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首页> 外文会议>European Conference of the Association for the Advancement of Assistive Technology in Europe >Assessing Gait Impairments Based on Auto-Encoded Patterns of Mahalanobis Distances from Consecutive Steps
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Assessing Gait Impairments Based on Auto-Encoded Patterns of Mahalanobis Distances from Consecutive Steps

机译：基于连续步骤的自动编码模式的基于自动编码模式的步态障碍

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摘要

Insole pressure sensors capture the force distribution patterns during the stance phase while walking. By comparing patterns obtained from healthy individuals to patients suffering different medical conditions based on a given similarity measure, automatic impairment indexes can be computed in order to help in applications such as rehabilitation. This paper uses the data sensed from insole pressure sensors for a group of healthy controls to train an auto-encoder using patterns of stochastic distances in series of consecutive steps while walking at normal speeds. Two experiment groups are compared to the healthy control group: a group of patients suffering knee pain and a group of post-stroke survivors. The Mahalanobis distance is computed for every single step by each participant compared to the entire dataset sensed from healthy controls. The computed distances for consecutive steps are fed into the previously trained autoencoder and the average error is used to assess how close the walking segment is to the autogenerated model from healthy controls. The results show that automatic distortion indexes can be used to assess each participant as compared to normal patterns computed from healthy controls. The stochastic distances observed for the group of stroke survivors are bigger than those for the people with knee pain.
机译：鞋垫压力传感器在步行时捕获力分布模式。通过将从健康个体获得的模式与基于给定的相似度措施的患者患有不同的医疗条件，可以计算自动损伤指标，以帮助在康复等应用中。本文使用鞋垫压力传感器感测的数据，用于一组健康的控制，以在正常速度行走时使用随机距离的随机距离图案培训自动编码器。将两种实验组与健康对照组进行比较：一群患有膝关节疼痛的患者和一组后卒中后幸存者。与从健康控制感测的整个数据集相比，每个参与者计算Mahalanobis距离。连续步骤的计算距离被馈送到先前培训的AutoEncoder中，并且平均误差用于评估步行段与健康控制的自动化模型的接近程度。结果表明，与从健康控制计算的正常模式相比，可以使用自动失真索引来评估每个参与者。对中风幸存者组观察到的随机距离大于膝关节疼痛的人的距离。

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