Analysis of Human Motion Data Using Recurrence Plots and Recurrence Quantification Measures

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.

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Differentiation of Human Motion Data Using Combined Spline and Least Squares Concepts

Journal of Biomechanical Engineering ◽

10.1115/1.2894894 ◽

1991 ◽

Vol 113 (3) ◽

pp. 348-351 ◽

Cited By ~ 7

Author(s):

W. Simons ◽

K. H. Yang

Keyword(s):

Least Squares ◽

Human Motion ◽

Smoothing Technique ◽

Data Smoothing ◽

Motion Data ◽

Differentiation Method ◽

Benchmark Data

A differentiation method, which combines the concepts of least squares and splines, has been developed to analyze human motion data. This data smoothing technique is not dependent on a choice of a cut-off frequency and yet it closely reflects the nature of the phenomenon. Two sets of published benchmark data were used to evaluate the new algorithm.

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A Web Application for Subsequence Matching in 3D Human Motion Data

2017 IEEE International Symposium on Multimedia (ISM) ◽

10.1109/ism.2017.73 ◽

2017 ◽

Author(s):

Jan Sedmidubsky ◽

Pavel Zezula

Keyword(s):

Web Application ◽

Human Motion ◽

Subsequence Matching ◽

Motion Data

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Human Motion Analysis and Simulation Tools

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Handbook of Research on Computational Simulation and Modeling in Engineering ◽

10.4018/978-1-4666-8823-0.ch012 ◽

2016 ◽

pp. 359-388 ◽

Cited By ~ 2

Author(s):

João F. Nunes ◽

Pedro M. Moreira ◽

João Manuel R. S. Tavares

Keyword(s):

User Interfaces ◽

Wide Spectrum ◽

Human Motion ◽

Rehabilitation Robotics ◽

Motion Simulation ◽

Simulation Tools ◽

Motion Data ◽

Engineering Software ◽

Software Applications ◽

Computational Systems

Computational systems to identify objects represented in image sequences and tracking their motion in a fully automatic manner, enabling a detailed analysis of the involved motion and its simulation are extremely relevant in several fields of our society. In particular, the analysis and simulation of the human motion has a wide spectrum of relevant applications with a manifest social and economic impact. In fact, usage of human motion data is fundamental in a broad number of domains (e.g.: sports, rehabilitation, robotics, surveillance, gesture-based user interfaces, etc.). Consequently, many relevant engineering software applications have been developed with the purpose of analyzing and/or simulating the human motion. This chapter presents a detailed, broad and up to date survey on motion simulation and/or analysis software packages that have been developed either by the scientific community or commercial entities. Moreover, a main contribution of this chapter is an effective framework to classify and compare motion simulation and analysis tools.

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MotionLab Sonify: A Framework for the Sonification of Human Motion Data

Ninth International Conference on Information Visualisation (IV'05) ◽

10.1109/iv.2005.84 ◽

2006 ◽

Cited By ~ 5

Author(s):

A. Effenberg ◽

J. Melzer ◽

A. Weber ◽

A. Zinke

Keyword(s):

Human Motion ◽

Motion Data

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A flexible motion tracking system based on inertial sensors

MATEC Web of Conferences ◽

10.1051/matecconf/201819804010 ◽

2018 ◽

Vol 198 ◽

pp. 04010

Author(s):

Zhonghao Han ◽

Lei Hu ◽

Na Guo ◽

Biao Yang ◽

Hongsheng Liu ◽

...

Keyword(s):

Motion Tracking ◽

Data Transfer ◽

Inertial Sensors ◽

Tracking System ◽

Calibration Procedure ◽

Human Motion ◽

The Body ◽

Support Vector ◽

Motion Data ◽

Motion Tracking System

As a newly emerging human-computer interaction, motion tracking technology offers a way to extract human motion data. This paper presents a series of techniques to improve the flexibility of the motion tracking system based on the inertial measurement units (IMUs). First, we built a most miniatured wireless tracking node by integrating an IMU, a Wi-Fi module and a power supply. Then, the data transfer rate was optimized using an asynchronous query method. Finally, to simplify the setup and make the interchangeability of all nodes possible, we designed a calibration procedure and trained a support vector machine (SVM) model to determine the binding relation between the body segments and the tracking nodes after setup. The evaluations of the whole system justify the effectiveness of proposed methods and demonstrate its advantages compared to other commercial motion tracking system.

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CSI-HC: A WiFi-Based Indoor Complex Human Motion Recognition Method

Mobile Information Systems ◽

10.1155/2020/3185416 ◽

2020 ◽

Vol 2020 ◽

pp. 1-20

Author(s):

Zhanjun Hao ◽

Yu Duan ◽

Xiaochao Dang ◽

Tong Zhang

Keyword(s):

Real Time ◽

Human Motion ◽

Detection Accuracy ◽

Behavior Recognition ◽

Motion Recognition ◽

Pass Filter ◽

Low Pass Filter ◽

Motion Data ◽

Indoor Scenes ◽

Human Motion Recognition

WiFi indoor personnel behavior recognition has become the core technology of wireless network perception. However, the existing human behavior recognition methods have great challenges in terms of detection accuracy, intrusion, and complexity of operations. In this paper, we firstly analyze and summarize the existing human motion recognition schemes, and due to the existence of the problems in them, we propose a noninvasive, highly robust complex human motion recognition scheme based on Channel State Information (CSI), that is, CSI-HC, and the traditional Chinese martial art XingYiQuan is verified as a complex motion background. CSI-HC is divided into two phases: offline and online. In the offline phase, the human motion data are collected on the commercial Atheros NIC and a powerful denoising method is constructed by using the Butterworth low-pass filter and wavelet function to filter the outliers in the motion data. Then, through Restricted Boltzmann Machine (RBM) training and classification, we establish offline fingerprint information. In the online phase, SoftMax regression is used to correct the RBM classification to process the motion data collected in real time and the processed real-time data are matched with the offline fingerprint information. On this basis, the recognition of a complex human motion is realized. Finally, through repeated experiments in three classical indoor scenes, the parameter setting and user diversity affecting the accuracy of motion recognition are analyzed and the robustness of CSI-HC is detected. In addition, the performance of the proposed method is compared with that of the existing motion recognition methods. The experimental results show that the average motion recognition rate of CSI-HC in three classic indoor scenes reaches 85.4%, in terms of motion complexity and indoor recognition accuracy. Compared with other algorithms, it has higher stability and robustness.

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