Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0

The number of devices on the Internet exceeded the number of people on the Internet in 2008, and is estimated to reach 50 billion in 2020. A wide-ranging Internet of Things (IOT) ecosystem is emerging to support the process of connecting real-world objects like buildings, roads, household appliances, and human bodies to the Internet via sensors and microprocessor chips that record and transmit data such as sound waves, temperature, movement, and other variables. The explosion in Internet-connected sensors means that new classes of technical capability and application are being created. More granular 24/7 quantified monitoring is leading to a deeper understanding of the internal and external worlds encountered by humans. New data literacy behaviors such as correlation assessment, anomaly detection, and high-frequency data processing are developing as humans adapt to the different kinds of data flows enabled by the IOT. The IOT ecosystem has four critical functional steps: data creation, information generation, meaning-making, and action-taking. This paper provides a comprehensive review of the current and rapidly emerging ecosystem of the Internet of Things (IOT).

[1]  Peter Norvig,et al.  The Unreasonable Effectiveness of Data , 2009, IEEE Intelligent Systems.

[2]  Begoña García Zapirain,et al.  A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee , 2012, Sensors.

[3]  K. Fujihara,et al.  High normal HbA1c levels were associated with impaired insulin secretion without escalating insulin resistance in Japanese individuals: the Toranomon Hospital Health Management Center Study 8 (TOPICS 8) , 2012, Diabetic medicine : a journal of the British Diabetic Association.

[4]  Rosalind W. Picard,et al.  A Wearable Sensor for Unobtrusive, Long-Term Assessment of Electrodermal Activity , 2010, IEEE Transactions on Biomedical Engineering.

[5]  Vladislav Kantchev Shunturov,et al.  Dormitory residents reduce electricity consumption when exposed to real‐time visual feedback and incentives , 2007 .

[6]  J A Bilello,et al.  Assessment of a multi-assay, serum-based biological diagnostic test for major depressive disorder: a Pilot and Replication Study , 2013, Molecular Psychiatry.

[7]  Marshall F Chalverus,et al.  The Black Swan: The Impact of the Highly Improbable , 2007 .

[8]  Melanie Swan,et al.  Multigenic condition risk assessment in direct-to-consumer genomic services , 2010, Genetics in Medicine.

[9]  M. Swan Health 2050: The Realization of Personalized Medicine through Crowdsourcing, the Quantified Self, and the Participatory Biocitizen , 2012, Journal of personalized medicine.

[10]  Marc'Aurelio Ranzato,et al.  Building high-level features using large scale unsupervised learning , 2011, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  C. Roberts,et al.  Foundation , 2000, The Fairchild Books Dictionary of Fashion.

[12]  Yi Lu,et al.  Using personal glucose meters and functional DNA sensors to quantify a variety of analytical targets. , 2011, Nature chemistry.

[13]  M. Newborough,et al.  Dynamic energy-consumption indicators for domestic appliances: environment, behaviour and design , 2003 .

[14]  Angela Kong,et al.  Self-monitoring and eating-related behaviors are associated with 12-month weight loss in postmenopausal overweight-to-obese women. , 2012, Journal of the Academy of Nutrition and Dietetics.

[15]  Erez Lieberman Aiden,et al.  Quantitative Analysis of Culture Using Millions of Digitized Books , 2010, Science.

[16]  Lars Kai Hansen,et al.  Smartphones Get Emotional: Mind Reading Images and Reconstructing the Neural Sources , 2011, ACII.

[17]  Tracey Neithercott Continuous glucose monitors. , 2011, Diabetes forecast.

[18]  D. Hoak,et al.  Pilot Evaluation of Energy Savings from Residential Energy Demand Feedback Devices , 2008 .