Influences of Smartphone Operation on Gait and Posture During Outdoor Walking Task

Luo, Yue; Zheng, Haolan; Chen, Yuhao; Giang, Wayne C.W.; Hu, Boyi

doi:10.1177/1071181320641418

Cited by 14 publications

(7 citation statements)

References 23 publications

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“…Subsequently, demographic data including age, gender, weight, and height with shoes on were obtained. Next, 17 IMU sensors were securely attached to participants’ body, specifically on: head, sternum, pelvis, right and left shoulder, right and left upper arm, right and left forearm, right and left hand, right and left upper leg, right and left lower leg, right and left foot 37 , 38 (Fig. 2 ).…”

Section: Methodsmentioning

confidence: 99%

Human mobile robot interaction in the retail environment

et al. 2022

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As technology advances, Human-Robot Interaction (HRI) is boosting overall system efficiency and productivity. However, allowing robots to be present closely with humans will inevitably put higher demands on precise human motion tracking and prediction. Datasets that contain both humans and robots operating in the shared space are receiving growing attention as they may facilitate a variety of robotics and human-systems research. Datasets that track HRI with rich information other than video images during daily activities are rarely seen. In this paper, we introduce a novel dataset that focuses on social navigation between humans and robots in a future-oriented Wholesale and Retail Trade (WRT) environment (https://uf-retail-cobot-dataset.github.io/). Eight participants performed the tasks that are commonly undertaken by consumers and retail workers. More than 260 minutes of data were collected, including robot and human trajectories, human full-body motion capture, eye gaze directions, and other contextual information. Comprehensive descriptions of each category of data stream, as well as potential use cases are included. Furthermore, analysis with multiple data sources and future directions are discussed.

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Section: Methodsmentioning

confidence: 99%

Human mobile robot interaction in the retail environment

et al. 2022

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“…Before gait detection and variable calculation, data exported from Xsens software (MVN Analyze 2019, Xsens Technologies BV, Enschede, Netherlands) were imported into MATLAB (R2020a, MathWorks, Natick, MA, USA). The following subsections detail the data preprocessing pipeline, which is derived from our previous publications [ 22 , 23 , 31 ].…”

Section: Methodsmentioning

confidence: 99%

Salient Targets and Fear of Falling Changed the Gait Pattern and Joint Kinematic of Older Adults

Luo

Grimaldi

et al. 2022

Sensors

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Background: Fear of falling and environmental barriers in the home are two major factors that cause the incidence of falling. Poor visibility at night is one of the key environmental barriers that contribute to falls among older adult residents. Ensuring their visual perception of the surroundings, therefore, becomes vital to prevent falling injuries. However, there are limited works in the literature investigating the impact of the visibility of the target on older adults’ walking destinations and how that impact differs across them with different levels of fear of falling. Objective: The purpose of the study was to examine the effects of target salience on older adults’ walking performance and investigate whether older adults with varying levels of fear of falling behave differently. Methods: The salient target was constructed with LED strips around the destination of walking. Fifteen older adults (aged 75 years old and above), seven with low fear of falling and eight with high fear of falling, volunteered for the study. Participants walked from the designated origin (i.e., near their beds) to the destination (i.e., near the bathroom entrance), with the target turned on or off around the destination of the walking trials. Spatiotemporal gait variables and lower-body kinematics were recorded by inertial sensors and compared by using analysis of variance methods. Results: Data from inertial sensors showed that a more salient target at the destination increased older adults’ gait speed and improved their walking stability. These changes were accompanied by less hip flexion at heel strikes and toe offs during walking. In addition, older adults with low fear of falling showed more substantial lower-body posture adjustments with the salient target presented in the environment. Conclusions: Older adults with a low fear of falling can potentially benefit from a more salient target at their walking destination, whereas those with a high fear of falling were advised to implement a more straightforward falling intervention in their living areas.

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“…Participants' movements were recorded using a wearable motion capture system (MVN Awinda, Xsens Technologies BV). This system uses 17 individual inertial measurement unit sensors (36 mm × 24.5 mm × 10 mm, 10 g), which were attached to the participants according to the manufacturer's instruction manual (Figure 1, left; Luo et al, 2020;Robert-Lachaine et al, 2017;Schepers et al, 2018;Zheng et al, 2020).…”

Section: Instrumentsmentioning

confidence: 99%

Distraction From Smartphones Changed Pedestrians’ Walking Behaviors in Open Areas

et al. 2023

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The prevalence of phone use has become a major concern for pedestrian safety. Using smartphones while walking reduces pedestrians’ ability to perceive the environment by increasing their cognitive, manual, and visual demands. The purpose of this study was to investigate the effect of common phone tasks (i.e., reading, tapping, gaming) on walking behaviors during outdoor walking. Nineteen young adults were instructed to complete four walking conditions (walking only, walking–reading, walking–tapping, and walking–gaming) along an open corridor. Results showed that all three phone tasks increased participants’ neck flexion (i.e., neck kyphosis) during walking. Meanwhile, the reading task showed a greater influence on the temporal aspect during the early phases of a gait cycle. The tapping task lowered the flexion angles of the middle and lower back (i.e., torso lordosis) and induced a longer terminal double support. And the gaming task resulted in a decrease in middle back flexion, a shorter stride length, and a longer terminal double support while walking. Findings from the study confirmed our hypothesis that phone tasks changed pedestrians’ physical responses to smartphone distraction while walking. To avoid potential risks caused by the observed posture and gait adaptations, safety precautions (e.g., roadside/electronic warning signals) might be imposed depending on the workload expected by different phone tasks.

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Influences of Smartphone Operation on Gait and Posture During Outdoor Walking Task

Cited by 14 publications

References 23 publications

Human mobile robot interaction in the retail environment

Human mobile robot interaction in the retail environment

Salient Targets and Fear of Falling Changed the Gait Pattern and Joint Kinematic of Older Adults

Distraction From Smartphones Changed Pedestrians’ Walking Behaviors in Open Areas

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