Maintaining a certain heart rate zone is important for athletes during running. This is often supported by smartwatches or smartphones paired with chest belts. Such devices can easily distract or interrupt runners. To address this, we developed smart goggles with an integrated ambient LED strip that visually displays heart rate zone feedback. In a within-subject study (N=11), we compared the LED-Goggles and a state-of-the-art Apple Watch. The findings indicated no statistically significant difference in the efficacy of the two systems in assisting runners in maintaining their target heart rate. However, participants rated the goggles as clearer and more helpful than the watch. Open-ended responses highlighted the goggles’ continuous, ambient feedback as advantages, reducing cognitive load and allowing users to focus more on running. Our findings suggest that ambient light feedback in goggles can serve as an effective, less intrusive alternative to smart watch feedback systems for heart rate monitoring during runs.

Running is the most practiced sport worldwide. Rhythmic breathing is a breathing technique claimed to enhance running economy and experience. Recent studies showed how sounds can guide runners in achieving and maintaining rhythmic breathing patterns. In this paper we explore how runners can interact with this sonic breath guidance while running, aiming to fit locomotion without forcing the runner to slow down or "stop-to-interact". Common devices, such as smartphones, smartwatches, and fitness trackers are widely employed, but their small interfaces and touch screens often necessitate slowing down for effective use. This demo paper introduces GestureGlove, a novel garment that captures fine and gross hand gestures to control rhythmic breathing guidance sounds. The concept combines gross arm gestures for sound selection by extending normal running motion, while fine in-hand gesturing enables fine-grained volume control of the selected sound independent of the running motion. These underlying principles of using gross and fine gesturing, and the embodied approach, show a new direction in the design for truly mobile interactions.

Abstract We describe two proof-of-concept approaches on the sonification of estimated operation states and conditions focusing on two scenarios: a laboratory setup of a manipulated 3D printer and an industrial setup focusing on the operations of a punching machine. The results of these studies form the basis for the development of an “intelligent” noise protection headphone as part of Cyber Physical Production Systems which provides auditorily augmented information to machine operators and enables radio communication between them. Further application areas are implementations in control rooms (equipped with multi-channel loudspeaker systems) and utilization for training purposes. As a first proof-of-concept, the data stream of error probability estimations regarding partly manipulated 3D printing processes were mapped to three sonification models, providing evidence about momentary operation states. The neural network applied indicates a high accuracy (> 93%) of the error estimation distinguishing between normal and manipulated operation states. None of the manipulated states could be identified by listening. An auditory augmentation, or sonification of these error estimations, provides a considerable benefit to process monitoring. For a second proof-of-concept, setup operations of a punching machine were recorded. Since all operations were apparently flawlessly executed, and there were no errors to be reported, we focused on the identification of operation phases. Each phase of a punching process could be algorithmically distinguished at an estimated probability rate of > 94%. In the auditory display, these phases were represented by different instrumentations of a musical piece in order to allow users to differentiate between operations auditorily.