Suppr超能文献

基于定制电容传感器足底压力测量系统的步态分段方法。

Gait Segmentation Method Using a Plantar Pressure Measurement System with Custom-Made Capacitive Sensors.

机构信息

Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, 219 Edmundo Larenas St., Concepción 4030000, Chile.

出版信息

Sensors (Basel). 2020 Jan 24;20(3):656. doi: 10.3390/s20030656.

Abstract

Gait analysis has been widely studied by researchers due to the impact in clinical fields. It provides relevant information on the condition of a patient's pathologies. In the last decades, different gait measurement methods have been developed in order to identify parameters that can contribute to gait cycles. Analyzing those parameters, it is possible to segment and identify different phases of gait cycles, making these studies easier and more accurate. This paper proposes a simple gait segmentation method based on plantar pressure measurement. Current methods used by researchers and clinicians are based on multiple sensing devices (e.g., multiple cameras, multiple inertial measurement units (IMUs)). Our proposal uses plantar pressure information from only two sensorized insoles that were designed and implemented with eight custom-made flexible capacitive sensors. An algorithm was implemented to calculate gait parameters and segment gait cycle phases and subphases. Functional tests were performed in six healthy volunteers in a 10 m walking test. The designed in-shoe insole presented an average power consumption of 44 mA under operation. The system segmented the gait phases and sub-phases in all subjects. The calculated percentile distribution between stance phase time and swing phase time was almost 60%/40%, which is aligned with literature reports on healthy subjects. Our results show that the system achieves a successful segmentation of gait phases and subphases, is capable of reporting COP velocity, double support time, cadence, stance phase time percentage, swing phase time percentage, and double support time percentage. The proposed system allows for the simplification of the assessment method in the recovery process for both patients and clinicians.

摘要

步态分析由于其在临床领域的影响而受到研究人员的广泛研究。它提供了有关患者病理状况的相关信息。在过去的几十年中,已经开发出了不同的步态测量方法,以便识别可以有助于步态周期的参数。分析这些参数,可以对步态周期的不同阶段进行分段和识别,从而使这些研究更容易和更准确。本文提出了一种基于足底压力测量的简单步态分段方法。研究人员和临床医生目前使用的方法基于多个感测设备(例如,多个摄像头,多个惯性测量单元(IMU))。我们的建议仅使用两个传感器鞋垫使用来自两个传感器鞋垫的足底压力信息,这些鞋垫是使用八个定制的柔性电容传感器设计和实现的。实施了一种算法来计算步态参数并分段步态周期阶段和子阶段。在 10 米步行测试中,对六名健康志愿者进行了功能测试。设计的鞋内鞋垫在运行时的平均功耗为 44 mA。该系统在所有受试者中都对步态阶段和子阶段进行了分段。计算出的支撑相时间和摆动相时间之间的百分比分布几乎为 60%/40%,与健康受试者的文献报告一致。我们的结果表明,该系统能够成功地对步态阶段和子阶段进行分段,能够报告 COP 速度,双支撑时间,步频,支撑相时间百分比,摆动相时间百分比和双支撑时间百分比。所提出的系统允许简化患者和临床医生在康复过程中的评估方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9a7/7038314/ddf8d92a695e/sensors-20-00656-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验