基于角位移传感器的可穿戴设备高效上肢位置估计

Efficient Upper Limb Position Estimation Based on Angular Displacement Sensors for Wearable Devices.

作者信息

Contreras-González Aldo-Francisco, Ferre Manuel, Sánchez-Urán Miguel Ángel, Sáez-Sáez Francisco Javier, Blaya Haro Fernando

机构信息

Centro de Automática y Robótica (CAR) UPM-CSIC, ETS Ingenieros Industriales, Universidad Politécnica de Madrid, Calle de José Gutiérrez Abascal, 2, 28006 Madrid, Spain.

ETS Ingeniería y Diseño Industrial, Universidad Politécnica de Madrid, Ronda de Valencia, 3, 28012 Madrid, Spain.

出版信息

Sensors (Basel). 2020 Nov 12;20(22):6452. doi: 10.3390/s20226452.

DOI:10.3390/s20226452

PMID:33198097

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7696256/

Abstract

Motion tracking techniques have been extensively studied in recent years. However, capturing movements of the upper limbs is a challenging task. This document presents the estimation of arm orientation and elbow and wrist position using wearable flexible sensors (WFSs). A study was developed to obtain the highest range of motion (ROM) of the shoulder with as few sensors as possible, and a method for estimating arm length and a calibration procedure was proposed. Performance was verified by comparing measurement of the shoulder joint angles obtained from commercial two-axis soft angular displacement sensors (sADS) from Bend Labs and from the ground truth system (GTS) OptiTrack. The global root-mean-square error (RMSE) for the shoulder angle is 2.93 degrees and 37.5 mm for the position estimation of the wrist in cyclical movements; this measure of RMSE was improved to 13.6 mm by implementing a gesture classifier.

摘要

近年来，运动跟踪技术得到了广泛研究。然而，捕捉上肢的运动是一项具有挑战性的任务。本文介绍了使用可穿戴柔性传感器（WFS）估计手臂方向以及肘部和腕部位置的方法。开展了一项研究，旨在用尽可能少的传感器获得最大的肩部运动范围（ROM），并提出了一种估计手臂长度的方法和校准程序。通过比较从Bend Labs的商用双轴软角位移传感器（sADS）和地面真值系统（GTS）OptiTrack获得的肩关节角度测量值，对性能进行了验证。在周期性运动中，肩部角度的全局均方根误差（RMSE）为2.93度，腕部位置估计的RMSE为37.5毫米；通过实施手势分类器，该RMSE测量值提高到了13.6毫米。