GaitWear: A smartwatch application for in-the-wild gait normalization based on a virtual field study assessing the effects of visual and haptic cueing

de Oliveira, A., Khamis, M. and Esteves, A. (2021) GaitWear: A smartwatch application for in-the-wild gait normalization based on a virtual field study assessing the effects of visual and haptic cueing. Behaviour and Information Technology, 40(12), pp. 1292-1309. (doi: 10.1080/0144929X.2021.1958060)

Abstract

We explore the use of Virtual Reality as a way to simulate field studies via what is known as Virtual Field Studies. This is particularly relevant when inviting participants to the lab is not feasible, or in the case of our work to simulate locomotion in crowded streets from the safety of the lab. We rely on this to assess the effects of four different cues in normalising gait performance in a simulated environment: two baselines from literature (visual and haptic) that have been traditionally explored in the context of a controlled lab environment, and two novel haptic cues that combine temporal and spatial feedback. We compare these in a holistic manner for the first time, capturing not only gait and gaze performance, but usability, perceived workload, and participant preference. Our haptic baseline performed according to the results described in the literature, and together with participants' gaze behaviour and sense of embodiment we start to validate Virtual Field Studies in this domain. We further report that the haptic baseline was the preferred cue by participants, and led to an overall better performance. We conclude with our implementation of GaitWear, a smart watch application that produces this haptic baseline on the fly.

• Item Type: Articles
• Additional Information: This research was supported by LARSyS (Projeto - UIDB/50009/2020).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Khamis, Dr Mohamed
• Authors: de Oliveira, A., Khamis, M., and Esteves, A.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Behaviour and Information Technology
• Publisher: Taylor & Francis
• ISSN: 0144-929X
• ISSN (Online): 1362-3001
• Published Online: 30 July 2021
• Copyright Holders: Copyright © 2021 Informa UK Limited, trading as Taylor and Francis Group
• First Published: First published in Behaviour and Information Technology 40(12): 1292-1309
• Publisher Policy: Reproduced in accordance with the publisher copyright policy