A Validation Study of Rehabilitation Exercise Monitoring Using Kinect

2019 ◽  
pp. 466-482
Author(s):  
Wenbing Zhao ◽  
Deborah D. Espy ◽  
Ann Reinthal
Keyword(s):  
Coordinate System ◽  
Motion Capture ◽  
Validation Study ◽  
Microsoft Kinect ◽  
System Level ◽  
System Transformation ◽  
Motion Data ◽  
Reference Motion ◽  
System 2 ◽  
Motion Capture Systems

In this chapter, the authors present their work on a validation study of using Microsoft Kinect to monitor rehabilitation exercises. Differing from other validation efforts, the authors focus on a system-level assessment instead of the joint-level comparison with reference motion capture systems. They assess the feasibility of using Kinect by examining the enforceability of a set of correctness rules defined for each exercise, which are invariances of each exercise and hence independent from the coordinate system used. This method is more advantageous in that (1) it does not require coordinate system transformation between those of the reference motion capture system and of the Kinect-based system, (2) it does not require an exact match of the Kinect joints and the corresponding external marker placements or derived joint centers often used in reference motion capture systems, and (3) the correctness rules and their mapping for Kinect motion data analysis developed in this study are readily implementable for a real motion monitoring system for physical therapy.

A Validation Study of Rehabilitation Exercise Monitoring Using Kinect

2018 ◽  
pp. 5941-5954 ◽  
Author(s):  
Wenbing Zhao ◽  
Deborah D. Espy ◽  
Ann Reinthal
Keyword(s):  
Coordinate System ◽  
Motion Capture ◽  
Validation Study ◽  
Microsoft Kinect ◽  
System Level ◽  
System Transformation ◽  
Motion Data ◽  
Reference Motion ◽  
System 2 ◽  
Motion Capture Systems

In this article, we present our work on a validation study of using Microsoft Kinect to monitor rehabilitation exercises. Differing from other validation efforts, we focus on a system-level assessment instead of the joint-level comparison with reference motion capture systems. We assess the feasibility of using Kinect by examining the enforceability of a set of correctness rules defined for each exercise, which are invariances of each exercise and hence independent from the coordinate system used. This method is more advantageous in that (1) it does not require coordinate system transformation between those of the reference motion capture system and of the Kinect based system, (2) it does not require an exact match of the Kinect joints and the corresponding external marker placements or derived joint centers often used in reference motion capture systems, and (3) the correctness rules and their mapping for Kinect motion data analysis developed in this study are readily implementable for a real motion monitoring system for physical therapy.

Low Cost Markerless Motion Capture Systems: A Comparison Between RGB Cameras and RGB-D Sensors

2020 ◽  
Author(s):  
Andrea Vitali ◽  
Daniele Regazzoni ◽  
Caterina Rizzi ◽  
Giorgio Lupi
Keyword(s):  
Motion Capture ◽  
Low Cost ◽  
Research Work ◽  
Microsoft Kinect ◽  
Lower Limbs ◽  
Specific Context ◽  
Depth Sensors ◽  
Research Fields ◽  
Markerless Motion Capture ◽  
Motion Capture Systems

Abstract In the last years, the advent of low-cost markerless motion capture systems fostered their use in several research fields, such as healthcare and sport. Any system presents benefits and drawbacks that have to be considered to design a Mocap solution providing a proper motion acquisition for a specific context. In order to evaluate low-cost technology, this research work focuses on the evaluation of the accuracy of two categories of devices: the RGB active cameras and the RGB-D, or depth sensors devices. In particular, GoPro Hero 6 active cameras and Microsoft Kinect v2 devices have been selected as representative of the two categories. In particular, this work evaluates and compares the performances of the two systems used to track the position of human articulations. The two devices have been chosen among those available on the market after a state of the art has been completed. Before starting with the campaign of acquisition, the number of sensors and their layout have been designed to optimize the acquisition with both mark-less Mocap systems. Their comparison is based on a list of specific movements of upper and lower limbs. Each movement has been acquired simultaneously, to guarantee the same test conditions. The results have been organized, compared and discussed by evaluating performances and limitations of both solutions related to specific context of use. Conclusions highlight the best candidate technology.

Generation of Character Motion by Using Reactive Motion Capture System with Force Feedback

2008 ◽  
Vol 12 (2) ◽  
pp. 116-124
Author(s):  
Woong Choi ◽  
◽  
Naoki Hashimoto ◽  
Ross Walker ◽  
Kozaburo Hachimura ◽  
...  
Keyword(s):  
Virtual Environment ◽  
Motion Capture ◽  
Force Feedback ◽  
Task Environment ◽  
Motion Capture System ◽  
Motion Generation ◽  
Motion Data ◽  
Human Scale ◽  
Real Objects ◽  
Motion Capture Systems

Creating reactive motions with conventional motion capture systems is difficult because of the different task environment required. To overcome this drawback, we developed a reactive motion capture system that combines conventional motion capture system with force feedback and a human-scale virtual environment. Our objective is to make animation with reactive motion data generated from the interaction with force feedback and the virtual environment, using the fact that a person’s motions in the real world can be represented by the reactions of the person to real objects. In this paper we present the results of some animations made under various scenarios using animating reactive motion generation with our reactive motion capture system. Our results demonstrate that the reactive motion generated by this system was useful for producing the animation including scenes of reactive motion.

Motion Capture for Human-Centered Simulation Using Kinects

2013 ◽  
Author(s):  
Chinmay P. Daphalapurkar ◽  
Wenjuan Zhu ◽  
Ming C. Leu ◽  
Xiaoqing F. Liu ◽  
Alpha M. Chang ◽  
...  
Keyword(s):  
Motion Capture ◽  
Low Cost ◽  
Microsoft Kinect ◽  
Frame Rate ◽  
Infrared Light ◽  
Aircraft Fuselage ◽  
Light Pattern ◽  
Human Movements ◽  
Shutter Mechanism ◽  
Motion Capture Systems

Microsoft Kinect is capable of tracking human movements and can be used to develop various human-centered simulations. It is very attractive for certain applications because it is a low-cost, marker-less device. This paper presents our research toward characterizing the accuracy of Kinect and developing Kinect-based motion capture systems. Besides a single-Kinect system, a motion capture system with multiple Kinects were developed in order to increase the tracking volume and to improve the simulation fidelity. A motor-driven shutter mechanism was developed for use with each Kinect for the multi-Kinect system to address the issue of interference on the infrared light pattern without lowering the frame rate. The capabilities of the developed Kinect-based motion capture systems are demonstrated by tracking a human in performing a fastening operation on an aircraft fuselage mockup.

scholarly journals An HMM-based averaging approach for creating mean motion data from a full-body Motion Capture system to support the development of a biomechanical model

2018 ◽  
Vol 4 (1) ◽  
pp. 389-393
Author(s):  
Andreas Kitzig ◽  
Julia Demmer ◽  
Tobias Bolten ◽  
Edwin Naroska ◽  
Gudrun Stockmanns ◽  
...  
Keyword(s):  
Motion Capture ◽  
Hidden Markov ◽  
Biomechanical Model ◽  
Human Movement ◽  
Body Motion ◽  
Game Development ◽  
Motion Data ◽  
Motion Sequence ◽  
Motion Capture Systems ◽  
Full Body

AbstractMotion capture systems or MoCap systems are used for game development and in the field of sports for the assessment and digitalization of human movement. Furthermore, MoCap systems are also used in the medical and therapeutic field for the analysis of human movement patterns. As examples gait analysis or examination of the musculoskeletal system and its function should be mentioned. Most application relate to a specific person and their movement or to the comparison of movements of different people. Within the scope of this paper an averaged motion sequence is supposed to be generated from MoCap data in order to be able to use it in the field of biomechanical modeling and simulation. For the averaging of individual movement sequences of different persons a Hidden Markov Model (HMM) based approach is presented.

Biomechanical Analysis of Typical Upper Limb Movements Based on Kinect-LifeMOD

2014 ◽  
Vol 599-601 ◽  
pp. 534-538 ◽  
Author(s):  
Ming Zeng ◽  
Chang Wei Chen ◽  
Qing Hao Meng ◽  
Hong Lin Ren ◽  
Shu Gen Ma
Keyword(s):  
Motion Capture ◽  
Upper Limb ◽  
Microsoft Kinect ◽  
Biomechanical Analysis ◽  
Combination Method ◽  
Depth Sensor ◽  
Motion Data ◽  
Position Data ◽  
Upper Limb Movements ◽  
Precision Motion

In traditional biomechanical analysis of upper limb, the high-precision motion data and lifelike human models are needed. It is obvious that those processes are costly and time-consuming. In this paper, a novel and simple combination method based on Kinect-LifeMOD is proposed. Firstly, the Microsoft Kinect (a latest depth sensor) is used to build a cheap and precise motion capture platform. Real-time and reliable key-node rotation data of human skeletons can be acquired by this motion capture system. Next, rotation data is converted into position data as the input of the LifeMOD software which can establish mathematical model of upper limb and execute biomechanical analysis automatically. The experimental results show that the proposed method could achieve the satisfactory performance.

Extrinsic Calibration of Camera and Motion Capture Systems

2021 ◽  
Author(s):  
Prashant Ganesh ◽  
Kyle Volle ◽  
Paul Buzaud ◽  
Kevin Brink ◽  
Andrew Willis
Keyword(s):  
Motion Capture ◽  
Extrinsic Calibration ◽  
Motion Capture Systems

scholarly journals Dance motion capture and composition using multiple RGB and depth sensors

2017 ◽  
Vol 13 (2) ◽  
pp. 155014771769608 ◽  
Author(s):  
Yejin Kim
Keyword(s):  
Motion Capture ◽  
High Speed ◽  
Motion Path ◽  
High Complexity ◽  
Motion Data ◽  
Depth Sensors ◽  
Motion Editing ◽  
Human Movements ◽  
Marker Free ◽  
Motion Search

Dynamic human movements such as dance are difficult to capture without using external markers due to the high complexity of a dancer’s body. This article introduces a marker-free motion capture and composition system for dance motion that uses multiple RGB and depth sensors. Our motion capture system utilizes a set of high-speed RGB and depth sensors to generate skeletal motion data from an expert dancer. During the motion acquisition process, a skeleton tracking method based on a particle filter is provided to estimate the motion parameters for each frame from a sequence of color images and depth features retrieved from the sensors. The expert motion data become archived in a database. The authoring methods in our composition system automate most of the motion editing processes for general users by providing an online motion search with an input posture and then performing motion synthesis on an arbitrary motion path. Using the proposed system, we demonstrate that various dance performances can be composed in an intuitive and efficient way on client devices such as tablets and kiosk PCs.

Research on Solving Method for Tooth Crest Curve of Spiral Bevel Gear under Form Milling

2010 ◽  
Vol 139-141 ◽  
pp. 1255-1259
Author(s):  
Xiu Ting Wei ◽  
Jing Cheng Liu ◽  
Qiang Du
Keyword(s):  
Coordinate System ◽  
Process Simulation ◽  
Machining Process ◽  
Geometric Transformation ◽  
Spiral Bevel Gear ◽  
Bevel Gears ◽  
Machining Process Simulation ◽  
System 2 ◽  
Spiral Bevel ◽  
Curve Equation

Combining the machining process simulation on UG NX software and the mathematically analysis, a new solving method for the tooth crest curve equation of spiral bevel gears is proposed in this paper. The steps are as follows:1) Establishing the gear blank cone equation in the original coordinate system; 2) Finding the gear convex and concave profile equations in the new coordinate system; 3) Integrating the original coordinate system and the new one through geometric transformation and then deducing the tooth crest curve equations on both sides. The equations will help calculate the cutter’s position and pose in addendum chamfering process, with the chamfer automation achieved.

Validation of wearable inertial sensor-based gait analysis system for measurement of spatiotemporal parameters and lower extremity joint kinematics in sagittal plane

2022 ◽  
pp. 095441192110729
Author(s):  
Gunjan Patel ◽  
Rajani Mullerpatan ◽  
Bela Agarwal ◽  
Triveni Shetty ◽  
Rajdeep Ojha ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Motion Analysis ◽  
Motion Capture ◽  
Inertial Sensor ◽  
Joint Kinematics ◽  
Percentage Error ◽  
Motion Capture System ◽  
Spatiotemporal Parameters ◽  
Analysis System ◽  
Motion Capture Systems

Wearable inertial sensor-based motion analysis systems are promising alternatives to standard camera-based motion capture systems for the measurement of gait parameters and joint kinematics. These wearable sensors, unlike camera-based gold standard systems, find usefulness in outdoor natural environment along with confined indoor laboratory-based environment due to miniature size and wireless data transmission. This study reports validation of our developed (i-Sens) wearable motion analysis system against standard motion capture system. Gait analysis was performed at self-selected speed on non-disabled volunteers in indoor ( n = 15) and outdoor ( n = 8) environments. Two i-Sens units were placed at the level of knee and hip along with passive markers (for indoor study only) for simultaneous 3D motion capture using a motion capture system. Mean absolute percentage error (MAPE) was computed for spatiotemporal parameters from the i-Sens system versus the motion capture system as a true reference. Mean and standard deviation of kinematic data for a gait cycle were plotted for both systems against normative data. Joint kinematics data were analyzed to compute the root mean squared error (RMSE) and Pearson’s correlation coefficient. Kinematic plots indicate a high degree of accuracy of the i-Sens system with the reference system. Excellent positive correlation was observed between the two systems in terms of hip and knee joint angles (Indoor: hip 3.98° ± 1.03°, knee 6.48° ± 1.91°, Outdoor: hip 3.94° ± 0.78°, knee 5.82° ± 0.99°) with low RMSE. Reliability characteristics (defined using standard statistical thresholds of MAPE) of stride length, cadence, walking speed in both outdoor and indoor environment were well within the “Good” category. The i-Sens system has emerged as a potentially cost-effective, valid, accurate, and reliable alternative to expensive, standard motion capture systems for gait analysis. Further clinical trials using the i-Sens system are warranted on participants across different age groups.

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