Development of a Contact Based Human Arm Motion Analysis System for Virtual Reality Applications

2014 ◽  
Vol 592-594 ◽  
pp. 2139-2144
Author(s):  
Varun Nalam ◽  
P.V. Manivannan
Keyword(s):  
Virtual Reality ◽  
Motion Tracking ◽  
Average Error ◽  
Central Computer ◽  
Human Arm ◽  
Reference Device ◽  
Measurement Units ◽  
Arm Motion ◽  
Analysis System ◽  
Tracking Device

This paper presents the development of a system for analyzing and adapting the Human Arm motion for virtual reality applications. The proposed system consists of number of Flex sensors, Inertial Measurement Units and Embedded Data Acquisition System, to record the joint angles for deriving the kinematic states (position, velocity and acceleration) of different parts of the human arm. A flexible structure is used for holding the sensors on the human arm at the required position, without hindering the movement. The embedded circuit utilizes a 32-bit Microcontroller to process the data from various sensors and transmits digitized data to the central computer for computing the various kinematics parameters. The system has been tested against standard motion tracking device and is found to perform close to the reference device with an average error of 6% . Such a device can be used to simulate critical operations in medicine and industry and analyze performance during various tasks.

Human Arm Motion Tracking by Inertial/Magnetic Sensors Using Unscented Kalman Filter and Relative Motion Constraint

2017 ◽  
Vol 90 (1-2) ◽  
pp. 161-170 ◽  
Author(s):  
Arash Atrsaei ◽  
Hassan Salarieh ◽  
Aria Alasty ◽  
Mohammad Abediny
Keyword(s):  
Kalman Filter ◽  
Relative Motion ◽  
Motion Tracking ◽  
Unscented Kalman Filter ◽  
Magnetic Sensors ◽  
Human Arm ◽  
Arm Motion ◽  
Motion Constraint

Human Arm Motion Tracking by Orientation-Based Fusion of Inertial Sensors and Kinect Using Unscented Kalman Filter

2016 ◽  
Vol 138 (9) ◽  
Author(s):  
Arash Atrsaei ◽  
Hassan Salarieh ◽  
Aria Alasty
Keyword(s):  
Kalman Filter ◽  
Motion Capture ◽  
Motion Tracking ◽  
Inertial Sensors ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Human Motion ◽  
Home Based ◽  
Human Arm ◽  
Arm Motion

Due to various applications of human motion capture techniques, developing low-cost methods that would be applicable in nonlaboratory environments is under consideration. MEMS inertial sensors and Kinect are two low-cost devices that can be utilized in home-based motion capture systems, e.g., home-based rehabilitation. In this work, an unscented Kalman filter approach was developed based on the complementary properties of Kinect and the inertial sensors to fuse the orientation data of these two devices for human arm motion tracking during both stationary shoulder joint position and human body movement. A new measurement model of the fusion algorithm was obtained that can compensate for the inertial sensors drift problem in high dynamic motions and also joints occlusion in Kinect. The efficiency of the proposed algorithm was evaluated by an optical motion tracker system. The errors were reduced by almost 50% compared to cases when either inertial sensor or Kinect measurements were utilized.

Human Arm Motion Tracking by Kinect Sensor Using Kalman Filter for Collaborative Robotics

2020 ◽  
pp. 326-334 ◽  
Author(s):  
Pierpaolo Palmieri ◽  
Matteo Melchiorre ◽  
Leonardo Sabatino Scimmi ◽  
Stefano Pastorelli ◽  
Stefano Mauro
Keyword(s):  
Kalman Filter ◽  
Motion Tracking ◽  
Kinect Sensor ◽  
Collaborative Robotics ◽  
Human Arm ◽  
Arm Motion

scholarly journals Optoelectronic tracking system for shooting simulator - tests in a virtual reality application

2020 ◽  
Vol 12 (2) ◽  
pp. 61
Author(s):  
Marcin Maciejewski ◽  
Marek Piszczek ◽  
Mateusz Pomianek ◽  
Norbert Pałka
Keyword(s):  
Virtual Reality ◽  
Motion Tracking ◽  
Tracking System ◽  
Low Cost ◽  
Test Results ◽  
Virtual Reality System ◽  
Training Simulator ◽  
Present Test ◽  
Accuracy And Precision ◽  
Tracking Device

We present test results of an authorial tracking device developed in the SteamVR system, optimized for use in a missile launcher shooting simulator. Data for analysis was collected using the virtual reality training application, with the launcher set on a stable tripod and held by a trainee who executed two scenarios with static and movable targets. The analysis of experimental data confirms that the SteamVR system together with the developed tracker can be successfully implemented in the virtual shooting simulator. Full Text: PDF ReferencesD. Bogatinov, P. Lameski, V. Trajkovik, K.M. Trendova, "Firearms training simulator based on low cost motion tracking sensor", Multimed. Tools Appl. 76(1) (2017) CrossRef D.C. Niehorster, L. Li, M. Lappe, "The Accuracy and Precision of Position and Orientation Tracking in the HTC Vive Virtual Reality System for Scientific Research", Iperception. 8(3) (2017) CrossRef A. Yates, J. Selan, POSITIONAL TRACKING SYSTEMS AND METHODS. US20160131761A1, (2016) DirectLink P. Caserman, A. Garcia-Agundez, R. Konrad, S. Göbel, R. Steinmetz, Virtual Real. 23(2) (2019) 155-68. CrossRef

scholarly journals An Open-Source 7-DOF Wireless Human Arm Motion-Tracking System for Use in Robotics Research

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3082
Author(s):  
Almas Shintemirov ◽  
Tasbolat Taunyazov ◽  
Bukeikhan Omarali ◽  
Aigerim Nurbayeva ◽  
Anton Kim ◽  
...  
Keyword(s):  
Open Source ◽  
Design Education ◽  
Motion Tracking ◽  
Tracking System ◽  
Inertial Motion ◽  
Research Activities ◽  
Human Arm ◽  
Arm Motion ◽  
Motion Tracking System ◽  
Arm Tracking

To extend the choice of inertial motion-tracking systems freely available to researchers and educators, this paper presents an alternative open-source design of a wearable 7-DOF wireless human arm motion-tracking system. Unlike traditional inertial motion-capture systems, the presented system employs a hybrid combination of two inertial measurement units and one potentiometer for tracking a single arm. The sequence of three design phases described in the paper demonstrates how the general concept of a portable human arm motion-tracking system was transformed into an actual prototype, by employing a modular approach with independent wireless data transmission to a control PC for signal processing and visualization. Experimental results, together with an application case study on real-time robot-manipulator teleoperation, confirm the applicability of the developed arm motion-tracking system for facilitating robotics research. The presented arm-tracking system also has potential to be employed in mechatronic system design education and related research activities. The system CAD design models and program codes are publicly available online and can be used by robotics researchers and educators as a design platform to build their own arm-tracking solutions for research and educational purposes.

ROBOTIC HANDWRITING

2005 ◽  
Vol 02 (01) ◽  
pp. 105-124 ◽  
Author(s):  
VELJKO POTKONJAK
Keyword(s):  
Mathematical Models ◽  
Muscle Fatigue ◽  
Simulation Study ◽  
Robot Control ◽  
Robot Arm ◽  
Redundancy Resolution ◽  
Kinematic Redundancy ◽  
New Concepts ◽  
Human Arm ◽  
Arm Motion

Handwriting has always been considered an important human task, and accordingly it has attracted the attention of researchers working in biomechanics, physiology, and related fields. There exist a number of studies on this area. This paper considers the human–machine analogy and relates robots with handwriting. The work is two-fold: it improves the knowledge in biomechanics of handwriting, and introduces some new concepts in robot control. The idea is to find the biomechanical principles humans apply when resolving kinematic redundancy, express the principles by means of appropriate mathematical models, and then implement them in robots. This is a step forward in the generation of human-like motion of robots. Two approaches to redundancy resolution are described: (i) "Distributed Positioning" (DP) which is based on a model to represent arm motion in the absence of fatigue, and (ii) the "Robot Fatigue" approach, where robot movements similar to the movements of a human arm under muscle fatigue are generated. Both approaches are applied to a redundant anthropomorphic robot arm performing handwriting. The simulation study includes the issues of legibility and inclination of handwriting. The results demonstrate the suitability and effectiveness of both approaches.

Quantifying the Accuracy of a Motion Detecting Neural Prosthesis

2020 ◽  
Author(s):  
Martin L. Tanaka ◽  
Premkumar Subbukutti ◽  
David Hudson ◽  
Kimberly Hudson ◽  
Pablo Valenzuela ◽  
...  
Keyword(s):  
Motion Capture ◽  
Gait Cycle ◽  
Neural Prosthesis ◽  
Average Error ◽  
Camera Motion ◽  
Motion Capture System ◽  
General Shape ◽  
Neural Connections ◽  
Measurement Units ◽  
The Brain

Abstract The neural prosthesis under development is designed to improve gait in people with muscle weakness. The strategy is to augment impaired or damaged neural connections between the brain and the muscles that control walking. This third-generation neural prosthesis contains triaxial inertial measurement units (IMUs - accelerometers, gyroscopes, and processing chip) to measure body segment position and force sensitive resistors placed under the feet to detect ground contact. A study was conducted to compare the accuracy of the neural prosthesis using a traditional camera motion capture system as a reference. The IMUs were found to accurately represent the amplitude of the gait cycle components and generally track the motion. However, there are some differences in phase, with the IMUs lagging the actual motion. Phase lagged by about 10 degrees in the ankle and by about 5 degrees in the knee. Error of the neural prosthesis varied over the gait cycle. The average error for the ankle, knee and hip were 6°, 8°, and 9°, respectively. Testing showed that the neural prosthesis was able to capture the general shape of the joint angle curves when compared to a commercial camera motion capture system. In the future, measures will be taken to reduce lag in the gyroscope and reduce jitter in the accelerometer so that data from both sensors can be combination to obtain more accurate readings.

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