Information presentation through a head-worn display (“smart glasses”) has a smaller influence on the temporal structure of gait variability during dual-task gait compared to handheld displays (paper-based system and smartphone)

Abstract

The need to complete multiple tasks concurrently is a common occurrence both daily life and in occupational activities, which can often include simultaneous cognitive and physical demands. As one example, there is increasing availability of head-worn display technologies that can be employed when a user is mobile (e.g., while walking). This new method of information presentation may, however, introduce risks of adverse outcomes such as a decrement to gait performance. The goal of this study was thus to quantify the effects of a head-worn display (i.e., smart glasses) on motor variability during gait and to compare these effects with those of other common information displays (i.e., smartphone and paper-based system). Twenty participants completed four walking conditions, as a single task and in three dual-task conditions (three information displays). In the dual-task conditions, the information display was used to present several cognitive tasks. Three different measures were used to quantify variability in gait parameters for each walking condition (using the cycle-to-cycle standard deviation, sample entropy, and the “goal-equivalent manifold” approach). Our results indicated that participants used less adaptable gait strategies in dual-task walking using the paper-based system and smartphone conditions compared with single-task walking. Gait performance, however, was less affected during dual-task walking with the smart glasses. We conclude that the risk of an adverse gait event (e.g., a fall) in head-down walking conditions (i.e., the paper-based system and smartphone conditions) were higher than in single-task walking, and that head-worn displays might help reduce the risk of such events during dual-task gait conditions.

Interplay of User Behavior, Communication, and Computing in Immersive Reality 6G Applications

“…A vast volume of work has concentrated on comparative analysis regarding the impact of the use of mobile phones and XR wearables (such as HMDs) on the gait variability. The results are consistent across various research groups and have established that head-up tasks (i.e., those involving HMDs) degrade walking performance to a lesser extent than headdown activities (i.e., those with mobile phones) [5]. In the case of mobile phone use, walking speed decreases significantly for dual-task versus single-task activity.…”

“…The use of a head-mounted display (HMD) naturally leads to significant gait changes, such as shorter stride length, greater stance time, and higher speed variability, compared to conventional user behavior [3], [4]. Due to the specific features of XR content presentation and navigation, motion patterns of HMD owners may considerably differ from what the use of mobile phones entails [5]. For example, in the case of conventional mobile phone applications, the walking patterns of users involved in text message writing and voice audio recording are noticeably different.…”

Emotional characteristic analysis of human gait while real-time movie viewing

“…In this walk, the HoloLens 2 is just a pair of transparent smart glasses showing no content at all. As we found in previous studies, including Kim et al (2018), Sedighi et al (2018), andSedighi et al (2020), if the participants never experienced using smart glasses while walking, adverse effects could occur and gait performance could be unstable. Our attempt to cope with this issue was to ask all subjects to take the rehearsal walks with and without wearing HoloLens 2 before performing actual recording.…”

“…In addition, subjects walked more conservatively and more cautiously when crossing obstacles (Kim et al, 2018). Unfortunately, adverse impacts such as walking instability can occur when using smart glasses, but the stability issue was not as significant as when using a smartphone and a paper-based system (Sedighi et al, 2018(Sedighi et al, , 2020.…”

Exploration of different classes of metrics to characterize motor variability during repetitive symmetric and asymmetric lifting tasks

“…There are several methods to quantify MV based on equifinality, including the uncontrolled manifold (UCM) 1,30 , tolerance-noise-covariation 31 , the minimum intervention principle 32 , and the Goal Equivalent Manifold (GEM) 29 . Among these, GEM is the only method from the equifinality class that can simultaneously quantify the magnitude and temporal structure of variability 33,34 .…”