The treatment of gait disorders and impairments are major challenges in physical therapy. The broad and fast development in low-cost, miniaturized, and wireless sensing technologies supports the development of embedded and unobtrusive systems for robust gait-related data acquisition and analysis. Next to their applications as portable and lowcost diagnostic tools, such systems are also capable of use as feedback devices for retraining gait. The approach described within this article applies movement-based sonification of gait to foster motor learning. This article aims at presenting and evaluating a prototype of a pair of instrumented insoles for real-time sonification of gait (SONIGait) and to assess its immediate effects on spatio-temporal gait parameters. For this purpose, a convenience sample of six healthy males (age 35 ± 5 years, height 178 ± 4 cm, mass 78 ± 12 kg) and six healthy females (age 38 ± 7 years, height 166 ± 5 cm, mass: 63±8 kg) was recruited. They walked at a self-selected walking speed across a force distribution measurement system (FDM) to quantify spatio-temporal gait parameters during walking without and with five different types of sonification. The primary results from this pilot study revealed that participants exhibited decreased cadence (p < 0.01) and differences Results suggest that sonification has an effect on gait parameters, however further investigation and development is needed to understand its role as a tool for gait rehabilitation.

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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.