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 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

Gait Analysis in the Assessment of Patients Undergoing a Total Hip Replacement

2019 ◽  
Author(s):  
Daniele Regazzoni ◽  
Andrea Vitali ◽  
Filippo Colombo Zefinetti ◽  
Caterina Rizzi
Keyword(s):  
Motion Capture ◽  
Hip Replacement ◽  
Motion Tracking ◽  
Low Cost ◽  
Quantitative Measure ◽  
Clinical Information ◽  
Human Motion ◽  
Technical Solution ◽  
Specific Context ◽  
Main Challenge

Abstract Nowadays, healthcare centers are not familiar with quantitative approaches for patients’ gait evaluation. There is a clear need for methods to obtain objective figures characterizing patients’ performance. Actually, there are no diffused methods for comparing the pre- and post-operative conditions of the same patient, integrating clinical information and representing a measure of the efficiency of functional recovery, especially in the short-term distance of the surgical intervention. To this aim, human motion tracking for medical analysis is creating new frontiers for potential clinical and home applications. Motion Capture (Mocap) systems are used to allow detecting and tracking human body movements, such as gait or any other gesture or posture in a specific context. In particular, low-cost portable systems can be adopted for the tracking of patients’ movements. The pipeline going from tracking the scene to the creation of performance scores and indicators has its main challenge in the data elaboration, which depends on the specific context and to the detailed performance to be evaluated. The main objective of this research is to investigate whether the evaluation of the patient’s gait through markerless optical motion capture technology can be added to clinical evaluations scores and if it is able to provide a quantitative measure of recovery in the short postoperative period. A system has been conceived, including commercial sensors and a way to elaborate data captured according to caregivers’ requirements. This allows transforming the real gait of a patient right before and/or after the surgical procedure into a set of scores of medical relevance for his/her evaluation. The technical solution developed in this research will be the base for a large acquisition and data elaboration campaign performed in collaboration with an orthopedic team of surgeons specialized in hip arthroplasty. This will also allow assessing and comparing the short run results obtained by adopting different state-of-the-art surgical approach for the hip replacement.

Hierarchical Model-Based Human Motion Tracking Via Unscented Kalman Filter

2007 ◽  
Author(s):  
GuoJun Liu ◽  
XiangLong Tang ◽  
JianHua Huang ◽  
JiaFeng Liu ◽  
Da Sun
Keyword(s):  
Kalman Filter ◽  
Hierarchical Model ◽  
Motion Tracking ◽  
Unscented Kalman Filter ◽  
Human Motion ◽  
Human Motion Tracking ◽  
Model Based

Integration of Vision and Inertial Sensors for 3D Arm Motion Tracking in Home-based Rehabilitation

2007 ◽  
Vol 26 (6) ◽  
pp. 607-624 ◽  
Author(s):  
Yaqin Tao ◽  
Huosheng Hu ◽  
Huiyu Zhou
Keyword(s):  
Motion Tracking ◽  
Inertial Sensors ◽  
Home Based ◽  
Arm Motion

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 DOES: A Deep Learning-Based Approach to Estimate Roll and Pitch at Sea

2021 ◽  
Author(s):  
Fabiana Di Ciaccio ◽  
Paolo Russo ◽  
Salvatore Troisi
Keyword(s):  
Deep Learning ◽  
Motion Tracking ◽  
Inertial Sensors ◽  
Low Cost ◽  
Human Motion ◽  
High Rate ◽  
The Novel ◽  
Wide Range ◽  
Maritime Navigation ◽  
Low Costs

The use of Attitude and Heading Reference Systems (AHRS) for orientation estimation is now common practice in a wide range of applications, e.g., robotics and human motion tracking, aerial vehicles and aerospace, gaming and virtual reality, indoor pedestrian navigation and maritime navigation. The integration of the high-rate measurements can provide very accurate estimates, but these can suffer from errors accumulation due to the sensors drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and techniques. As an example, camera-based solutions have drawn a large attention by the community, thanks to their low-costs and easy hardware setup; moreover, impressive results have been demonstrated in the context of Deep Learning. This work presents the preliminary results obtained by DOES , a supportive Deep Learning method specifically designed for maritime navigation, which aims at improving the roll and pitch estimations obtained by common AHRS. DOES recovers these estimations through the analysis of the frames acquired by a low-cost camera pointing the horizon at sea. The training has been performed on the novel ROPIS dataset, presented in the context of this work, acquired using the FrameWO application developed for the scope. Promising results encourage to test other network backbones and to further expand the dataset, improving the accuracy of the results and the range of applications of the method.

scholarly journals Human Motion Tracking Based on Unscented Kalman Filter in Sports Domain

Kalman Filter ◽  
10.5772/9579 ◽  
2010 ◽  
Author(s):  
GuoJun Liu ◽  
XiangLong Tang
Keyword(s):  
Kalman Filter ◽  
Motion Tracking ◽  
Unscented Kalman Filter ◽  
Human Motion ◽  
Human Motion Tracking

Comparison of Marker-Based and Marker-Less Systems for Low-Cost Human Motion Capture

2013 ◽  
Author(s):  
Sajeev C. Puthenveetil ◽  
Chinmay P. Daphalapurkar ◽  
Wenjuan Zhu ◽  
Ming C. Leu ◽  
Xiaoqing F. Liu ◽  
...  
Keyword(s):  
Motion Capture ◽  
Motion Tracking ◽  
Low Cost ◽  
Human Motion ◽  
Motion Data ◽  
Aircraft Fuselage ◽  
Modeling Software ◽  
Human Motion Simulation ◽  
Optical Motion ◽  
Quality Of Motion

To generate graphic simulation of human motion, marker-based optical motion capture technology is widely used because of the accuracy and reliability of motion data provided by this technology. However, tracking of human motion without markers is very desirable on the factory floor because the human operator does not need to wear a special suit mounted with markers and there is no physical interference with the manufacturing or assembly operation during the motion tracking. In this paper, we compare marker-based and marker-less motion capture systems. First, the operational principles of these two different types of systems are compared. Then the quality of motion data obtained by a marker-less system using Kinect is compared with that obtained by a marker-based system using Optitrack cameras. The comparison also includes the accuracy of body joint angles and variations in body segment lengths measured by the two different systems. Furthermore, we compare the human motion simulation developed in the Jack digital human modeling software using the data captured by these two systems when a person is performing a fastening operation on a physical mockup of the belly section of an aircraft fuselage.

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 Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5357 ◽  
Author(s):  
Haseeb Ahmed ◽  
Ihsan Ullah ◽  
Uzair Khan ◽  
Muhammad Bilal Qureshi ◽  
Sajjad Manzoor ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Inertial Sensors ◽  
Low Cost ◽  
Optimal Estimation ◽  
Cost Effective ◽  
Estimation Accuracy ◽  
Ground Vehicle ◽  
Navigation Data ◽  
Mems Imu

Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter.

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