User's Walking Research Articles

SWiLoc: Fusing Smartphone Sensors and WiFi CSI for Accurate Indoor Localization.

Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone's direction and the user's actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc (Smartphone and WiFi-based Localization), an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user's walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system's applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 m in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.

Distributions of walking access to public transport in Melbourne, Australia – Evidence on acceptable and tolerable walking distances

The international literature indicates a wide interest in the distances public transport users walk to access their services. Urban and transport planners seek information on acceptable walking distances (AWD) in the provision of minimum levels of service coverage. This study uses a large database from Melbourne, Australia, to analyze trip length frequency distributions (TLFD) of walking access and egress to consider AWD in a multimodal public transportation system and to examine tolerable walking distances (TWD). AWD provides a guide to planners about stop/station locations for desirable minimum service coverage. TWD is a representative maximum walking distance for assessing the effectiveness of that service provision and can be used in conjunction with AWD. A statistical distribution function for walking distances can facilitate the use of regional values for AWD and TWD in transit service planning. The Burr Type XII distribution is shown to provide a good fit to the observed data. This provides a valuable tool for the analysis of percentile walking distances and suggests a general framework for the study of AWD and TWD in any city or region.

A Light Weight Exoskeleton

The WIM exoskeleton presents an innovative, lightweight, and easily wearable solution that elevates user walking experiences. With its assist mode features, WIM has been shown to reduce the energy required to walk, while its exercise mode produces resistance forces that intensify walking or running workouts. Social determinants of health such as economic stability and community support, however, impact the widespread use and accessibility of the exoskeleton. The intersections of gender, religion, and geopolitical factors further impact accessibility. In the future, WIM may be used for industrial and/or rescue workers to enhance productivity and prevent injury. Limitations of the device suggest that further comprehensive studies are essential prior to the widespread use of WIM.

RedirectedDoors+: Door-Opening Redirection with Dynamic Haptics in Room-Scale VR.

RedirectedDoors is a visuo-haptic door-opening redirection technique in VR, and it has shown promise in its ability to efficiently compress the physical space required for a room-scale VR experience. However, its previous implementation has only supported laboratory experiments with a single door opening at a fixed location. To significantly expand this technique for room-scale VR, we have developed RedirectedDoors+, a robot-based system that permits consecutive door-opening redirection with haptics. Specifically, our system is mainly achieved with the use of three components: (1) door robots, a small number of wheeled robots equipped with a doorknob-like prop, (2) a robot-positioning algorithm that arbitrarily positions the door robots to provide the user with just-in-time haptic feedback during door opening, and (3) a user-steering algorithm that determines the redirection gain for every instance of door opening to keep the user away from the boundary of the play area. Results of simulated VR exploration in six virtual environments reveal our system's performance relative to user walking speed, paths, and number of door robots, from which we derive its usage guidelines. We then conduct a user study ($N=12$) in which participants experience a walkthrough application using the actual system. The results demonstrate that the system is able to provide haptic feedback while redirecting the user within a limited play area.

Taming travel time fluctuations through adaptive stop pooling

Ride sharing services combine trips of multiple users in the same vehicle and may provide more sustainable transport than private cars. As mobility demand varies during the day, the travel times experienced by passengers may substantially vary as well, making the service quality unreliable. We show through model simulations that such travel time fluctuations may be drastically reduced by stop pooling. Having users walk to meet at joint locations for pick-up or drop-off allows buses to travel more direct routes by avoiding frequent door-to-door detours, especially during high demand. We in particular propose adaptive stop pooling by adjusting the maximum walking distance to the temporally and spatially varying demand. The results highlight that adaptive stop pooling may substantially reduce travel time fluctuations while even improving the average travel time of ride sharing services, especially for high demand. Such quality improvements may in turn increase the acceptance and adoption of ride sharing services.

Active Ankle–Foot Orthosis Design and Computer Simulation with Multi-Objective Parameter Optimization

There are many current active orthosis designs to assist with disabilities related to foot-drop, with most of them intervening during the whole gait cycle. We propose that, for the treatment of foot-drop, it is possible to design an ankle–foot device that will assist a walking user only during the dorsiflexion stages of the gait, avoiding interference with other stages, by using a single actuator with a simple transmission and a suspension block. This design can be improved by the use of multi-objective optimization to obtain a static set of parameters that are applicable to varying initial conditions. We present a computer simulation study of an active ankle–foot orthosis design, based on the interaction of a cam and lever with a suspension block, with the objective of assisting only with dorsiflexion during the gait cycle, leaving the rest of the movements unimpeded while reducing the complexity and weight of the device. This design is validated using a full simulation environment that includes the movements of the lower leg and foot, as they interact with our device and a ground element. As part of the design and validation, we found sets of mechanical and control parameters that provoke adequate output behavior of the orthosis to help the wearer perform a moderate-speed, normal gait. To optimize the design, we proposed three objectives to warrant ankle angle accuracy, minimal oscillations, and low energy consumption. A set of solutions was obtained with multi-objective optimization algorithms NSGA-II and RVEA to tune the parameters of the active orthosis. The solutions set from RVEA resulted in lower mean and standard deviation values for the oscillations and energy objectives in comparison to the solutions from NSGA-II, while for the MSE objective, NSGA-II obtained lower mean and standard deviation; for the energy consumption objective, the mean score using RVEA is 17% less than with NSGA-II. The orthosis is shown to be robust to differences in initial ankle angles. We observed that it is possible to obtain a broad set of solutions with a good performance during the gait cycle in controlled spaces and that in this application, the RVEA algorithm results in a better option for optimization to balance the objectives.

Evaluation of Interface Design for Video See-Through Head-Mounted Display: Focusing on Walking Users

Evaluation of Interface Design for Video See-Through Head-Mounted Display: Focusing on Walking Users

Habitually wearing high heels may improve user walking economy in any footwear.

The habitual use of high-heeled footwear may structurally remodel user leg muscle tendons, thereby altering their functional capabilities. High heels set users' ankles in relatively plantarflexed positions, causing calf muscle tendons to operate at relatively short lengths. Habitually operating muscle tendons at relatively short lengths induces structural remodeling that theoretically affects muscle metabolism. Because structural changes occur within the body, the user's locomotor metabolism may change in any footwear condition (e.g., conventional shoes, barefoot). Here, we studied the influence of habitual high-heel use on users' leg muscle-tendon structure and metabolism during walking in flat-soled footwear. We tested eight participants before and after 14 wk of agreeing to wear high heels as their daily shoes. Overall, participants who wore high heels >1,500 steps per day, experienced a 9% decrease in their net metabolic power during walking in flat-soled footwear (d = 1.66, P ≤ 0.049), whereas participants who took <1,000 daily steps in high heels did not (d = 0.44; P = 0.524). Across participants, for every 1,000 daily steps in high heels, net metabolic power during walking in flat-soled footwear decreased 5.3% (r = -0.73; P = 0.040). Metabolic findings were partially explained (r2 = 0.43; P = 0.478) by trending shorter medial gastrocnemius fascicle lengths (d = 0.500, P = 0.327) and increased Achilles tendon stiffness (d = 2.889, P = 0.088). The high-heel intervention did not alter user walking stride kinematics in flat-soled footwear (d ≤ 0.567, P ≥ 0.387). While our limited dataset is unable to establish the mechanisms underlying the high-heel-induced walking economy improvement, it appears that prescribing specific footwear use can be implemented to alter user muscle-tendon properties and augment their function in any shoes.NEW & NOTEWORTHY Habitually wearing high-heeled footwear structurally remodels leg muscle tendons and improves user walking economy, regardless of worn attire.

Redirected Walking on Omnidirectional Treadmill.

Redirected walking (RDW) and omnidirectional treadmill (ODT) are two effective solutions to the natural locomotion interface in virtual reality. ODT fully compresses the physical space and can be used as the integration carrier of all kinds of devices. However, the user experience varies in different directions of ODT, and the premise of interaction between users and integrated devices is a good match between virtual and real objects. RDW technology uses visual cues to guide the user's location in physical space. Based on this principle, combining RDW technology with ODT to guide the user's walking direction through visual cues can effectively improve user experience on ODT and make full use of various devices integrated on ODT. This paper explores the novel prospects of combining RDW technology with ODT and formally puts forward the concept of O-RDW (ODT-based RDW). Two baseline algorithms, i.e., OS2MD (ODT-based steer to multi-direction), and OS2MT (ODT-based steer to multi-target), are proposed to combine the merits of both RDW and ODT. With the help of the simulation environment, this paper quantitatively analyzes the applicable scenarios of the two algorithms and the influence of several main factors on the performance. Based on the conclusions of the simulation experiments, the two O-RDW algorithms are successfully applied in the practical application case of multi-target haptic feedback. Combined with the user study, the practicability and effectiveness of O-RDW technology in practical use are further verified.

F-RDW: Redirected Walking With Forecasting Future Position.

In order to serve better VR experiences to users, existing predictive methods of Redirected Walking (RDW) exploit future information to reduce the number of reset occurrences. However, such methods often impose a precondition during deployment, either in the virtual environment's layout or the user's walking direction, which constrains its universal applications. To tackle this challenge, we propose a mechanism F-RDW that is twofold: (1) forecasts the future information of a user in the virtual space without any assumptions by using the conventional method, and (2) fuse this information while maneuvering existing RDW methods. The backbone of the first step is an LSTM-based model that ingests the user's spatial and eye-tracking data to predict the user's future position in the virtual space, and the following step feeds those predicted values into existing RDW methods (such as MPCRed, S2C, TAPF, and ARC) while respecting their internal mechanism in applicable ways. The results of our simulation test and user study demonstrate the significance of future information when using RDW in small physical spaces or complex environments. We prove that the proposed mechanism significantly reduces the number of resets and increases the traveled distance between resets, hence augmenting the redirection performance of all RDW methods explored in this work. Our project and dataset are available at https://github.com/YonseiCGnA-VR/F-RDW.

Access distance to e-scooters: Analysis of app use and trip data in Stockholm

Users’ access distance to shared micromobility services is an important component of travel patterns, a determinant of travel choices, and input to determining service catchment areas. Users’ willingness to walk to shared micromobility vehicles is increasingly relevant as policymakers regulate shared free-floating e-scooters to designated parking zones. This paper proposes a novel approach to analyze access distances of e-scooters users based on e-scooter app use and trip data for Stockholm, Sweden. Euclidean access and map-based walking distances are derived from the distances between the location where the users opens the app to search for an e-scooter and the trip’s origin. Variations in access and walking distances are analyzed based on time of day, day of week, proximity to public transportation, and geographical distribution. Users walk on average 185 m and have an active walking time of 2.3 min with a median value of 95 m and 2.1 min. Shorter walking distances are observed for trips during the morning and lunch hours compared to the afternoon and at night. Furthermore, users walk slightly longer during the weekend compared to weekdays. Access distances are shortest within a 0–100 m radius to the nearest public transportation station. The suggested catchment area radius for shared e-scooters ranges from 128 m to 203 m, based on the 75th percentile of access distances. A policy implication is the importance of planning parking zones for e-scooters very close to public transportation to encourage multimodal trips.

Vision Transformer Customized for Environment Detection and Collision Prediction to Assist the Visually Impaired.

This paper presents a system that utilizes vision transformers and multimodal feedback modules to facilitate navigation and collision avoidance for the visually impaired. By implementing vision transformers, the system achieves accurate object detection, enabling the real-time identification of objects in front of the user. Semantic segmentation and the algorithms developed in this work provide a means to generate a trajectory vector of all identified objects from the vision transformer and to detect objects that are likely to intersect with the user's walking path. Audio and vibrotactile feedback modules are integrated to convey collision warning through multimodal feedback. The dataset used to create the model was captured from both indoor and outdoor settings under different weather conditions at different times across multiple days, resulting in 27,867 photos consisting of 24 different classes. Classification results showed good performance (95% accuracy), supporting the efficacy and reliability of the proposed model. The design and control methods of the multimodal feedback modules for collision warning are also presented, while the experimental validation concerning their usability and efficiency stands as an upcoming endeavor. The demonstrated performance of the vision transformer and the presented algorithms in conjunction with the multimodal feedback modules show promising prospects of its feasibility and applicability for the navigation assistance of individuals with vision impairment.

A Study on the Development of Interactive Technology for the Convenience of the Visually Impaired

In this paper, as human-computer interface (HCI) technology advances, opportunities are analyzed to provide a better service environment for the visually impaired. The system and user interaction for the visually impaired will become possible through the convergence of technologies that include location-based services, wireless body sensor networks, and smart devices, which can have direct and positive impacts. The system connects to the user's mobility assistance device through a smartphone app and helps the user walk to identify and guide through obstacles. This interactive technology is expected to provide a convenient environment for visually impaired users.

&lt;em&gt;Roblox&lt;/em&gt; and Meta Verch

&lt;em&gt;Roblox&lt;/em&gt; and Meta Verch

Comparative analysis of urban underground public space and user walking paths based on the social network model

Comparative analysis of urban underground public space and user walking paths based on the social network model

WiWalk: Gait-Based Dual-User Identification Using WiFi Device

The rapid development of the Internet of Things (IoT) boosts the spread of intelligent spaces. Biometrics-based user identification has gained great popularity recently, among which gait analysis offers a stable, user-friendly, and economical solution. Thanks to the advancement in wireless sensing technologies, capturing gait characteristics using WiFi signals has become a promising new paradigm. The identification process is contactless, insensitive to lighting conditions, and can reuse the incumbent WiFi infrastructure. In this article, we present a gait-based dual-user identification framework named WiWalk to tackle the difficulty where users walk closely together with mixed effects on WiFi signals. The core of WiWalk is to train a deep neural network that can separate and recover individual signals from the mixed ones. Since the separation process inevitably causes information loss, we carefully design a series of algorithms for interference elimination, segmentation, and feature extraction, to enhance the identification accuracy. We conduct extensive experiments to evaluate WiWalk at different locations and times with users of different ages, genders, clothing, and walking behaviors. WiWalk can reach an accuracy of 94.44%, which is suitable for smart homes or offices with a small user base.

Real-time Allocation of Shared Parking Spaces Based on Deep Reinforcement Learning

&lt;p&gt;Aiming at the parking space heterogeneity problem in shared parking space management, a multi-objective optimization model for parking space allocation is constructed with the optimization objectives of reducing the average walking distance of users and improving the utilization rate of parking spaces, a real-time allocation method for shared parking spaces based on deep reinforcement learning is proposed, which includes a state space for heterogeneous regions, an action space based on policy selection and a reward function with variable coefficients. To accurately evaluate the model performance, dynamic programming is used to derive the theoretical optimal values. Simulation results show that the improved algorithm not only improves the training success rate, but also increases the Agent performance by at least 12.63% and maintains the advantage for different sizes of parking demand, reducing the user walking distance by 53.58% and improving the parking utilization by 6.67% on average, and keeping the response time less than 0.2 seconds.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Gait rehabilitation training robot: A motion-intention recognition approach with safety and convenience

Motion-intention recognition is a vital prerequisite in active training when employing a gait rehabilitation training robot. To accurately recognize the motion intention of the elderly and the people with inconveniences in the human–robot interaction process, a novel approach of motion intention recognition with safety, accuracy, and convenience, including directional intention recognition (DIR) and speed intention recognition (SIR) is proposed in this paper. Firstly, the structures of the gait rehabilitation training robot and its motion-intention recognition system are illustrated. To ensure that the user walks in any desired direction safely and naturally, an improved distance-type fuzzy reasoning algorithm combined with a shake-intent filter and second-order optimization algorithm is proposed. It effectively eliminates the control error caused by body shaking and usage habits in human–robot interaction. Furthermore, by extracting from the pressure sensor data, a novel algorithm, taking advantage of the Gaussian probability density function (PDF)’s characteristics, is proposed for SIR, which does not increase the system complexity. Finally, a multi-directional fuzzy reasoning experiment and a human–robot interaction experiment are implemented. The results show that the algorithm can accurately recognize the motion direction intention and motion speed intention of people with weak motion capability, which also improves the safety and convenience of the interaction approach. The proposed method can be integrated into a walker with similar structures, and the whole system can be applied in hospitals, families, and other places for assisting the elderly and the disabled.

Nonholonomic Virtual Constraints for Control of Powered Prostheses Across Walking Speeds.

This paper presents a method to design a nonholonomic virtual constraint (NHVC) controller that produces multiple distinct stance-phase trajectories for corresponding walking speeds. NHVCs encode velocity-dependent joint trajectories via momenta conjugate to the unactuated degree(s)-of-freedom of the system. We recently introduced a method for designing NHVCs that allow for stable bipedal robotic walking across variable terrain slopes. This work extends the notion of NHVCs for application to variable-cadence powered prostheses. Using the segmental conjugate momentum for the prosthesis, an optimization problem is used to design a single stance-phase NHVC for three distinct walking speed trajectories (slow, normal, and fast). This stance-phase controller is implemented with a holonomic swing phase controller on a powered knee-ankle prosthesis, and experiments are conducted with an able-bodied user walking in steady and non-steady velocity conditions. The control scheme is capable of representing 1) multiple, task-dependent reference trajectories, and 2) walking gait variance due to both temporal and kinematic changes in user motion.

Evaluating Cybersickness of Walking on an Omnidirectional Treadmill in Virtual Reality

Cybersickness is a type of motion sickness that may occur during a virtual reality (VR) experience. Many studies have proposed solutions to mitigate cybersickness, while navigating a virtual environment with controllers or walking over a floor. However, reducing the levels of cybersickness while physically walking on an omnidirectional treadmill has been largely overlooked. In this article, we performed a within-subject study, where 34 novice participants underwent four visual conditions while walking in a virtual maze over an omnidirectional treadmill. In the control condition, the movement speed was reduced of the half compared to a standard navigation speed, a movement speed smoothing was added, and the user’s virtual body was represented. The other three conditions changed one of the visual parameters of the control condition: in the standard speed condition, the speed reduction was not performed; for the no smoothing condition, the smoothing was not performed; and for the no avatar condition, the user’s avatar was removed. Results showed that the standard speed condition was reported to induce a significant level of cybersickness compared to the control and no avatar conditions. Nevertheless, standard speed was also the condition most preferred to navigate a virtual environment. This suggests the need to find a tradeoff between the easiness to move quickly in a virtual environment and the cybersickness that can be induced. We provide a discussion of the obtained results and their implications for the design of VR experiences while users walk upon an omnidirectional treadmill.