Author(s):
- Celementina R. Russo
Abstract:
Over the course of this decade, both mobile devices and mobile device applications designed for the express purpose of tracking physiological and cognitive performance metrics are nearly ubiquitous in our everyday endeavors. Though the idea of tracking our own various daily metrics is nothing new, the advent of technological innovations that carry the capacity to store, sort and share these ever-accruing amounts of data presents to us unchartered territory regarding our relationship to and understanding and interpretation of these metrics both singularly (as snapshots) and in the aggregate (over time). This work explores the form and function of the Quantified Self Movement. It discusses the current state of analyzing and interpreting information accrued from various metrics, specifically the issues that arise in synchronizing heterogeneous metrics, and in the context of an AugCog framework, proposes a method of approach to analyze multivariate data by curation based on the simultaneity of measured events.
Documentation:
https://doi.org/10.1007/978-3-319-20816-9_49
References:
1.Cummings, M.: Technology impedances to augmented cognition. Ergonomics in Design (Views & Provocations), 25–27 (2010)Google Scholar
2.Hurley, D.: Wired: Diabetes patients are hacking their way toward a bionic pancreas (2014). http://www.wired.com/2014/12/diabetes-patients-hacking-together-diy-bionic-pancreases/
3.Fox, S., Duggan, M.: Tracking for health (2013). http://www.pewinternet.org/2013/01/28/tracking-for-health/
4.Gevins, A., Smith, M.: Neurophysiological measures of cognitive workload during human-computer interaction. Theor. Issues in Ergon. Sci. 4, 113–131 (2003)CrossRefGoogle Scholar
5.Ikehara, C., Crosby, M.: Assessing cognitive load with physiological sensors. In: Proceedings of the 38th Hawaii International Conference on System Sciences – 2005 (2005)Google Scholar
6.Lewington, L.: BBC: Why activity trackers deliver mismatched fitness data (2015). http://www.bbc.com/news/technology-31113602
7.Life Stream (2013). http://lifestreamblog.com/lifelogging
8.Hoskins, M.: Healthline: newsflash: Dexcom SHARE gets FDA clearance! (2015). http://www.healthline.com/diabetesmine/newsflash-dexcom-share-gets-fda-clearance/
9.Marshall, S.P.: The index of cognitive activity: measuring cognitive workload. In: IEEE 7 Human Factors Meeting, Scottsdale, Arizona, pp. 7–5–7–9, August 2002Google Scholar
10.Matsangas, P., McCauley, M.: Sopite syndrome: a revised definition. Aviati. Space Environ. Med. 85, 672–673 (2014)CrossRefGoogle Scholar
11.Matsangas, P., McCauley, M.: Yawning as a behavioral marker of mild motion sickness and sopite syndrome. Aviati. Space Environ. Med. 85, 658–661 (2014)CrossRefGoogle Scholar
12.Matthews, R., McDonald, N., Hervieux, P., Turner, P., Steindorf, M.: A wearable physiological sensor suite for unobtrusive monitoring of physiological and cognitive state. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2007, pp. 5276–5281, August 2007Google Scholar
13.Matthews, R., Turner, P., McDonald, N., Ermolaev, K., Manus, T., Shelby, R., Steindorf, M.: Real time workload classification from an ambulatory wireless eeg system using hybrid eeg electrodes. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2008, pp. 5871–5875, August 2008Google Scholar
14.Mehler, B., Reimer, B., Coughlin, J.: Sensitivity of physiological measures for detecting systematic variations in cognitive demand from a working memory task: An on-road study across three age groups. Hum. Factors 54, 396–412 (2012)CrossRefGoogle Scholar
15.Orbach, T.: Methods and systems for physiological and psycho-physiological monitoring and uses thereof, U.S. Patent App. 11/884,775, 4 September 2008. http://www.google.com/patents/US20080214903
16.Paulus, M., Stein, M.: Interoception in anxiety and depression. Brain Struct. Funct. 214, 451–463 (2010)CrossRefGoogle Scholar
17.Ryu, K., Ayung, M.: Evaluation of mental workload with a combined measure based on physiological indices during a dual task of tracking and mental arithmetic. Int. J. Industr. Ergon. 35, 991–1009 (2005)CrossRefGoogle Scholar
18.Skinner, A., Russo, C., Baraniecki, L., Maloof, M.: Ubiquitous augmented cognition (2014). http://2014.hci.international/index.php
19.Swan, M.: Sensor mania! the internet of things, wearable computing, objective metrics, and the quantified self 2.0. J. Sensor. Actuator Netw. 1, 217–253 (2012)CrossRefGoogle Scholar
20.The BodyTrack Team (2015). http://fluxstream.org
21.Tomaka, J., Blascovitch, J., KIbler, J., Earnst, J.: Cognitive and physiological antecedents of of threat and challenge appraisal. J. Personailty Soc. Psychol. 1, 63–72 (1997)CrossRefGoogle Scholar
22.Weintraub, K.: Quantified self: The tech based route to a better life? (2013). http://www.bbc.com/future/story/20130102-self-track-route-to-a-better-life
23.Widerhiold, B., Jang, D., Kim, S., Wiederhold, M.D.: Physiological monitoring as an objective tool in virtual reality therapy. Cyberpychology Behav. 5, 77–82 (2002)CrossRefGoogle Scholar
24.Winzce, J., Hoon, P., Soon, E.: Sexual arousal in women: a comparison of cognitive and physiological responses by continuous measurement. Arch. Sex Behav. 2, 121–133 (1977)Google Scholar
25.Winzce, J., Vendetti, E., Barlow, D., Mavissakalian, M.: The effects of a subjective monitoring task in the physiological measure of genital response to erotic stimulation. Arch. Sex Behav. 9, 533–545 (1980)CrossRefGoogle Scholar
26.Zaki, J., Davis, J., Ochsner, K.: Overlapping activity in anterior insult during interoception and emotional experience. NeuroImage 62, 493–499 (2012)CrossRefGoogle Scholar