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基于惯性传感器的助行器行走距离估计

Walking Distance Estimation Using Walking Canes with Inertial Sensors.

机构信息

Electrical Engineering Department, University of Ulsan, Ulsan 44610, Korea.

出版信息

Sensors (Basel). 2018 Jan 15;18(1):230. doi: 10.3390/s18010230.

DOI:10.3390/s18010230
PMID:29342971
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5795858/
Abstract

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.

摘要

提出了一种使用安装在拐杖上不同位置的惯性传感器单元来估算拐杖使用者行走距离的算法。应用了一种使用间接卡尔曼滤波器的标准惯性导航算法,以在运动过程中更新拐杖的速度和位置。对于四足拐杖,提出了一种标准的零速度测量更新方法。对于标准拐杖,提出了一种基于倒立摆模型的速度更新方法。通过三个使用两种不同类型的拐杖和传感器模块不同位置的行走实验对所提出的算法进行了验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dbc/5795858/4d4f919c6d22/sensors-18-00230-g017.jpg
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本文引用的文献

1
Step-Detection and Adaptive Step-Length Estimation for Pedestrian Dead-Reckoning at Various Walking Speeds Using a Smartphone.使用智能手机在不同步行速度下进行行人航位推算的步检测与自适应步长估计
Sensors (Basel). 2016 Sep 2;16(9):1423. doi: 10.3390/s16091423.
2
Walking pattern classification and walking distance estimation algorithms using gait phase information.基于步态相位信息的行走模式分类和行走距离估计算法。
IEEE Trans Biomed Eng. 2012 Oct;59(10):2884-92. doi: 10.1109/TBME.2012.2212245. Epub 2012 Aug 8.
3
A zero velocity detection algorithm using inertial sensors for pedestrian navigation systems.
Sensors (Basel). 2020 Jan 23;20(3):631. doi: 10.3390/s20030631.
4
Weight-Bearing Estimation for Cane Users by Using Onboard Sensors.利用车载传感器估算使用手杖者的承重情况。
Sensors (Basel). 2019 Jan 26;19(3):509. doi: 10.3390/s19030509.
5
A Multi-Sensor Matched Filter Approach to Robust Segmentation of Assisted Gait.多传感器匹配滤波器方法在辅助步态稳健分割中的应用
Sensors (Basel). 2018 Sep 6;18(9):2970. doi: 10.3390/s18092970.
基于惯性传感器的行人导航系统零速度检测算法。
Sensors (Basel). 2010;10(10):9163-78. doi: 10.3390/s101009163. Epub 2010 Oct 13.
4
Geriatric assistive devices.老年辅助器具。
Am Fam Physician. 2011 Aug 15;84(4):405-11.
5
Detection of falls using accelerometers and mobile phone technology.利用加速度计和移动电话技术检测跌倒。
Age Ageing. 2011 Nov;40(6):690-6. doi: 10.1093/ageing/afr050. Epub 2011 May 19.
6
Ambulatory assistive devices in orthopaedics: uses and modifications.矫形外科的助行器:用途和改造。
J Am Acad Orthop Surg. 2010 Jan;18(1):41-50. doi: 10.5435/00124635-201001000-00006.
7
Mobility devices to promote activity and participation: a systematic review.促进活动与参与的移动设备:一项系统综述
J Rehabil Med. 2009 Sep;41(9):697-706. doi: 10.2340/16501977-0427.
8
A fall and near-fall assessment and evaluation system.跌倒及近乎跌倒评估与评价系统。
Open Biomed Eng J. 2009 Jan 21;3:1-7. doi: 10.2174/1874120700903010001.
9
Assistive devices for balance and mobility: benefits, demands, and adverse consequences.平衡与行动辅助设备:益处、需求及不良后果
Arch Phys Med Rehabil. 2005 Jan;86(1):134-45. doi: 10.1016/j.apmr.2004.04.023.
10
Ambulatory devices for chronic gait disorders in the elderly.老年人慢性步态障碍的门诊治疗设备。
Am Fam Physician. 2003 Apr 15;67(8):1717-24.