Continuous affect state annotation using a joystick-based user interface

Ongoing research at the DLR (German Aerospace Center) aims to employ affective computing techniques to ascertain the emotional states of users in motion simulators. In this work, a novel user feedback interface employing a joystick to acquire subjective evaluation of the affective experience is presented. This interface allows the subjects to continuously annotate their affect states, elicited in this scenario by watching video clips. Several physiological parameters (e.g. heart rate, electrodermal activity, respiration rate, etc.) were acquired during the viewing session. A statistical analysis is presented, which shows expected patterns in data that validate the design and methodology of the experiment and lay the groundwork for further experiments to be undertaken at the DLR

Music, computing and health: A roadmap for the current and future roles of music technology for health care and well-being.

Health and self-regulatio

Motivation through gamification: A Self-Determination Theory perspective for the design of an adaptive reward system

Research on the nature and origins of human motivation has addressed the role of rewards in learning and behaviour. Gamification finds its raison d'être in being able to leverage motivational theories, to foster motivation in users through the use of game elements. One of the main criticisms moved to the use of gamification for learning purposes is related to the one-size-fits-all approach that tends to characterize many gamified applications. In this paper we explore the possibilities that can arise from the convergence of Self-Determination Theory principles and machine learning, to improve the efficacy of gamification reward systems

Rule Mining for Local Boundary Detection in Melodies

The task of melodic segmentation is a long-standing MIR task that has not been solved, yet. In this paper, we shortly review existing approaches, most of which are either based on rule-sets derived from Gestalt principles, or on a statis- tical learning approach. We use a method related to both approaches. A rule mining algorithm is employed to find a rule set that classifies notes within their local context as phrase boundary. The advantage of a rule-based model is its interpretability. By inspecting the rules, some important clues are revealed about what constitutes a melodic phrase boundary, notably a prevalence of rhythmic features over pitch features. Both the discovered rule set and a Random Forest Classifier trained on the same data set outper- form previous methods on the task of melodic segmenta- tion of melodies from the Essen Folk Song Collection, the Meertens Tune Collections, and the set of Bach Chorales

Multi-Temporal Convolutions for Human Action Recognition in Videos

Effective extraction of temporal patterns is crucial for the recognition of temporally varying actions in video. We argue that the fixed-sized spatio-temporal convolution kernels used in convolutional neural networks (CNNs) can be improved to extract informative motions that are executed at different time scales. To address this challenge, we present a novel convolution block that is capable of extracting spatio-temporal patterns at multiple temporal resolutions. Our proposed multi-temporal convolution (MTConv) blocks utilize two branches that focus on brief and prolonged spatio-temporal patterns, respectively. The extracted time-varying features are aligned in a third branch, with respect to global motion patterns through recurrent cells. The proposed blocks are lightweight and can be integrated into any 3D-CNN architecture. This introduces a substantial reduction in computational costs. Extensive experiments on Kinetics, Moments in Time and HACS action recognition benchmark datasets demonstrate competitive performance of MTConvs compared to the state-of-the-art with a significantly lower computational footprint 11Our code is available at: https://git.io/JfuPi

Analyzing human–human interactions: A survey

Many videos depict people, and it is their interactions that inform us of their activities, relation to one another and the cultural and social setting. With advances in human action recognition, researchers have begun to address the automated recognition of these human–human interactions from video. The main challenges stem from dealing with the considerable variation in recording setting, the appearance of the people depicted and the coordinated performance of their interaction. This survey provides a summary of these challenges and datasets to address these, followed by an in-depth discussion of relevant vision-based recognition and detection methods. We focus on recent, promising work based on deep learning and convolutional neural networks (CNNs). Finally, we outline directions to overcome the limitations of the current state-of-the-art to analyze and, eventually, understand social human actions

CrowdAR Table - An AR Table for Interactive Crowd Simulation

In this paper we describe a prototype implementation of an augmented reality (AR) system for accessing and interacting with crowd simulation software. We identify a target audience and tasks (access to the software in a science museum) motivate the choice of AR system (an interactive table complemented with handheld AR via smartphones) and describe its implementation. Our system has been realized in a prototypical implementation verifying its feasibility and potential. Detailed user testing will be part of our future work

ICT: Health’s best friend and worst enemy?

I propose a paradigm shift for health care, as there is an urgent need for i) continuous (semi-)automatic medical checkups, ii) cost reduction, and iii) cure for the 21st century black plague (i.e., stress-related diseases) are very much needed. To realize this ICT’s Paradox has to be solved. On the one hand, ICT can cause i) musculoskeletal problems, ii) vision problems, iii) headache, iv) obesity, v) stress disorders (e.g., burn out), vi) metabolic issues, vii) addiction (e.g., to games, social media, and Internet), viii) sleeping problems, ix) social isolation, and x) an unrealistic world view. On the other hand, ICT claims to provide these problems’ solutions. Consequently, health informatics needs to adopt a holistic approach, improve its fragile theoretical frameworks, and handle the incredible variance we all show. As a remedy, I propose to take up the challenge to next-generation models of personality, as they are a crucial determinant in people’s stress coping style