A Digitally-Augmented Ground Space with Timed Visual Cues for Facilitating Forearm Crutches’ Mobility

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Abstract

Persuasive technologies for physical rehabilitation have been proposed in a number of different health interventions such as post-stroke gait rehabilitation. We propose a new persuasive system, called Augmented Crutches, aimed at helping people to walk with crutches. People with injuries, or with any sort of mobility problem typically use assistive devices such as crutches, walkers or canes in order to be able to walk more independently. However, walking with crutches is a learning skill that needs continuous repetition and constant attention to detail in order to walk correctly with them and without suffering negative consequences, such as falls or injuries. In close collaboration with therapists, we identify the main issues that patients face when walking with crutches. These vary from person to person, but the most common and hardest challenges are the position and coordination of the crutches. Augmented Crutches studies human behavior aspects in these situations and augments the ground space around the user with digital visual cues where timing is the most important factor, without the need for a constant therapist providing manual help. This is performed through a mini-projector connected to a smartphone, worn by the user in a portable, lightweight manner. Our system helps people to learn how to walk using crutches with increased self-confidence and motivation. Additionally, our work identifies timing, controllability and awareness as the key design dimensions for the successful creation of persuasive, interactive experiences for learning how to walk with crutches.

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APA

Peres, B., Campos, P. F., & Azadegan, A. (2019). A Digitally-Augmented Ground Space with Timed Visual Cues for Facilitating Forearm Crutches’ Mobility. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11746 LNCS, pp. 184–201). Springer Verlag. https://doi.org/10.1007/978-3-030-29381-9_12

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