Abstract
Today, an increasing number of systems produce, process, and store personal and intimate data. Such data has plenty of potential for entirely new types of software applications, as well as for improving old applications, particularly in the domain of smart healthcare. However, utilizing this data, especially when it is continuously generated by sensors and other devices, with the current approaches is complex—data is often using proprietary formats and storage, and mixing and matching data of different origin is not easy. Furthermore, many of the systems are such that they should stimulate interactions with humans, which further complicates the systems. In this article, we introduce the Human Data Model—a new tool and a programming model for programmers and end users with scripting skills that help combine data from various sources, perform computations, and develop and schedule computer-human interactions. Written in JavaScript, the software implementing the model can be run on almost any computer either inside the browser or using Node.js. Its source code can be freely downloaded from GitHub, and the implementation can be used with the existing IoT platforms. As a whole, the work is inspired by several interviews with professionals, and an online survey among healthcare and education professionals, where the results show that the interviewed subjects almost entirely lack ideas on how to benefit the ever-increasing amount of data measured of the humans. We believe that this is because of the missing support for programming models for accessing and handling the data, which can be satisfied with the Human Data Model.
- A. Solanas, C. Patsakis, M. Conti, I. S. Vlachos, V. Ramos, F. Falcone, O. Postolache, et al. 2014. Smart health: A context-aware health paradigm within smart cities. IEEE Communications Magazine 52, 8 (Aug. 2014), 74--81. DOI:http://dx.doi.org/10.1109/MCOM.2014.6871673Google Scholar
Cross Ref
- G. D. Abowd. 2016. Beyond Weiser: From ubiquitous to collective computing. Computer 49, 1 (Jan. 2016), 17--23. DOI:http://dx.doi.org/10.1109/MC.2016.22Google Scholar
Digital Library
- Kim Mens, Rafael Capilla, Nicolás Cardozo, and Bruno Dumas. 2016. A taxonomy of context-aware software variability approaches. In Companion Proceedings of the 15th International Conference on Modularity (MODULARITY Companion’16). ACM, New York, NY, 119--124. DOI:http://dx.doi.org/10.1145/2892664.2892684Google Scholar
- Miguel Á. Conde and Ángel Hernández-García. 2019. Data driven education in personal learning environments—What about learning beyond the institution? International Journal of Learning Analytics and Artificial Intelligence for Education 1, 1 (2019), 43--57.Google Scholar
- Rolf Reber, Elizabeth A. Canning, and Judith M. Harackiewicz. 2018. Personalized education to increase interest. Current Directions in Psychological Science 27, 6 (2018), 449--454.Google Scholar
- Antero Taivalsaari and Tommi Mikkonen. 2017. A roadmap to the programmable world: Software challenges in the IoT era. IEEE Software 34, 1 (2017), 72--80.Google Scholar
Digital Library
- W. Shi and S. Dustdar. 2016. The promise of Edge computing. Computer 49, 5 (May 2016), 78--81. DOI:http://dx.doi.org/10.1109/MC.2016.145Google Scholar
Digital Library
- A. V. Dastjerdi and R. Buyya. 2016. Fog computing: Helping the Internet of Things realize its potential. Computer 49, 8 (Aug. 2016), 112--116. DOI:http://dx.doi.org/10.1109/MC.2016.245Google Scholar
- Bill Wasik. 2013. In the programmable world, all our objects will act as one. Wired. Retrieved August 14, 2019 from http://www.wired.com/2013/05/internet-of-things-2/.Google Scholar
- Polar AccessLink API. n.d. Home Page. Retrieved August 14, 2019 from https://www.polar.com/accesslink-api/?shellpolar-accesslink-api.Google Scholar
- Suunto. n.d. Suunto APIs. Retrieved August 14, 2019 from https://apizone.suunto.com/docs/services/.Google Scholar
- Garmin. n.d. Garmin APIs. Retrieved August 14, 2019 from https://developer.garmin.com.Google Scholar
- FitBot. n.d. FitBod Developers’ Guide. Retrieved August 14, 2019 from https://dev.fitbit.com/build/guides/.Google Scholar
- Developers. n.d. Wear OK Guide. Retrieved August 14, 2019 from https://developer.android.com/training/wearables/apps/creating.Google Scholar
- Google. n.d. Google Fit APIs. Retrieved August 14, 2019 from https://developers.google.com/fit/.Google Scholar
- João Santos, Joel J. P. C. Rodrigues, Bruno M. C. Silva, João Casal, Kashif Saleem, and Victor Denisov. 2016. An IoT-based mobile gateway for intelligent personal assistants on mobile health environments. Journal of Network and Computer Applications 71 (2016), 194--204.Google Scholar
Digital Library
- Sandro Pinto, Jorge Cabral, and T. Gomes. 2017. We-care: An IoT-based health care system for elderly people. In Proceedings of the IEEE International Conference on Industrial Technology (ICIT’17). IEEE, Los Alamitos, CA, 1378--1383.Google Scholar
- Luca Mainetti, Luigi Patrono, Andrea Secco, and Ilaria Sergi. 2016. An IoT-aware AAL system for elderly people. In Proceedings of the International Multidisciplinary Conference on Computer and Energy Science (SpliTech’16). IEEE, Los Alamitos, CA, 1--6.Google Scholar
- Amir-Mohammad Rahmani, Nanda Kumar Thanigaivelan, Tuan Nguyen Gia, Jose Granados, Behailu Negash, Pasi Liljeberg, and Hannu Tenhunen. 2015. Smart e-Health Gateway: Bringing intelligence to Internet-of-Things based ubiquitous healthcare systems. In Proceedings of the 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC’15). IEEE, Los Alamitos, CA, 826--834.Google Scholar
- Danan Thilakanathan, Shiping Chen, Surya Nepal, Rafael Calvo, and Leila Alem. 2014. A platform for secure monitoring and sharing of generic health data in the Cloud. Future Generation Computer Systems 35, Supplement C (2014), 102--113. DOI:http://dx.doi.org/10.1016/j.future.2013.09.011Google Scholar
- Jong Hyun Lim, Andong Zhan, Evan Goldschmidt, JeongGil Ko, Marcus Chang, and Andreas Terzis. 2012. HealthOS: A platform for pervasive health applications. In Proceedings of the 2nd ACM Workshop on Mobile Systems, Applications, and Services for HealthCare (mHealthSys’12). ACM, New York, NY, Article 4, 6 pages. DOI:http://dx.doi.org/10.1145/2396276.2396281Google Scholar
- M. Mazhar Rathore, Anand Paul, Awais Ahmad, Marco Anisetti, and Gwanggil Jeon. 2017. Hadoop-based Intelligent Care System (HICS): Analytical approach for Big Data in IoT. ACM Transactions on Internet Technology 18, 1 (Nov. 2017), Article 8, 24 pages. DOI:http://dx.doi.org/10.1145/3108936Google Scholar
Digital Library
- Gianpaolo Cugola and Alessandro Margara. 2012. Processing flows of information: From data stream to complex event processing. ACM Computing Surveys 44, 3 (2012), Article 15, 62 pages.Google Scholar
- Ching Yu Chen, Jui Hsi Fu, Today Sung, Ping-Feng Wang, Emery Jou, and Ming-Whei Feng. 2014. Complex event processing for the Internet of Things and its applications. In Proceedings of the 2014 IEEE International Conference on Automation Science and Engineering (CASE’14). 1144--1149. DOI:http://dx.doi.org/10.1109/CoASE.2014.6899470Google Scholar
- Denzil Ferreira, Vassilis Kostakos, and Anind K. Dey. 2015. AWARE: Mobile context instrumentation framework. Frontiers in ICT 2 (2015), 6.Google Scholar
Cross Ref
- Syed Monowar Hossain, Timothy Hnat, Nazir Saleheen, Nusrat Jahan Nasrin, Joseph Noor, Bo-Jhang Ho, Tyson Condie, Mani Srivastava, and Santosh Kumar. 2017. mCerebrum: A mobile sensing software platform for development and validation of digital biomarkers and interventions. In Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems. ACM, New York, NY, 7.Google Scholar
Digital Library
- Apache. 2011. Storm, Distributed and Fault-Tolerant Realtime Computation. Retrieved September 6, 2020 from https://storm.apache.org/.Google Scholar
- Apache. 2014. Apache Spark. Retrieved September 6, 2020 from https://spark.apache.org/.Google Scholar
- Manoj K. Garg, Duk-Jin Kim, Deepak S. Turaga, and Balakrishnan Prabhakaran. 2010. Multimodal analysis of body sensor network data streams for real-time healthcare. In Proceedings of the International Conference on Multimedia Information Retrieval (MIR’10). ACM, New York, NY, 469--478. DOI:http://dx.doi.org/10.1145/1743384.1743467Google Scholar
Digital Library
- David Bermbach, Frank Pallas, David García Pérez, Pierluigi Plebani, Maya Anderson, Ronen Kat, and Stefan Tai. 2017. A research perspective on Fog computing. In Proceedings of the 2nd Workshop on IoT Systems Provisioning and Management for Context-Aware Smart Cities.Google Scholar
- Flavio Bonomi, Rodolfo Milito, Jiang Zhu, and Sateesh Addepalli. 2012. Fog computing and its role in the Internet of Things. In Proceedings of the 1st Edition of the MCC Workshop on Mobile Cloud Computing. ACM, New York, NY, 13--16.Google Scholar
Digital Library
- Eemil Lagerspetz, Jonatan Hamberg, Xin Li, Huber Flores, Petteri Nurmi, Nigel Davies, and Sumi Helal. 2019. Pervasive data science on the Edge. IEEE Pervasive Computing 18, 3 (2019), 40--48.Google Scholar
Digital Library
- Apache. n.d. Home Page. Retrieved August 9, 2019 from https://kafka.apache.org/.Google Scholar
- David C. Luckham and Brian Frasca. 1998. Complex event processing in distributed systems. Computer Systems Laboratory Technical Report CSL-TR-98-754. Stanford University, Stanford 28 (1998), 16.Google Scholar
- Timo Aaltonen, Tommi Mikkonen, Heikki Peltola, and Arto Salminen. 2014. From mashup applications to open data ecosystems. In Proceedings of The International Symposium on Open Collaboration. ACM, 15.Google Scholar
Digital Library
- J. Guillen, J. Miranda, J. Berrocal, J. Garcia-Alonso, J. M. Murillo, and C. Canal. 2014. People as a service: A mobile-centric model for providing collective sociological profiles. Software, IEEE 31, 2 (Mar 2014), 48--53. DOI:http://dx.doi.org/10.1109/MS.2013.140Google Scholar
- James Scott, Jon Crowcroft, Pan Hui, and Christophe Diot. 2006. Haggle: A networking architecture designed around mobile users. In WONS 2006: Third Annual Conference on Wireless On-demand Network Systems and Services. 78--86.Google Scholar
- Karim Habak, Mostafa Ammar, Khaled A Harras, and Ellen Zegura. 2015. Femto Clouds: Leveraging mobile devices to provide Cloud service at the Edge. In 2015 IEEE 8th International Conference on Cloud Computing (CLOUD). IEEE, 9--16.Google Scholar
- Huber Flores, Rajesh Sharma, Denzil Ferreira, Vassilis Kostakos, Jukka Manner, Sasu Tarkoma, Pan Hui, and Yong Li. 2017. Social-aware hybrid mobile offloading. Pervasive and Mobile Computing 36 (2017), 25--43.Google Scholar
Digital Library
- U. Neisser. 1976. Cognition and Reality: Principles and Implications of Cognitive Psychology. W. H. Freeman.Google Scholar
- David Vernon. 2014. Artificial Cognitive Systems: A Primer. MIT Press.Google Scholar
- Eun Kyoung Choe, Bongshin Lee, et al. 2015. Characterizing visualization insights from quantified selfers’ personal data presentations. IEEE Computer Graphics and Applications 35, 4 (2015), 28--37.Google Scholar
Digital Library
- N. Mäkitalo, T. Aaltonen, M. Raatikainen, A. Ometov, S. Andreev, Y. Koucheryavy, and T. Mikkonen. 2019. Action-oriented programming model: Collective executions and interactions in the Fog. Journal of Systems and Software, in print (2019).Google Scholar
- Amit Sheth, Pramod Anantharam, and Cory Henson. 2013. Physical-cyber-social computing: An early 21st century approach. IEEE Intelligent Systems 28, 1 (2013), 78--82.Google Scholar
Digital Library
- Amit P. Sheth. 2010. Computing for human experience: Semantics-empowered sensors, services, and social computing on the ubiquitous Web. IEEE Internet Computing 14, 1 (2010), 88--91.Google Scholar
Digital Library
- Jing Zeng, Laurence T. Yang, Man Lin, Huansheng Ning, and Jianhua Ma. 2016. A survey: Cyber-physical-social systems and their system-level design methodology. Future Generation Computer Systems (2016).Google Scholar
- Zhong Liu, Dong-sheng Yang, Ding Wen, Wei-ming Zhang, and Wenji Mao. 2011. Cyber-physical-social systems for command and control. IEEE Intelligent Systems 26, 4 (2011), 92--96.Google Scholar
Digital Library
- Fei-Yue Wang. 2010. The emergence of intelligent enterprises: From CPS to CPSS. IEEE Intelligent Systems 25, 4 (2010), 85--88.Google Scholar
Digital Library
- Katrin Hänsel, Natalie Wilde, Hamed Haddadi, and Akram Alomainy. 2015. Challenges with current wearable technology in monitoring health data and providing positive behavioural support. In Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare. 158--161.Google Scholar
Digital Library
- Antonio J. Jara, Yann Bocchi, and Dominique Genoud. 2014. Social Internet of Things: The potential of the Internet of Things for defining human behaviours. In 2014 International Conference on Intelligent Networking and Collaborative Systems. IEEE, 581--585.Google Scholar
- Henry Friday Nweke, Ying Wah Teh, Ghulam Mujtaba, and Mohammed Ali Al-Garadi. 2019. Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions. Information Fusion 46 (2019), 147--170.Google Scholar
Digital Library
- Niko Mäkitalo, Timo Aaltonen, and Tommi Mikkonen. 2016. Coordinating proactive social devices in a mobile Cloud: Lessons learned and a way forward. In Proceedings of the International Conference on Mobile Software Engineering and Systems (MOBILESoft’16). ACM, New York, NY, USA, 179--188.Google Scholar
Index Terms
Human Data Model: Improving Programmability of Health and Well-Being Data for Enhanced Perception and Interaction
Recommendations
UbiPhone: Human-Centered Ubiquitous Phone System
Emerging rich wireless networking modalities facilitate the development of new intelligent, innovative services on smart phones. The authors propose a ubiquitous phone (UbiPhone) system that demonstrates innovative context-aware human-centric phone ...
Opportunistic Human Activity and Context Recognition
Achieving true ambient intelligence calls for a new opportunistic activity recognition paradigm in which, instead of deploying information sources for a specific goal, the recognition methods themselves dynamically adapt to available sensor data. The ...
Ubiquitous Computing: Are We There Yet?
The widespread deployment of technologies like mobile phones continues to drive new applications and to open research opportunities.






Comments