I always harbored two assumptions about the development of physiological computing systems that have only become apparent (to me at least) as technological innovation seems to contradict them.\u00a0 First of all, I thought nascent forms of physiological computing systems would be developed for desktop system where the user stays in a stationary and more-or-less sedentary position, thus minimising the probability of movement artifacts.\u00a0 Also, I assumed that physiological computing devices would only ever be achieved as coordinated holistic systems.\u00a0 In other words, specific sensors linked to a dedicated controller that provides input to adaptive software, all designed as a seamless chain of information flow.<\/p>\n
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The rise of mobile devices and explosion of apps has changed my mind on both counts.\u00a0 No small amount of ingenuity has turned rudimentary sensors on mobile devices into fledgling forms of physiological monitoring.\u00a0 Take iStethoscope<\/a> for example, which uses the iPhone’s inbuilt microphone to generate an ECG trace based on heart sounds.\u00a0 Alternatively, Instant Heart Rate<\/a> is an Android-based app that has made a very novel use of the camera; the user places her thumb over the camera and small changes in skin colour (associated with oxygenated blood) are used to determine heart rate.\u00a0 The adaptation of the camera and microphone that come as standard on mobile devices is not an answer in the long-term, but does make one wonder what kind of apps may be developed if a dedicated heart rate sensor were to be built into a mobile device.\u00a0 This is obviously a development under consideration at Apple who filed a patent for embedded heart rate assessment<\/a> earlier this year.<\/p>\n The probable introduction of physiological computing apps on a mobile device blows my first assumption out of the water – namely that systems would target users likely to be in stable and sedentary positions.\u00a0 The momentum seems to be in the opposite direction, to develop\u00a0 physiological monitoring apps as part of an exercise regime.\u00a0 In hindsight, my bias may be created by the fact that I’m a psychologist interested in physiological activity as representative of psychological states, and therefore, physical activity tends to create noise as far as I’m concerned.\u00a0 However, what physiological computing and mobile devices both offer is continuous and unobtrusive recordings.\u00a0 From the perspective of body blogging for various applications, the intensive data collection that may be demanded by this\u00a0 technology and pervasiveness of the hardware are a perfect fit.<\/p>\n My second assumption about dedicated and integrated platforms for physiological computing systems does not fit with opportunistic, niche-seeking character of app development.\u00a0 As we’ve already seen, physiological data collection can be achieved in a number of ways and once these data are stored on a mobile device, it can be used to ‘feed’ any number of software systems, from health monitoring to mobile gaming.<\/p>\n I believe the nature of app development means that a full biocybernetic loop (the cycle of information flow from data collection to software adaptation) will be achieved in a piecemeal fashion.\u00a0 One app to collect raw data, another one to do the relevant data analysis and a third to serve as a platform for software adaptation.\u00a0 For example, imagine an iPhone equipped with embedded heart rate monitoring – a data collection app would collect a raw ECG trace, a second app would extract a desired measure (e.g. heart rate, heart rate variability), whilst correcting for physical activity and artifacts in the data.\u00a0 This clean data would be passed on to a revamped version of iTunes that monitored your heart rate responses to every song played; the purpose of which would be to automatically create a ‘chill out’ playlist of tunes that slow your heart rate – that you could activate in stressful times.<\/p>\n This is just one example of how apps could function as modular building blocks to create physiological computing loops on mobile devices and across different hardware platforms.\u00a0 The data provided by pervasive physiological monitoring has the potential to function as a common (information) currency across a range of related apps.<\/p>\n","protected":false},"excerpt":{"rendered":" I always harbored two assumptions about the development of physiological computing systems that have only become apparent (to me at least) as technological innovation seems to contradict them.\u00a0 First of all, I thought nascent forms of physiological computing systems would be developed for desktop system where the user stays in a stationary and more-or-less sedentary […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spay_email":""},"categories":[5,6],"tags":[24,34,59,73,86],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/pY315-b2","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/posts\/684"}],"collection":[{"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=684"}],"version-history":[{"count":1,"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/posts\/684\/revisions"}],"predecessor-version":[{"id":4723,"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/posts\/684\/revisions\/4723"}],"wp:attachment":[{"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=684"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=684"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=684"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}