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

Conception and Implementation of a Location-based Augmented Reality Kernel

The availability of sophisticated mobile applications on many platforms constitutes a challenging task. In order to cover the most relevant mobile operating systems and make the best use of their underlying features, the native development on the target platform still others the most diverse possibilities. Aside from the most widely spread mobile operating systems - namely Android and iOS - the Windows Phone platform oters a unique design language and many developer tools and technologies for building Windows Store apps. Making use of the capabilities of modern smartphones enables the development and use of desktop-like applications. The built-in sensors, cameras and powerful processing units of such a device other a versatile platform to build against. As a result, many mobile applications and technologies have emerged. However, information on profound insight into the development of such an application is hard to find. In this work, the development of AREA on the Windows Phone 8.1 platform is presented. AREA is a location-based mobile Augmented Reality engine and already available on Android and iOS. By porting the engine to yet another mobile platform, more third-party mobile business applications can integrate AREA and make use of its efficient and modular design. This work also points out the differences in implementation between the Windows Phone version and its counterparts on Android and iOS. Insights into the architecture and some references to the mathematical basis are also provided

Touch-Enhanced Gesture Control Scheme

We present an approach for improving gesture control by combining it with touch input to address a key shortcoming of gesture live mic syndrome by using touch- screen commands as a virtual clutch. The touch-enhanced gesture control scheme is designed and developed using a generic smartphone. For performance evaluation, this scheme was compared to the commercially available Myo armband device. Two tasks designed to measure selection accuracy and speed in a within-subject user study (n=30) reveal our touch-enhanced control scheme is faster and more accurate when executing selection commands. Additionally, qualitative results from a post-study questionnaire showed a majority of participants selected the touch-enhanced as easier to use over the Myo.M.S., Digital Media -- Drexel University, 201

Algorithms for Constructing Vehicle Trajectories in Urban Networks Using Inertial Sensors Data from Mobile Devices

Vehicle trajectories are an important source of information for estimating traffic flow characteristics. Lately, several studies have focused on identifying a vehicle’s trajectory in traffic network using data from mobile devices. However, these studies predominantly employed GPS coordinate information for tracking a vehicle’s speed and position in the transportation network. Considering the known limitations of GPS, such as, connectivity issues at urban canyons and underpasses, low precision of localization, high power consumption of device while GPS is in use, this research focuses on developing alternate methods for identifying a vehicle’s trajectory at an intersection and at a urban grid network using sensor data other than GPS in order to minimize GPS dependency. In particular, accelerometer and gyroscope data collected using smartphone’s inertial sensors, and speed data collected using an on-board diagnostics (OBD) device, are utilized to develop algorithms for maneuver (i.e., left/right turn and through), trip direction, and trajectory identification. Different algorithms using threshold of gyroscope and magnetometer readings, and machine learning techniques such as k-medoids clustering and dynamic time warping are developed for maneuver identification and their accuracy is tested on collected field data. It is found that, clustering based on maximum and minimum value of gyroscope readings is effective for maneuver identification. For trip direction identification at an intersection, two different methods are developed and tested. The first method utilizes accelerometer, gyroscope and OBD speed data, and the 2nd method employs magnetometer and acceleration data. The results demonstrate that the developed method using accelerometer, gyroscope and OBD speed data are effective in identifying a vehicle’s direction. An effective algorithm is developed using OBD speed information, maneuver and trip direction identification algorithms to identify vehicle’s trajectory at a grid network. Techniques for noise removal and orientation correction to transfer the raw data from phone’s local coordinate to global coordinate system are also demonstrated. Overall, this research eliminates the need for continuous GPS connectivity for trajectory identification. This research can be incorporated in methods developed by researchers to estimate traffic flow, delays, and queue lengths at intersections. This information can lead to better signal timings, travel recommendations, and traffic updates

Indoor tracking from multidimensional sensor data

Tracking the position of people or vehicles in large indoor settings with high accuracy is still a challenge despite the significant progress observed in indoor positioning technology in the last decade. To date, there is not a clearly dominant indoor positioning solution for general use, and challenges related to seamless indoor-outdoor positioning, reliable floor estimation and indoor maps are still needing more research. In this context, the IPIN 2016 conference is promoting a competition to evaluate a set of competing indoor positioning solutions in a realistic scenario. This paper describes the proposal of the UMINHO team and some of the obtained results.This work has been supported by COMPETE: POCI-01- 0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013

Road Maintenance through Machine Learning

This thesis explores the use of machine learning techniques for road infrastructure maintenance. We propose an innovative machine learning-based approach to improve the efficiency and effectiveness of road maintenance strategies. The focal point of this investigation is the development and implementation of a machine learning framework to enhance road quality monitoring. We use Long Short-Term Memory (LSTM) networks to accurately predict future road conditions and identify potential areas requiring maintenance before significant deterioration occurs. This predictive approach is designed to enable a shift from reactive to proactive road maintenance, optimizing the use of limited resources and improving overall road safety. The methodology of the research is structured in three phases: the creation of a prototype system for road condition data collection, the application of LSTM networks for predictive analysis, and the utilization of optimization techniques to guide effective maintenance decisions. By focusing on predictive accuracy and the strategic allocation of maintenance efforts, the study seeks to extend the lifespan of road infrastructure, reduce maintenance costs, and enhance the driving experience. This thesis is a contribution to the field of road infrastructure maintenance by introducing a predictive maintenance model that leverages advanced machine learning techniques. It aims to transform the traditional maintenance approach, providing a scalable and efficient solution to road infrastructure management challenges, with the potential to significantly influence policy and practice in infrastructure maintenance.KEYWORDS: Machine learning; Infrastructure maintenance; Proactive maintenanc

Walkcompass: Finding Walking Direction Leveraging Smartphone\u27s Inertial Sensors

Determining moving direction with smartphone\u27s inertial sensors is a well known problem in the field of location service. Compass alone cannot solve this problem because smartphone\u27s compass cannot achieve high accuracy. Moreover GPS is not suitable in indoor scenario. Another well known approach is dead-reckoning but dead-reckoning needs to know phones initial orientation and over time it keeps accumulating errors and after some time the estimation becomes to noisy to use. To overcome these limitations, we propose a solution called WalkCompass which is specially designed for pedestrians keeping in mind the variation of force during normal human walk. Therefore the algorithm is inherently free from any error generated by the orientation of the phone. However, the performance of the system does not depend on the holding style or location of the phone in the body. The algorithm can work very fast to determine the direction of movement in real time and because of its low complexity the complete system can be implemented on a smartphone. WalkCompass does not need any bootstrapping and can produce results with the granularity of each step of a walk

Context Mining with Machine Learning Approach: Understanding, Sensing, Categorizing, and Analyzing Context Parameters

Context is a vital concept in various fields, such as linguistics, psychology, and computer science. It refers to the background, environment, or situation in which an event, action, or idea occurs or exists. Categorization of context involves grouping contexts into different types or classes based on shared characteristics. Physical context, social context, cultural context, temporal context, and cognitive context are a few categories under which context can be divided. Each type of context plays a significant role in shaping our understanding and interpretation of events or actions. Understanding and categorizing context is essential for many applications, such as natural language processing, human-computer interaction, and communication studies, as it provides valuable information for interpretation, prediction, and decision-making. In this paper, we will provide an overview of the concept of context and its categorization, highlighting the importance of context in various fields and applications. We will discuss each type of context and provide examples of how they are used in different fields. Finally, we will conclude by emphasizing the significance of understanding and categorizing context for interpretation, prediction, and decision-making