Abstract
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.
Highlights
Falling is a leading cause of unintentional injury and death in the elderly [1,2] and can result in impaired mobility, disability, fear of falling and reduced quality of life [3,4,5]
This study presents a novel leg tracking method using a Kalman filter and global nearest neighbor (GNN)-based data association with a variable validation region based on the state of each leg
This study presents a gait measurement system using a laser range sensor (LRS) for the multi-target stepping task (MTST) to evaluate the risk of falling
Summary
Falling is a leading cause of unintentional injury and death in the elderly [1,2] and can result in impaired mobility, disability, fear of falling and reduced quality of life [3,4,5]. Since the measurement of the effects of this training is carried out by observation in actual community health centers, it is difficult to quantitatively evaluate the capability of the participants To overcome these problems, an ultrasonic sensor, a laser range sensor (LRS) [21] or a RGB-Depth sensor such as the Microsoft Kinect [22] can be used. In gait training, to avoid the risk of falling for some participants during the MTST, a nursing attendant walks alongside the participant and the participant uses a stick if they use one normally Both legs could be close to each other because of a narrow stride, or one leg might be hidden from the sensor owing to the increased number of cross steps in the high-risk elderly. We confirmed the validity of walking parameters such as foot contact time and position obtained by the proposed system from the results of the target step judgment
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