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基于 RSSI 和惯性传感器测量的人体部位跟踪和运动学特征评估。

Human body parts tracking and kinematic features assessment based on RSSI and inertial sensor measurements.

机构信息

School of Computer Science & Engineering, Hebrew University of Jerusalem, Jerusalem 91904, Israel.

出版信息

Sensors (Basel). 2013 Aug 23;13(9):11289-313. doi: 10.3390/s130911289.

DOI:10.3390/s130911289
PMID:23979481
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3821292/
Abstract

Acquisition of patient kinematics in different environments plays an important role in the detection of risk situations such as fall detection in elderly patients, in rehabilitation of patients with injuries, and in the design of treatment plans for patients with neurological diseases. Received Signal Strength Indicator (RSSI) measurements in a Body Area Network (BAN), capture the signal power on a radio link. The main aim of this paper is to demonstrate the potential of utilizing RSSI measurements in assessment of human kinematic features, and to give methods to determine these features. RSSI measurements can be used for tracking different body parts' displacements on scales of a few centimeters, for classifying motion and gait patterns instead of inertial sensors, and to serve as an additional reference to other sensors, in particular inertial sensors. Criteria and analytical methods for body part tracking, kinematic motion feature extraction, and a Kalman filter model for aggregation of RSSI and inertial sensor were derived. The methods were verified by a set of experiments performed in an indoor environment. In the future, the use of RSSI measurements can help in continuous assessment of various kinematic features of patients during their daily life activities and enhance medical diagnosis accuracy with lower costs.

摘要

在不同环境中获取患者运动学数据在检测风险情况（如老年患者跌倒检测、受伤患者康复和设计神经疾病患者治疗计划）中起着重要作用。接收信号强度指示 (RSSI) 在身体区域网络 (BAN) 中的测量，捕获无线电链路中的信号功率。本文的主要目的是展示利用 RSSI 测量评估人体运动学特征的潜力，并提供确定这些特征的方法。RSSI 测量可用于跟踪几厘米范围内的不同身体部位的位移，用于分类运动和步态模式，而不是惯性传感器，并作为其他传感器（特别是惯性传感器）的附加参考。推导了用于身体部位跟踪、运动学运动特征提取的标准和分析方法，以及用于 RSSI 和惯性传感器聚合的卡尔曼滤波模型。这些方法通过在室内环境中进行的一组实验得到了验证。将来，RSSI 测量的使用可以帮助在患者的日常生活活动中连续评估各种运动学特征，并以更低的成本提高医疗诊断的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/3307d34f891c/sensors-13-11289f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/06da9d141a83/sensors-13-11289f1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/33eb41455580/sensors-13-11289f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/2b8e1ea211b2/sensors-13-11289f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/76b08cfda310/sensors-13-11289f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/a9907434c9a8/sensors-13-11289f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/01b99ed32491/sensors-13-11289f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/ea1b330a331d/sensors-13-11289f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/3307d34f891c/sensors-13-11289f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/06da9d141a83/sensors-13-11289f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/dfe369996092/sensors-13-11289f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/e9583c362ef8/sensors-13-11289f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/33eb41455580/sensors-13-11289f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/2b8e1ea211b2/sensors-13-11289f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/76b08cfda310/sensors-13-11289f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/a9907434c9a8/sensors-13-11289f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/01b99ed32491/sensors-13-11289f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/ea1b330a331d/sensors-13-11289f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2af3/3821292/3307d34f891c/sensors-13-11289f10.jpg

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