Advancing Quantified-Self Applications Utilizing Visual Data Analytics and the Internet of Things

The exponential growth of the number and variety of IoT devices and applications for personal use, as well as the improvement of their quality and performance, facilitates the realization of intelligent eHealth concepts. Nowadays, it is easier than ever for individuals to monitor themselves, quantify and log their everyday activities in order to gain insights about their body performance and receive recommendations and incentives to improve it. Of course, in order for such systems to live up to the promise, given the treasure trove of data that is collected, machine learning techniques need to be integrated in the processing and analysis of the data. This systematic and automated quantification, logging and analysis of personal data, using IoT and AI technologies, has given birth to the phenomenon of Quantified-Self. This work proposes a prototype decentralized Quantified-Self application, built on top of a dedicated IoT gateway, that aggregates and analyses data from multiple sources, such as biosignal sensors and wearables, and performs analytics and visualization on it.

[1]  Michael J. McGrath,et al.  Wellness, Fitness, and Lifestyle Sensing Applications , 2013 .

[2]  Andreas Menychtas,et al.  Automated integration of wireless biosignal collection devices for patient-centred decision-making in point-of-care systems. , 2016, Healthcare technology letters.

[3]  Atsushi Mitani,et al.  Development of a wearable tele-monitoring system with IoT for bio-medical applications , 2016, 2016 IEEE 5th Global Conference on Consumer Electronics.

[4]  Konrad Tollmar,et al.  Health Mashups: Presenting Statistical Patterns between Wellbeing Data and Context in Natural Language to Promote Behavior Change , 2013, TCHI.

[5]  Christoph Sorge,et al.  Privacy Challenges in the Quantified Self Movement – An EU Perspective , 2016, Proc. Priv. Enhancing Technol..

[6]  Marimuthu Palaniswami,et al.  EHOPES: Data-centered Fog platform for smart living , 2015, 2015 International Telecommunication Networks and Applications Conference (ITNAC).

[7]  Z. Daniil,et al.  Utilizing a Homecare Platform for Remote Monitoring of Patients with Idiopathic Pulmonary Fibrosis. , 2017, Advances in experimental medicine and biology.

[8]  R. Simpson,et al.  Bringing Care Home: How Telemonitoring Can Expand Population Health Management Beyond the Hospital , 2014, Nursing administration quarterly.

[9]  George R. Milne,et al.  Internet of Things: Convenience vs. privacy and secrecy , 2015 .

[10]  Vyacheslav Kharchenko,et al.  SMART-ITEM: IoT-enabled smart living , 2016, 2016 Future Technologies Conference (FTC).

[11]  Athanasios V. Vasilakos,et al.  Data Mining for the Internet of Things: Literature Review and Challenges , 2015, Int. J. Distributed Sens. Networks.

[12]  E. Mackensen,et al.  Bluetooth Low Energy (BLE) based wireless sensors , 2012, 2012 IEEE Sensors.

[13]  Tom Fawcett.  Mining the Quantified Self: Personal Knowledge Discovery as a Challenge for Data Science , 2015, Big Data.

[14]  Melanie Swan,et al.  The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery , 2013, Big Data.

[15]  Andreas Menychtas,et al.  A Versatile Architecture for Building IoT Quantified-Self Applications , 2017, 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS).

[16]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[17]  Wanda Pratt,et al.  Understanding quantified-selfers' practices in collecting and exploring personal data , 2014, CHI.

[18]  Sean A. Munson,et al.  Exploring goal-setting, rewards, self-monitoring, and sharing to motivate physical activity , 2012, 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.