Walking Distance Estimation Research Articles

Disentangling Cerebellar and Parietal Contributions to Gait and Body Schema: A Repetitive Transcranial Magnetic Stimulation Study

The overlap between motor and cognitive signs resulting from posterior parietal cortex (PPC) and cerebellar lesions can mask their relative contribution in the sensorimotor integration process. This study aimed to identify distinguishing motor and cognitive features to disentangle PPC and cerebellar involvement in two sensorimotor-related functions: gait and body schema representation. Thirty healthy volunteers were enrolled and randomly assigned to PPC or cerebellar stimulation. Sham stimulation and 1 Hz-repetitive-Transcranial-Magnetic-Stimulation were delivered over P3 or cerebellum before a balance and a walking distance estimation task. Each trial was repeated with eyes open (EO) and closed (EC). Eight inertial measurement units recorded spatiotemporal and kinematic variables of gait. Instability increased in both groups after real stimulation: PPC inhibition resulted in increased instability in EC conditions, as evidenced by increased ellipse area and range of movement in medio-lateral and anterior–posterior (ROMap) directions. Cerebellar inhibition affected both EC (increased ROMap) and EO stability (greater displacement of the center of mass). Inhibitory stimulation (EC vs. EO) affected also gait spatiotemporal variability, with a high variability of ankle and knee angles plus different patterns in the two groups (cerebellar vs parietal). Lastly, PPC group overestimates distances after real stimulation (EC condition) compared to the cerebellar group. Stability, gait variability, and distance estimation parameters may be useful clinical parameters to disentangle cerebellar and PPC sensorimotor integration deficits. Clinical differential diagnosis efficiency can benefit from this methodological approach.

Walking Distance Estimation Using a Shank-Mounted Inertial Measurement Unit and Vertical Distance Sensor

This paper investigates whether adding a vertical distance sensor to a shank-mounted inertialmeasurement unit can improvewalking distance estimation accuracy. Some important features that can be extracted from the vertical distance sensor and a gyroscope were proposed, and the model was validated with personalized and generalized parameters. Results showthat the vertical distance sensor is unnecessary for personalized model, but can retain the estimation accuracy of generalized model at 96%. As a comparison, many existing methods yield high accuracy for personalized model, but some has the accuracy reduced by 5-10% for generalized model.

Spatial orientation in virtual environment compared to real-world

Virtual reality (VR) is popular across many disciplines and has been increasingly used in sports as a training tool lately. However, it is not clear whether the spatial orientation of humans works equally within VR and in the real-world. In this paper, two studies are presented, in which natural body movements were allowed and demanded. Firstly, a series of verbal and walking distance estimation tests were conducted in both the virtual and the real environment. The non-parametric Friedman test with pairwise comparisons showed no significant differences neither in verbal nor in walking distance estimations between the conditions (all p > 0.05). However, shorter distances (0.9-1.5 m) were estimated more precisely than larger distances (2.6-2.8 m) in both environments. Secondly, a self-developed route recall test to examine the spatial orientation was performed in the virtual and the real environment. The participants visually perceived the predefined route and were instructed to follow these routes with their eyes blindfolded and afterward to return to their starting position. Between the ending and the starting position, no difference between the two environments was observed (p > 0.05). Based on these two studies, the performance of the human spatial orientation preliminarily verified the same in a virtual and real environment.

Histogram Feature-Based Approach for Walking Distance Estimation Using a Waist-Mounted IMU

This paper presents a new method of walking distance estimation using an inertial measurement unit (IMU) placed on the user's waist. When the sensor is mounted on the waist, the walking steps can be easily detected. However, step length estimation is a challenging task. In this paper, a walking distance estimation method based on histogram features is proposed. The histogram features, including the uniform and logarithmic quantizations, are derived from each step segment of the acceleration norm data. Then, machine learning algorithms such as support vector regression (SVR), gaussian process regression (GPR), and linear regression (LR) are applied based on the histogram features to estimate the walking distance. Two experiments are conducted to evaluate the walking distance estimation accuracy, where test walking paths consist of a straight line corridor (80 m) and a rectangular path (about 1282 m). Experimental results show that the average absolute errors using the SVR model are 0.76% for straight line corridors and 1.14% for the rectangular paths. The proposed method is shown to outperform existing methods from comparison study.

Human Gait Tracking for Normal People and Walker Users Using a 2D LiDAR

This paper proposes a human gait tracking method using a low scanning rate (10Hz) 360 degree 2D LiDAR. The sensor is placed at a height of 0.3m above the ground. Since the human gait tracking is important not only for normal people but also for patients who need to use walking-aid such as walker, we propose a tracking method for both normal people and walker users. The left and right legs are separated and tracked even with nonlinear walking path. To overcome the occlusions between legs when one leg might be hidden from the sensor, a spline-based smooth and interpolation method is applied to estimate the legs trajectories. An algorithm is also proposed to remove the walker legs data in case of walker users. The performance of proposed tracking method is investigated in different walking speed levels and two positions of LiDAR (parallel and horizontal to walking direction). The experimental results verify the robustness of our proposed method. The horizontal position of LiDAR gives better estimation than the parallel position. The RMSE is less than 2.0cm for walking step length estimation and is less than 1.4cm for walking distance estimation. The tracking range of our system is up to 10m with horizontal placement of LiDAR.

Opportunistic Calibration Method for Walking Distance Estimation Using a Waist-Mounted Inertial Sensor

This article introduces an opportunistic calibration method for walking distance estimation using a waist-mounted inertial sensor. In the proposed method, the calibration data are automatically collected from daily normal walking and it does not require a laboratory calibration procedure. While the walking steps can be accurately detected, the walking step length estimation is more difficult in a waist-mounted sensor case. In our method, when short walking data are detected from normal walking, a smoothing algorithm is used to estimate its walking distance. The walking step length and walking features of these data are used to update the step length model. Two experiments are conducted to evaluate the performance of the proposed method. The accuracy of smoother-based short walking distance estimation has been investigated in the first experiment. This experiment also investigates which feature is the best fit for our linear step length model. The second experiment is to demonstrate the opportunistic nature of the proposed method; 3–10 walking steps segments are extracted from 2 h walking data of one healthy subject and used to calibrate his walking profile. This method can be applied to a consumer device for human walking distance estimation without laboratory calibration procedure.

Investigating urban metro stations as cognitive places in cities using points of interest

The significance of urban metro stations extends beyond their roles as transport nodes in a city. Their surroundings are usually well-developed and attract a lot of human activities, which make the metro station areas important cognitive places characterized by vague boundaries and rich semantics. Current studies mainly define metro station areas based on an estimation of walking distance to the stations (e.g., 700 m) and investigate these areas from the perspectives of transportation and land use instead of as cognitive places perceived by the crowd. To fill this gap, this study proposes a novel framework for extracting and understanding the cognitive regions of urban metro stations based on points of interest (POIs). First, we extract the cognitive regions of metro stations based on co-occurrence patterns of the stations and their surrounding POIs on web pages by proposing a cohesive approach combined of spatial clustering, web page extraction, knee-point detection, and polygon generation techniques. Second, we identify the semantics of metro stations based on POI types inside the regions using the term frequency-inverse document frequency (TF-IDF) method. In total 166 metro stations along with more than one million POIs in Shenzhen, China are utilized as data sources of the case study. The results indicate that our proposed framework can well detect the place characteristics of urban metro stations, which enriches the place-based GIS research and provides a human-centric perspective for urban planning and location-based-service (LBS) applications.

Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition

Stride length and walking distance estimation are becoming a key aspect of many applications. One of the methods of enhancing the accuracy of pedestrian dead reckoning is to accurately estimate the stride length of pedestrians. Existing stride length estimation (SLE) algorithms present good performance in the cases of walking at normal speed and the fixed smartphone mode (handheld). The mode represents a specific state of the carried smartphone. The error of existing SLE algorithms increases in complex scenes with many mode changes. Considering that stride length estimation is very sensitive to smartphone modes, this paper focused on combining smartphone mode recognition and stride length estimation to provide an accurate walking distance estimation. We combined multiple classification models to recognize five smartphone modes (calling, handheld, pocket, armband, swing). In addition to using a combination of time-domain and frequency-domain features of smartphone built-in accelerometers and gyroscopes during the stride interval, we constructed higher-order features based on the acknowledged studies (Kim, Scarlett, and Weinberg) to model stride length using the regression model of machine learning. In the offline phase, we trained the corresponding stride length estimation model for each mode. In the online prediction stage, we called the corresponding stride length estimation model according to the smartphone mode of a pedestrian. To train and evaluate the performance of our SLE, a dataset with smartphone mode, actual stride length, and total walking distance were collected. We conducted extensive and elaborate experiments to verify the performance of the proposed algorithm and compare it with the state-of-the-art SLE algorithms. Experimental results demonstrated that the proposed walking distance estimation method achieved significant accuracy improvement over existing individual approaches when a pedestrian was walking in both indoor and outdoor complex environments with multiple mode changes.

Walking Distance Estimation Using Walking Canes with Inertial Sensors.

A walking distance estimation algorithm for cane users is proposed using an inertial sensor unit attached to various positions on the cane. A standard inertial navigation algorithm using an indirect Kalman filter was applied to update the velocity and position of the cane during movement. For quadripod canes, a standard zero-velocity measurement-updating method is proposed. For standard canes, a velocity-updating method based on an inverted pendulum model is proposed. The proposed algorithms were verified by three walking experiments with two different types of canes and different positions of the sensor module.

Walking distance estimation using multi-agent simulation of pedestrian flows

When choosing locations for certain organizations in the city, the most advantageous sites need to be found by taking into account the specifics of the business and the need to achieve greater customer coverage. One of the ways to choose such a location is to analyze isochrone maps, i.e. maps of isolines showing the places that can be reached within the specified time. The existing methods of creating such maps have some disadvantages: open cartographic data on pedestrian paths are incomplete for some areas; desire paths are not considered when creating an isochrone map because these are not marked on maps. In this paper we propose a method and a tool for creating an isochrone map, which is built upon agent-based modeling. This isochrone map combined with agent-based modeling considers behavioral characteristics of pedestrian traffic and shows higher accuracy and reliability of the results compared to classical methods of isochrone map creation.

Step-Detection and Adaptive Step-Length Estimation for Pedestrian Dead-Reckoning at Various Walking Speeds Using a Smartphone.

We propose a walking distance estimation method based on an adaptive step-length estimator at various walking speeds using a smartphone. First, we apply a fast Fourier transform (FFT)-based smoother on the acceleration data collected by the smartphone to remove the interference signals. Then, we analyze these data using a set of step-detection rules in order to detect walking steps. Using an adaptive estimator, which is based on a model of average step speed, we accurately obtain the walking step length. To evaluate the accuracy of the proposed method, we examine the distance estimation for four different distances and three speed levels. The experimental results show that the proposed method significantly outperforms conventional estimation methods in terms of accuracy.

Stride Counting in Human Walking and Walking Distance Estimation Using Insole Sensors.

This paper proposes a novel method of estimating walking distance based on a precise counting of walking strides using insole sensors. We use an inertial triaxial accelerometer and eight pressure sensors installed in the insole of a shoe to record walkers’ movement data. The data is then transmitted to a smartphone to filter out noise and determine stance and swing phases. Based on phase information, we count the number of strides traveled and estimate the movement distance. To evaluate the accuracy of the proposed method, we created two walking databases on seven healthy participants and tested the proposed method. The first database, which is called the short distance database, consists of collected data from all seven healthy subjects walking on a 16 m distance. The second one, named the long distance database, is constructed from walking data of three healthy subjects who have participated in the short database for an 89 m distance. The experimental results show that the proposed method performs walking distance estimation accurately with the mean error rates of 4.8% and 3.1% for the short and long distance databases, respectively. Moreover, the maximum difference of the swing phase determination with respect to time is 0.08 s and 0.06 s for starting and stopping points of swing phases, respectively. Therefore, the stride counting method provides a highly precise result when subjects walk.

Tactile and proprioceptive sensory stimulation modifies estimation of walking distance but not upright gait stability: a pilot study.

[Purpose] Recently, there has been growing interest in the somatosensory system, but little data exist on the interaction between dynamic postural control and the somatosensory system. The purpose of this study was to determine whether a training program, based on tactile and proprioceptive sensory stimulation of the trunk with the use of perceptual surfaces, improved the estimation of walking distance by healthy subjects, the ability to walk toward a memorized distance without vision, and whether it increases upright gait stability. [Subjects and Methods] Ten healthy subjects with a mean age of 31.9 ± 2.5 years were enrolled and participated in 10 daily sessions of perceptive training using perceptual surfaces, for 45 minutes each session. An experimental indoor test measured the subjects’ ability to perceive walking distances to a memorized target in an indoor environment. [Results] After treatment, the distances that were traversed were closer to the target than before treatment. Trunk acceleration did not differ significantly between pre- and post-training and did not increase significantly after training. [Conclusion] Treatment with perceptual surfaces stimulating the trunk midline improves the estimation of walking distance and modifies proprioceptive gait patterns, allowing various corrective strategies to be implemented during ambulation.

Axis-Exchanged Compensation and Gait Parameters Analysis for High Accuracy Indoor Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is an effective way for navigation coupled with GNSS (Global Navigation Satellite System) or weak GNSS signal environment like indoor scenario. However, indoor location with an accuracy of 1 to 2 meters determined by PDR based on MEMS-IMU is still very challenging. For one thing, heading estimation is an important problem in PDR because of the singularities. For another thing, walking distance estimation is also a critical problem for pedestrian walking with randomness. Based on the above two problems, this paper proposed axis-exchanged compensation and gait parameters analysis algorithm to improve the navigation accuracy. In detail, an axis-exchanged compensation factored quaternion algorithm is put forward first to overcome the singularities in heading estimation without increasing the amount of computation. Besides, real-time heading is updated by R-adaptive Kalman filter. Moreover, gait parameters analysis algorithm can be divided into two steps: cadence detection and step length estimation. Thus, a method of cadence classification and interval symmetry is proposed to detect the cadence accurately. Furthermore, a step length model adjusted by cadence is established for step length estimation. Compared to the traditional PDR navigation, experimental results showed that the error of navigation reduces 32.6%.

Manipulating perception versus action in recalibration tasks

We conducted six experiments to examine how manipulating perception versus action affects perception-action recalibration in real and imagined blindfolded walking tasks. Participants first performed a distance estimation task (pretest) and then walked through an immersive virtual environment on a treadmill for 10min. Participants then repeated the distance estimation task (posttest), the results of which were compared with their pretest performance. In Experiments 1a, 2a, and 3a, participants walked at a normal speed during recalibration, but the rate of visual motion was either twice as fast or half as fast as the participants' walking speed. In Experiments 1b, 2b, and 3b, the rate of visual motion was kept constant, but participants walked at either a faster or a slower speed. During pre- and posttest, we used either a blindfolded walking distance estimation task or an imagined walking distance estimation task. Additionally, participants performed the pretest and posttest distance estimation tasks in either the real environment or the virtual environment. With blindfolded walking as the distance estimation task for pre- and posttest, we found a recalibration effect when either the rate of visual motion or the walking speed was manipulated during the recalibration phase. With imagined walking as the distance estimation task, we found a recalibration effect when the rate of visual motion was manipulated, but not when the walking speed was manipulated in both the real environment and the virtual environment. Discussion focuses on how spatial-updating processes operate on perception and action and on representation and action.

Modeling isoexposure to transit users for market potential analysis

Transit operators face a difficult fiscal environment and an imperative to contribute to urban sustainability. Under these circumstances, operators must find innovative ways to make public transportation attractive to broader segments of the public, while simultaneously trying to raise revenue to reduce reliance on public subsidies. Development of commercial partnerships is seen as a promising way to achieve these goals. Previous research has examined the potential of using geodemographics to assist transit agencies in the task of identifying potential partners for developing mutually beneficial commercial agreements. In this paper we describe an approach to model isoexposure to transit users as a tool to assess market potential. The approach is based on the analysis of walking behavior of transit users, and specifically distance walked at the end of their transit trip. Spatial modeling is used to geographically project estimates of walking distance for a desired demographic profile at a specific transit facility. After expanding the estimates using sample weights, overlays of these estimates can be used to generate variations in exposure to transit travelers at different locations in space. The approach is demonstrated using the case of Metro users in Montreal, Canada. The case study demonstrates the use of isoexposure profiles as a novel approach to generate marketing intelligence. This should be of interest to transit agencies and businesses interested in developing partnerships.

Walking Pattern Classification and Walking Distance Estimation Algorithms Using Gait Phase Information

This paper presents a walking pattern classification and a walking distance estimation algorithm using gait phase information. A gait phase information retrieval algorithm was developed to analyze the duration of the phases in a gait cycle (i.e., stance, push-off, swing, and heel-strike phases). Based on the gait phase information, a decision tree based on the relations between gait phases was constructed for classifying three different walking patterns (level walking, walking upstairs, and walking downstairs). Gait phase information was also used for developing a walking distance estimation algorithm. The walking distance estimation algorithm consists of the processes of step count and step length estimation. The proposed walking pattern classification and walking distance estimation algorithm have been validated by a series of experiments. The accuracy of the proposed walking pattern classification was 98.87%, 95.45%, and 95.00% for level walking, walking upstairs, and walking downstairs, respectively. The accuracy of the proposed walking distance estimation algorithm was 96.42% over a walking distance.

Estimating daily walking distance of captive African elephants using an accelerometer

Two central concerns for elephant husbandry and management are whether zoological enclosures are appropriately sized and the degree to which naturalistic exercise and activity are observed in such enclosures. In order to address these issues, accurate data on the daily walking distance of elephants both in situ and ex situ are necessary. We used an accelerometer, a pedometer that measures step count and activity level, to estimate walking distance in African elephants (Loxodonta africana) at the San Diego Zoo's Wild Animal Park. The accelerometer was worn simultaneously with a GPS unit that recorded actual walking distance. Estimates of walking distance were extrapolated from the accelerometer and compared with actual distances determined by GPS data. The accelerometer was found to overestimate step count, and subsequently walking distance, by including false counts of steps. Extrapolating walking distance based upon stride length measurements did not match actual GPS walking distance. However, activity level output from the accelerometer significantly correlated with actual GPS walking distance. In addition, we report that the rate of movement is comparable to that reported in other zoological settings. We provide a linear regression equation that can be utilized by other institutions to estimate daily walking distance of elephants in their collection who are outfitted with accelerometers.

A Mobile Pedestrian Dead Reckoning System

A pedestrian dead reckoning system estimates a pedestrian's position relative to a known location, and has a variety of applications. It is commonly used with a GPS receiver; however, the GPS signals may be affected by many factors and consequently unable to position correctly. This paper presents a pedestrian dead reckoning system that includes an inertial module, a GPS receiver module, and a dead reckoning module. The inertial module detects the acceleration of a step and heading. Step length is difficult to determine since it is affected by many factors and varies from person to person. Accurate stride estimation is significant in a pedestrian dead reckoning system. When the GPS is able to position, the Particle Swarm Optimization algorithm is applied to discover a pedestrian's step length coefficient. The obtained coefficient and the detected acceleration signal are used to determine the stride of a pedestrian. From the information of step length and heading, the dead reckoning can be processed without the GPS reception. The experimental results have demonstrated that the system presents good performances in either walking distance estimation or walking trajectory determination without knowing a pedestrian's leg length or assistance of wireless sensor network.

Locomotor body scheme

The concept of body schema has been introduced and widely discussed in the literature to explain various clinical observations and distortions in the body and space representation. Here we address the role of body schema related information in multi-joint limb motion. The processing of proprioceptive information may differ significantly in static and dynamic conditions since in the latter case the control system may employ specific dynamic rules and constraints. Accordingly, the perception of movement, e.g., estimation of step length and walking distance, may rely on a priori knowledge about intrinsic dynamics of limb segment motion and inherent relationships between gait parameters and body proportions. The findings are discussed in the general framework of space and body movement representation and suggest the existence of a dynamic locomotor body schema used for controlling step length and path estimation.