Saving and Reproduction of Human Motion Data by Using Haptic Devices with Different Configurations

2011 ◽  
Vol 131 (3) ◽  
pp. 267-274 ◽  
Author(s):  
Noboru Tsunashima ◽  
Yuki Yokokura ◽  
Seiichiro Katsura
Keyword(s):  
Human Motion ◽  
Haptic Devices ◽  
Motion Data

scholarly journals Acceleration Feature Extraction of Human Body Based on Wearable Devices

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 924
Author(s):  
Zhenzhen Huang ◽  
Qiang Niu ◽  
Ilsun You ◽  
Giovanni Pau
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Kalman Filter ◽  
Human Body ◽  
Wearable Devices ◽  
Human Motion ◽  
Data Set ◽  
Feature Extraction Method ◽  
Motion Data ◽  
Kalman Filter Algorithm

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.

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

1991 ◽  
Vol 113 (3) ◽  
pp. 348-351 ◽  
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.

scholarly journals Human Motion Analysis and Simulation Tools

2016 ◽  
pp. 359-388 ◽  
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.

scholarly journals A flexible motion tracking system based on inertial sensors

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.

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

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.

Adaptive Animation of Human Motion for E-Learning Applications

2011 ◽  
pp. 134-144
Author(s):  
Frederick W.B. Li ◽  
Rynson W.H. Lau ◽  
Taku Komura ◽  
Meng Wang ◽  
Becky Siu
Keyword(s):  
Human Motion ◽  
Learning System ◽  
The Internet ◽  
Motion Information ◽  
User Interest ◽  
Motion Data ◽  
Network Bandwidth ◽  
Levels Of Detail ◽  
E Learning ◽  
Human Motions

Human motion animation has been one of the major research topics in the field of computer graphics for decades. Techniques developed in this area help present human motions in various applications. This is crucial for enhancing the realism as well as promoting the user interest in the applications. To carry this merit to e-learning applications, we have developed efficient techniques for delivering human motion information over the Internet to collaborating e-learning users and revealing the motion information in the client machines with different rendering capability. Our method offers a mechanism to extract human motion data at various levels of detail (LoD). We also propose a set of importance factors to allow an e-learning system to determine the LoD of the human motion for rendering as well as transmission, according to the importance of the motion and the available network bandwidth. At the end of the paper, we demonstrate the effectiveness of the new method with some experimental results.

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