Motion Tracking of a Fish as a Novel Way to Control Electronic Music Performance

Leonardo ◽  
2016 ◽  
Vol 49 (3) ◽  
pp. 203-210 ◽  
Author(s):  
Shaltiel Eloul ◽  
Gil Zissu ◽  
Yehiel H. Amo ◽  
Nori Jacoby
Keyword(s):  
Electronic Music ◽  
Motion Tracking ◽  
Music Performance ◽  
Three Dimensional ◽  
Extra Information

The authors have mapped the three-dimensional motion of a fish onto various electronic music performance gestures, including loops, melodies, arpeggio and DJ-like interventions. They combine an element of visualization, using an LED screen installed on the back of an aquarium, to create a link between the fish’s motion and the sonified music. This visual addition provides extra information about the fish’s role in the music, enabling the perception of versatile and developing auditory structures during the performance that extend beyond the sonification of the momentary motion of objects.

scholarly journals Performers of the night: Examining the mental health of electronic music artists

2021 ◽  
pp. 030573562097698
Author(s):  
Jolan Kegelaers ◽  
Lewie Jessen ◽  
Eline Van Audenaerde ◽  
Raôul RD Oudejans
Keyword(s):  
Mental Health ◽  
Social Support ◽  
Sleep Disturbance ◽  
Electronic Music ◽  
Music Performance ◽  
Well Being ◽  
Cross Sectional ◽  
Music Performance Anxiety ◽  
Symptoms Of Depression ◽  
Levels Of Functioning

Despite growing popular interest for the mental health of electronic music artists, scientific research addressing this topic has remained largely absent. As such, the aim of the current study was to examine the mental health of electronic music artists, as well as a number of determinants. Using a cross-sectional quantitative design, a total of 163 electronic music artists participated in this study. In line with the two-continua model of mental health, both symptoms of depression/anxiety and well-being were adopted as indicators for mental health. Furthermore, standardized measures were used to assess potential determinants of mental health, including sleep disturbance, music performance anxiety, alcohol abuse, drug abuse, occupational stress, resilience, and social support. Results highlighted that around 30% of participants experienced symptoms of depression/anxiety. Nevertheless, the majority of these participants still demonstrated at least moderate levels of functioning and well-being. Sleep disturbance formed a significant predictor for both symptoms of depression/anxiety and well-being. Furthermore, resilience and social support were significant predictors for well-being. The results provide a first glimpse into the mental health challenges experienced by electronic music artists and support the need for increased research as well as applied initiatives directed at safeguarding their mental health.

scholarly journals Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141773275 ◽  
Author(s):  
Francisco J Perez-Grau ◽  
Fernando Caballero ◽  
Antidio Viguria ◽  
Anibal Ollero
Keyword(s):  
Monte Carlo ◽  
Motion Tracking ◽  
Robot Navigation ◽  
Tracking System ◽  
Three Dimensional ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Monte Carlo Localization ◽  
Motion Tracking System

This article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (RGB-D) sensor, distances to several radio-tags placed in the environment, and an inertial measurement unit. The approach is demonstrated with an unmanned aerial vehicle flying for 10 min indoors and validated with a very precise motion tracking system. The approach has been implemented using the robot operating system framework and works smoothly on a regular i7 computer, leaving plenty of computational capacity for other navigation tasks such as motion planning or control.

scholarly journals Interacting with Maps in Virtual and Augmented Reality

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Bernhard Jenny ◽  
Kadek Ananta Satriadi ◽  
Yalong Yang ◽  
Christopher R. Austin ◽  
Simond Lee ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Motion Tracking ◽  
Three Dimensional ◽  
Geospatial Data ◽  
Leap Motion ◽  
Flow Maps ◽  
Points Of Interest ◽  
Tracking Controllers ◽  
Game Engines ◽  
Virtual And Augmented Reality

<p><strong>Abstract.</strong> Augmented reality (AR) and virtual reality (VR) technology are increasingly used for the analysis and visualisation of geospatial data. It has become simple to create an immersive three-dimensional AR or VR map with a combination of game engines (e.g., Unity), software development kits for streaming and rendering geospatial data (e.g., Mapbox), and affordable hardware (e.g., HTC Vive). However, it is not clear how to best interact with geospatial visualisations in AR and VR. For example, there are no established standards to efficiently zoom and pan, select map features, or place markers on AR and VR maps. In this paper, we explore interaction with AR and VR maps using gestures and handheld controllers.</p><p>As for gesture-controlled interaction, we present the results of recent research projects exploring how body gestures can control basic AR and VR map operations. We use motion-tracking controllers (e.g., Leap Motion) to capture and interpret gestures. We conducted a set of user studies to identify, explore and compare various gestures for controlling map-related operations. This includes, for example, mid-air hand gestures for zooming and panning (Satriadi et al. 2019), selecting points of interest, adjusting the orientation of maps, or placing markers on maps. Additionally, we present novel VR interfaces and interaction methods for controlling the content of maps with gestures.</p><p>As for handheld controllers, we discuss interaction with exocentric globes, egocentric globes (where the user stands inside a large virtual globe), flat maps, and curved maps in VR. We demonstrate controller-based interaction for adjusting the centre of world maps displayed on these four types of projection surfaces (Yang et al. 2018), and illustrate the utility of interactively movable VR maps by the example of three-dimensional origin-destination flow maps (Yang et al. 2019).</p>

PO-0861: Three-dimensional liver motion tracking using 2D real-time MRI

2014 ◽  
Vol 111 ◽  
pp. S86
Author(s):  
L. Brix ◽  
S. Ringgaard ◽  
T. Sangild Sørensen ◽  
P. Rugaard Poulsen
Keyword(s):  
Real Time ◽  
Motion Tracking ◽  
Three Dimensional

Residual Elimination Algorithm Enhancements to Improve Foot Motion Tracking During Forward Dynamic Simulations of Gait

2015 ◽  
Vol 137 (11) ◽  
Author(s):  
Jennifer N. Jackson ◽  
Chris J. Hass ◽  
Benjamin J. Fregly
Keyword(s):  
Motion Tracking ◽  
Three Dimensional ◽  
Patient Specific ◽  
Ground Contact ◽  
Gait Patterns ◽  
Elimination Algorithm ◽  
Foot Motion ◽  
Gait Optimization ◽  
Joint Acceleration ◽  
Full Body

Patient-specific gait optimizations capable of predicting post-treatment changes in joint motions and loads could improve treatment design for gait-related disorders. To maximize potential clinical utility, such optimizations should utilize full-body three-dimensional patient-specific musculoskeletal models, generate dynamically consistent gait motions that reproduce pretreatment marker measurements closely, and achieve accurate foot motion tracking to permit deformable foot-ground contact modeling. This study enhances an existing residual elimination algorithm (REA) Remy, C. D., and Thelen, D. G., 2009, “Optimal Estimation of Dynamically Consistent Kinematics and Kinetics for Forward Dynamic Simulation of Gait,” ASME J. Biomech. Eng., 131(3), p. 031005) to achieve all three requirements within a single gait optimization framework. We investigated four primary enhancements to the original REA: (1) manual modification of tracked marker weights, (2) automatic modification of tracked joint acceleration curves, (3) automatic modification of algorithm feedback gains, and (4) automatic calibration of model joint and inertial parameter values. We evaluated the enhanced REA using a full-body three-dimensional dynamic skeletal model and movement data collected from a subject who performed four distinct gait patterns: walking, marching, running, and bounding. When all four enhancements were implemented together, the enhanced REA achieved dynamic consistency with lower marker tracking errors for all segments, especially the feet (mean root-mean-square (RMS) errors of 3.1 versus 18.4 mm), compared to the original REA. When the enhancements were implemented separately and in combinations, the most important one was automatic modification of tracked joint acceleration curves, while the least important enhancement was automatic modification of algorithm feedback gains. The enhanced REA provides a framework for future gait optimization studies that seek to predict subject-specific post-treatment gait patterns involving large changes in foot-ground contact patterns made possible through deformable foot-ground contact models.

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