Ayanori Yorozu

Improved Leg Tracking Considering Gait Phase and Spline-Based Interpolation during Turning Motion in Walk Tests.

Falling is a common problem in the growing elderly population, and fall-risk assessment systems are needed for community-based fall prevention programs. In particular, the timed up and go test (TUG) is the clinical test most often used to evaluate elderly individual ambulatory ability in many clinical institutions or local communities. This study presents an improved leg tracking method using a laser range sensor (LRS) for a gait measurement system to evaluate the motor function in walk tests, such as the TUG. The system tracks both legs and measures the trajectory of both legs. However, both legs might be close to each other, and one leg might be hidden from the sensor. This is especially the case during the turning motion in the TUG, where the time that a leg is hidden from the LRS is longer than that during straight walking and the moving direction rapidly changes. These situations are likely to lead to false tracking and deteriorate the measurement accuracy of the leg positions. To solve these problems, a novel data association considering gait phase and a Catmull–Rom spline-based interpolation during the occlusion are proposed. From the experimental results with young people, we confirm that the proposed methods can reduce the chances of false tracking. In addition, we verify the measurement accuracy of the leg trajectory compared to a three-dimensional motion analysis system (VICON).

Human-centered X - Y - T space path planning for mobile robot in dynamic environments

An autonomous mobile robot in a humans living space should be able to realize not only collision-free motion, but also human-centered motion, i.e., motion giving priority to a moving human according to the situation. In this study, we propose a real-time obstacle avoidance method for an autonomous mobile robot that considers the robots dynamic constraints, personal space, and the humans directional area using grid-based X - Y - T space path planning. The proposed method generates collision-free motion in which the robot can give way to humans. The relative position, velocity and avoidance motion with respect to the robot varies from person to person. To show the effectiveness of the proposed method to human-like motion, we verify the robots motion under several assumed scenarios by changing the initial state of both the robot and the human. Moreover, we verify the robots motion with respect to a simulated human based on the human-like behavior approach. Through these simulations, we confirm that the proposed method is able to generate safe human-centered motion under several assumed scenarios. Additionally, the effectiveness of the proposed method in practice is confirmed by experiments in which the humans position and velocity are estimated using a laser range finder. This paper presents safe human-centered navigation for a mobile robot.Our approach is X - Y - T space path planning considering the dynamic constraints.We provide collision-free motion in which the robot can give way to humans.The effectiveness of the proposed method is confirmed by simulations and experiments.

Gait Measurement System for the Elderly Using Laser Range Sensor

Gait measurement is important in various applications such as monitoring systems for the elderly. This paper presents a gait measurement system applicable to the elderly using a laser range sensor (LRS). An LRS can obtain high accuracy distance data over a wide range and leg position can be calculated based on characteristic leg patterns from the scan data. However, situations in which a leg is hidden from the LRS or both legs are too close together lead to false tracking or losing track of both legs entirely. In the case of the elderly in particular, these situations are likely to occur due to slow movement or narrow stride. To solve these problems, we present a novel leg detection method with five observed leg patterns and global nearest neighbor (GNN)-based data association, using a variable gate based on the state of each leg. Experimental results of several elderly people show that the proposed system can reduce the chances of both false tracking and losing track of both legs, and can acquire the accurate trajectory of both legs.

Development of gait measurement robot using laser range sensor for evaluating long-distance walking ability in the elderly

Falling is a common problem in the growing elderly population and fall-risk assessment systems are needed for community-based fall prevention program. In particular, gait measurements such as several-meters walk tests are carried out in community health activities. To evaluate the walking ability of the participant, it is necessary to measure foot contact positions so that the walking parameters such as stride length can be used for fall-risk assessment. However, the conventional measurement systems are difficult to install for use in community health activities because of their scale, cost and constraints of the measurement range. Therefore, we propose a gait measurement robot (GMR) using laser range sensor (LRS) for a long-distance walk tests. From the experimental results with young people, it was confirmed that the GMR could measure the both leg trajectory and the foot contact positions. However, in the case of the elderly especially, a false tracking is likely to occur due to the narrow stride. In addition, the GMR calculates the foot contact positions by analyzing the estimated position and speed of each leg. In the case of the elderly, there is a possibility that the GMR cannot detect the foot contact correctly because the walking speed is likely to be slow. In this study, we carry out the seven-meter straight walk tests with the elderly people using a stationary LRS for the advance verification of measurement in the elderly with the GMR. We verify the leg tracking and foot contact detection using a stationary LRS in the elderly compared with the video analysis.

Gait measurement system for the multi-target stepping task using a laser range sensor.

For the prevention of falling in the elderly, gait training has been proposed using tasks such as the multi-target stepping task (MTST), in which participants step on assigned colored targets. This study presents a gait measurement system using a laser range sensor for the MTST to evaluate the risk of falling. The system tracks both legs and measures general walking parameters such as stride length and walking speed. Additionally, it judges whether the participant steps on the assigned colored targets and detects cross steps to evaluate cognitive function. However, situations in which one leg is hidden from the sensor or the legs are close occur and are likely to lead to losing track of the legs or false tracking. To solve these problems, we propose a novel leg detection method with five observed leg patterns and global nearest neighbor-based data association with a variable validation region based on the state of each leg. In addition, methods to judge target steps and detect cross steps based on leg trajectory are proposed. From the experimental results with the elderly, it is confirmed that the proposed system can improve leg-tracking performance, judge target steps and detect cross steps with high accuracy.

Development of gait measurement robot for prevention of falls in the elderly

To prevent falls in the elderly, gait measurements such as several-meters walking test and gait trainings are carried out in community health activities. To evaluate the risk of falling of the participant, it is necessary to measure foot contact times and positions so that the stride length of each leg and the walking speed can be used as evaluation parameters. However, the conventional measurement systems are difficult to install for use in community health activities because of their scale, cost and constraints of the measurement range. In this study, we propose a novel gait measurement system which uses an autonomous mobile robot with laser range sensor (LRS) for a long-distance walking test in a real living space regardless of detection range of sensor. The robot sequentially estimates its own pose and acquires the position of both legs of the participant. The robot leads the participant from the start to the goal of the walking test while maintaining a certain distance from the participant. Then, the foot contact times and the positions are calculated by analyzing estimated position and speed of each leg. From the experimental results, it was confirmed that the proposed robot could acquire the foot contact times and positions.

Markerless Knee Joint Position Measurement Using Depth Data during Stair Walking

Climbing and descending stairs are demanding daily activities, and the monitoring of them may reveal the presence of musculoskeletal diseases at an early stage. A markerless system is needed to monitor such stair walking activity without mentally or physically disturbing the subject. Microsoft Kinect v2 has been used for gait monitoring, as it provides a markerless skeleton tracking function. However, few studies have used this device for stair walking monitoring, and the accuracy of its skeleton tracking function during stair walking has not been evaluated. Moreover, skeleton tracking is not likely to be suitable for estimating body joints during stair walking, as the form of the body is different from what it is when it walks on level surfaces. In this study, a new method of estimating the 3D position of the knee joint was devised that uses the depth data of Kinect v2. The accuracy of this method was compared with that of the skeleton tracking function of Kinect v2 by simultaneously measuring subjects with a 3D motion capture system. The depth data method was found to be more accurate than skeleton tracking. The mean error of the 3D Euclidian distance of the depth data method was 43.2 ± 27.5 mm, while that of the skeleton tracking was 50.4 ± 23.9 mm. This method indicates the possibility of stair walking monitoring for the early discovery of musculoskeletal diseases.

Ground reaction force estimation using insole plantar pressure measurement system from single-leg standing

A long-range, continuous, and accessible kinetic measurement system is required for evaluating gait disorders. Although methods employing force plates are gold standard in kinetic gait analysis, their usage is often limited by their measurement range, and they are cost-prohibitive for general clinics. Instrumented insole-based gait analysis systems using accessible sensors were proposed in previous works. However, these systems rely on the use of force plates to construct models to estimate ground reaction force. In this study, a method to construct models without using force plates was developed and evaluated. Subject-specific linear least squares regression models (with bounds and linear constraints) using data from two types of single-leg standing (SLS) tasks; static SLS and voluntary weight shift during SLS were used to determine ground reaction force. Comparison of the results with force plate data for straight walking in terms of the %RMSE, which indicates estimation accuracy of the models, showed that the results were about the same accurate as the models using force plates. In addition, we found possibility that voluntary weight shift during SLS can improve estimation accuracy of models.

Gait parameters extraction by using mobile robot equipped with Kinect v2

The needs for monitoring systems to be used in houses are getting stronger because of the increase of the single household population due to the low birth rate and longevity. Among others, gait parameters are under the spotlight to be examined as the relations with several diseases have been reported. It is known that the gait parameters obtained at a walk test are different from those obtained under the daily life. Thus, the system which can measure the gait parameters in the real living environment is needed. Generally, gait abilities are evaluated by a measurement test, such as Timed Up and Go test and 6-minute walking test. However, these methods need measurers, so the accuracy depends on them and the lack of objectivity is pointed out. Although, a precise motion capture system is used for more objective measurement, it is hard to be used in daily measurement, because the subjects have to put the markers on their body. To solve this problem, marker less sensors, such as Kinect, are developed and used for gait information acquisition. When they are attached to a mobile robot, there is no limitation of distance. However, they still have challenges of calibration for gait parameters, and the important gait parameters to be acquired are not well examined. Therefore, in this study, we extract the important parameters for gait analysis, which have correlations with diseases and age differences, and suggest the gait parameters extraction from depth data by Kinect v2 which is mounted on a mobile robot aiming at applying to the living environment.

3D X-Y-T space path planning for autonomous mobile robots considering dynamic constraints

An autonomous mobile robot in a human living space should be able to not only realize collision-free motion but also give way to humans depending on the situation. Although various reactive obstacle avoidance methods have been proposed, it is difficult to achieve such motion. On the other hand, 3D X-Y-T space path planning, which takes into account the motion of both the robot and the human in a look-ahead time horizon, is effective. This paper proposes a real-time obstacle avoidance method for an autonomous mobile robot that considers the robots dynamic constraints, the personal space, and human directional area based on grid-based 3D X-Y-T space path planning. The proposed method generates collision-free motion in which the robot can yield to humans. To verify the effectiveness of the proposed method, various experiments in which the humans position and velocity were estimated using laser range finders were carried out.