2019). An m-health application for cerebral stroke detection and monitoring using cloud services. International Journal of Information Management. 45:319-327. https://doi.org/10.1016/j.ijinfomgt.2018.06.004S3193274

Effectiveness of Music-Based Respiratory Biofeedback in Reducing Stress during Visually Demanding Tasks

Biofeedback techniques have shown to be effective to manage stress and improve task performance. Biofeedback generally can be divided into two steps (i) measuring physiological functions (e.g. respiration, heart rate) via sensors and (ii) conveying the physiological signals to the user to improve self-awareness. Current systems require costly and invasive sensors to measure physiology, which are not comfortable and are not readily accessible to the general population. Additionally, current feedback mechanisms may be physically unpleasant or may hinder multitasking, especially in visually-demanding environments. To overcome these problems, we developed two tools: a music-based biofeedback tool that uses music as the medium of feedback, and a tool to measure breathing rate using a smartphone camera. The music biofeedback tool encourages slow breathing by adjusting the quality of the music in response to the user’s breathing rate. This intervention combines the benefits of biofeedback and music to help users regulate their stress response while performing a visual task (driving a car simulator). We evaluate the intervention on a 2×2 design with music and auditory biofeedback as independent variables. Our results indicate that music-biofeedback leads to lower arousal (as measured by electrodermal activity and heart rate variability) than music alone, auditory biofeedback alone, and a control condition. Music biofeedback also reduces driving errors when compared to the other three conditions. While our results suggest that the music-based biofeedback tool is useful and enjoyable, it still requires expensive physiological sensors which are intrusive in nature. Hence, we present a second tool to measure breathing rate in real-time via smartphone camera, which makes it easily accessible given the pervasiveness of smartphones. Our algorithm measures breathing rate by obtaining the photoplethysmographic signal and performing spectral analysis using Goertzel algorithm. We validated the method under a range of controlled breathing rate conditions, and our results show a high degree of agreement between our estimates and ground truth measurements obtained via standard respiratory sensors. These results show that it is possible to accurately compute breathing rate in real-time using a smartphone

Noncontact measurement of emotional and physiological changes in heart rate from a webcam

Heart rate, measured in beats per minute (BPM), can be used as an index of an individual's physiological state. Each time the heart beats, blood is expelled and travels through the body. This blood flow can be detected in the face using a standard webcam that is able to pick up subtle changes in color that cannot be seen by the naked eye. Due to the light absorption spectrum of blood, we are able to detect differences in the amount of light absorbed by the blood traveling just below the skin (i.e., photoplethysmography). By modulating emotional and physiological stress -- i.e., viewing arousing images and sitting vs. standing, respectively -- to elicit changes in heart rate, we explored the feasibility of using a webcam as a psychophysiological measurement of autonomic activity. We found a high level of agreement between established physiological measures, electrocardiogram (ECG), and blood pulse oximetry, and heart rate estimates obtained from the webcam. We thus suggest webcams can be used as a non-invasive and readily available method for measuring psychophysiological changes, easily integrated into existing stimulus presentation software and hardware setups

A Survey on Human Emotion Recognition Approaches, Databases and Applications

This paper presents the various emotion classification and recognition systems which implement methods aiming at improving Human Machine Interaction. The modalities and approaches used for affect detection vary and contribute to accuracy and efficacy in detecting emotions of human beings. This paper discovers them in a comparison and descriptive manner. Various applications that use the methodologies in different contexts to address the challenges in real time are discussed. This survey also describes the databases that can be used as standard data sets in the process of emotion identification. Thus an integrated discussion of methods, databases used and applications pertaining to the emerging field of Affective Computing (AC) is done and surveyed.This paper presents the various emotion classification and recognition systems which implement methods aiming at improving Human Machine Interaction. The modalities and approaches used for affect detection vary and contribute to accuracy and efficacy in detecting emotions of human beings. This paper discovers them in a comparison and descriptive manner. Various applications that use the methodologies in different contexts to address the challenges in real time are discussed. This survey also describes the databases that can be used as standard data sets in the process of emotion identification. Thus an integrated discussion of methods, databases used and applications pertaining to the emerging field of Affective Computing (AC) is done and surveyed

Smartphone Plastic Optical Fiber Sensors

Telehealth is quickly becoming an essential tool in delivering medical care. It can easily be used to monitor the states of patients who are located at remote locations away from hospitals. For example, breathing rate is one of important physiological parameters requiring monitoring, since it can be used in the diagnosis of respiratory diseases. However, the tools of remote monitoring have to be cheap and easy in use. These requirements can be satisfied by smartphone sensor based on plastic optical fiber (POF). The proposed solution is an all-fiber sensor where the flashlight acts as a light source and the camera acts as a photodetector. First of all, smartphones have become ubiquitous. POF, on the other hand, is not expensive. Hence, the proposed combination can be a cost-effective solution for implementing telehealth. In the work, the technique of intensity modulation in POF is adapted for sensing breathing rate. The measurements are analyzed in both time and frequency domains. In addition, multiplexing is also a promising direction for conducting sensing in optical fibers, since it can be used to measure multiple parameters. The possibility of implementing it in POF will be considered as well

Physiological Correlates of Volunteering

We review research on physiological correlates of volunteering, a neglected but promising research field. Some of these correlates seem to be causal factors influencing volunteering. Volunteers tend to have better physical health, both self-reported and expert-assessed, better mental health, and perform better on cognitive tasks. Research thus far has rarely examined neurological, neurochemical, hormonal, and genetic correlates of volunteering to any significant extent, especially controlling for other factors as potential confounds. Evolutionary theory and behavioral genetic research suggest the importance of such physiological factors in humans. Basically, many aspects of social relationships and social activities have effects on health (e.g., Newman and Roberts 2013; Uchino 2004), as the widely used biopsychosocial (BPS) model suggests (Institute of Medicine 2001). Studies of formal volunteering (FV), charitable giving, and altruistic behavior suggest that physiological characteristics are related to volunteering, including specific genes (such as oxytocin receptor [OXTR] genes, Arginine vasopressin receptor [AVPR] genes, dopamine D4 receptor [DRD4] genes, and 5-HTTLPR). We recommend that future research on physiological factors be extended to non-Western populations, focusing specifically on volunteering, and differentiating between different forms and types of volunteering and civic participation