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Interview met Ben Kröse, bijzonder hoogleraar ambient robotics aan de Universiteit van Amsterdam over de toepassing van intelligente systemen zoals robots, observatiesystemen en interactieve apparaten in de gezondheidszorg.
Gamers are, like Yamauchi, described as nonconformist, creative, and self-confident persons, who seem unafraid to make mistakes (Beck and Wade 2004). Is it true that games present us with an opportunity to develop a particular identity, or are specific people attracted to games that create these opportunities? In the last decade, research has been conducted into the (playful) organizational style of gamers, and into the leadership qualities that may be developed in a game (DeMarco, Lesser, and O’Driscoll 2007; Reeves and Malone 2007). The search for an answer to the above question is the aim of this chapter. To be more specific, we would like to better understand identity construction and representation. For this reason we would like to further elaborate on the notion of playful identity as discussed in the introductory chapter of this volume. In contrast to other identity constructs, a playful identity characterizes someone’s ludic activities without immediately discussing the valuing and moralizing practices arising from these activities
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Gaming Horizons is a EU-funded project that explored the role of video games in culture, the economy and education. We engaged with more than 280 stakeholders through interviews, workshops and webinars.
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Grenzen zijn binnen onderwijs en opvoeding een klassiek thema. Maar waar enerzijds een roep is om meer en hardere (opvoedings)grenzen, voelen we anderzijds ook een verlegenheid bij het stellen van die grenzen. Waar komt dat ongemak vandaan en hoe gaan we ermee om? Hoogleraar Maatschappelijke Opvoedingsvraagstukken Micha de Winter beantwoordt zeven vragen over het vraagstuk
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BackgroundA key factor in successfully preventing falls, is early identification of elderly with a high risk of falling. However, currently there is no easy-to-use pre-screening tool available; current tools are either not discriminative, time-consuming and/or costly. This pilot investigates the feasibility of developing an automatic gait-screening method by using a low-cost optical sensor and machinelearning algorithms to automatically detect features and classify gait patterns.MethodParticipants (n = 204, age 27 ± 7 yrs.) performed a gait test under two conditions: control and with distorted depth perception (induced by wearing special goggles). Each test consisted of 4x 3m walking at comfortable speed. Full-body 3D kinematics were captured using an optical sensor (Microsoft Xbox One Kinect). Tests were conducted in a public space to establish relatively 'natural' conditions. Data was processed in Matlab and common spatiotemporal variables were calculated per gait section. The 3D-time series data of the centre of mass for each section was used as input for a neural network, that was trained to discriminate between the two conditions.ResultsWearing the goggles affected the gait pattern significantly: gait velocity and step length decreased, and lateral sway increased compared to the control condition. A 2-layer neural network could correctly classify 79% of the gait segments (i.e. with or without distorted vision).ConclusionsThe results show that gait patterns of healthy people with distorted vision could automatically be classified with the proposed approach. Future work will focus on adapting this model for identification of specific physical risk-factors in elderly.
