{"id":260,"date":"2009-12-09T10:20:33","date_gmt":"2009-12-09T10:20:33","guid":{"rendered":"http:\/\/www.physiologicalcomputing.net\/wordpress\/?p=260"},"modified":"2021-12-22T20:22:10","modified_gmt":"2021-12-22T20:22:10","slug":"categories-of-physiological-computing","status":"publish","type":"post","link":"https:\/\/www.physiologicalcomputing.net\/?p=260","title":{"rendered":"Categories of Physiological Computing"},"content":{"rendered":"<p>In my last post I articulated a concern about how the name adopted by this field may drive the research in one direction or another.\u00a0 I&#8217;ve adopted the Physiological Computing (PC) label because it covers the widest range of possible systems.\u00a0 Whilst the PC label is broad, generic and probably vague, it does cover a lot of different possibilities without getting into the tortured semantics of categories, sub-categories and sub- sub-categories.<\/p>\n<p>I&#8217;ve defined PC as a computer system that uses real-time bio-electrical activity as input data.\u00a0 At one level, moving a mouse (or a Wii) with your hand represents a form of physiological computing as do physical interfaces based on gestures &#8211; as both are ultimately based on muscle potentials.\u00a0 But that seems a little pedantic.\u00a0 In my view, the PC concept begins with Muscle Interfaces (e.g. eye movements) where the electrical activity of muscles is translated into gestures or movements in 2D space.\u00a0 Brain-Computer Interfaces (BCI) represent a second category where the electrical activity of the cortex is converted into input control.\u00a0 Biofeedback represents the &#8216;parent&#8217; of this category of technology and was ultimately developed as a control device, to train the user how to manipulate the autonomic nervous system.\u00a0 By contrast, systems involving biocybernetic adaptation passively monitor spontaneous activity from the central nervous system and translate these signals into real-time software adaptation &#8211; most forms of affective computing fall into this category.\u00a0 Finally, we have the &#8216;black box&#8217; category of ambulatory recording where physiological data are continuously recorded and reviewed at some later point in time by the user or medical personnel.<\/p>\n<p>I&#8217;ve tried to capture these different categories in the diagram below.\u00a0 The differences between each grouping lie on a continuum from overt observable physical activity to covert changes in psychophysiology.\u00a0 Some are intended to function as explicit forms of intentional communication with continuous feedback, others are implicit with little intentionality on the part of the user.\u00a0 Also, there is huge overlap between the five different categories of PC: most involve a component of biofeedback and all will eventually rely on ambulatory monitoring in order to function.\u00a0 What I&#8217;ve tried to do is sketch out the territory in the most inclusive way possible.\u00a0 This inclusive scheme also makes hybrid systems easier to imagine, e.g. BCI + biocybernetic adaptation, muscle interface + BCI &#8211; basically we have systems (2) and (3) designed as input control, either of which may be combined with (5) because it operates in a different way and at a different level of the HCI.<\/p>\n<p>As usual, all comments welcome.<\/p>\n<div id=\"attachment_280\" style=\"width: 730px\" class=\"wp-caption aligncenter\"><img aria-describedby=\"caption-attachment-280\" loading=\"lazy\" class=\"size-full wp-image-280\" title=\"Slide1\" src=\"http:\/\/www.physiologicalcomputing.net\/wordpress\/wp-content\/uploads\/2009\/12\/Slide1.jpg\" alt=\"Five Categories of Physiological Computing\" width=\"720\" height=\"540\" \/><p id=\"caption-attachment-280\" class=\"wp-caption-text\">Five Categories of Physiological Computing<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"<p>In my last post I articulated a concern about how the name adopted by this field may drive the research in one direction or another.\u00a0 I&#8217;ve adopted the Physiological Computing (PC) label because it covers the widest range of possible systems.\u00a0 Whilst the PC label is broad, generic and probably vague, it does cover a [&hellip;]<\/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],"tags":[9,18,19,28,61,73],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/pY315-4c","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/posts\/260"}],"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=260"}],"version-history":[{"count":1,"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/posts\/260\/revisions"}],"predecessor-version":[{"id":4740,"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/posts\/260\/revisions\/4740"}],"wp:attachment":[{"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=260"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=260"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=260"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}