Quantified self and modeling of human cognition

Abstract

This paper tackles an important issue of how to use quantified self technologies for modeling human cognition. At the same time, it provides some insights on how Quantified Self community can benefit from user modeling and personalization, especially in the domain of human cognition.

Health Promotion for Childhood Obesity: An Approach Based on Self-Tracking of Data

“…The data obtained on user behavior can be used to create user models in the short, medium, and long term (18). For instance, Sarzotti et al [49] proposed an Enhanced User Model (EUM) with shortand long-term data on four types of information: Therefore, in this paper, we describe a QS proposal for creating a short-, medium-, and long-term user model that focuses not only on cognitive functions, attitudes, behaviors, and emotions, but also on physical and healthy performance activities.…”

“…These data can be recorded daily (heart rate, respiration, hours slept) or even more frequently (blood pressure or weight, for example). Other data that can enrich the QS user model can be related to short-term and long-term information, such as the attitudes, behaviors, effects, and cognitive functions of users [18,19]. QS can involve the graphical presentation of the information and a response loop of reflection and self-analysis [17].…”

Holistic User Models for Cognitive Disabilities

“…In PIUMA, the ASD individual is represented by a user model which aims to capture different aspects of her cognition [6], affects, and habits, according to the Real World User Model perspective, which stresses the opportunities for exploiting the data coming from the "real world" (e.g. not just from the web) to build a "complete" representation of the user [7].…”

“…These POIs include both places that the user already knows and appreciated in the past, and new places suggested by the recommendation engine, based on crowdsourced information provided by other people through the interaction with the map. We exploit open data made available by web sites like OpenStreetMap and public administrations (Municipality of Torino 5 and Regione Piemonte 6 ) in order to gather information about the typology and "quality" of the places on the map (for 3 http://openstreetmap.org 4 https://developers.facebook.com/docs/graph-api 5 http://aperto.comune.torino.it 6 http://www.dati.piemonte.it However, these data are often incomplete and, more important, they do not contain information about those perceptual features that ASD individuals consider essential to define a place as safe, as we have seen in the interviews. Thus, in order to have this crucial information to define a place as safe, in PIUMA we decided to exploit also a crowdsourcing mechanism, allowing people to provide rates, comments, and reviews about places.…”

Self-Monitoring of Emotions and Mood Using a Tangible Approach

“…Thanks to the expansion of the Internet of things (IoT), the miniaturization of sensors, and research in ubiquitous technologies and mobile devices, PI systems have started to be used outside the clinical setting. Nowadays, a large variety of data can be monitored and analyzed: from sleep quality to weight, from heart rate and step count to performance values, habits, and actions [7][8][9][10]. Collecting these data allows users to self-monitor their behaviors in a way inconceivable without such technological means.…”