Human Pose Detection for Robotic-Assisted and Rehabilitation Environments (original) (raw)
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Archives of Physiotherapy and Global Researches, 2016
From a cibernetic approach, the body system can be defined like a net of structural and functional related subsystems with motor equifinality inside the concept of balance, energetic economy and comfort: therefore the ideal posture is the one that allows the maximum effectiveness of motor gesture, in absence of pain with the maximum energetic economy. The present study research is based on the necessity to individuate a real objective evaluation system of the postural parameters, inexpensive and of simple use compared to the evaluation instruments in use already scientifically validated. The instrument used in this study is the Microsoft Kinect®, gaming platform combined with the Xbox console. Created by Microsoft in the field of play, the Microsoft Kinect® for years has entertained millions of consumers through the Motion Capture System, the recording of movement through cameras and instant or deferred replay. The primary aim of this randomized controlled single-blind research is the demonstration that, despite being commonly defined objective the evaluation systems that utilize markers, is essential to look for an alternative evaluation method to minimize the systematic human error. The results demonstrate the real validity of Kinect®, and have verified the reliability of the data obtained from the assessment, showing the scientific reliability of this innovative objective evaluation method in rehabilitation-clinical field.
Upper Limb Joints and Motions Sampling System Using Kinect Camera
2018
The needs of research on human posture and its joint-motion relationships are important. Providing a real-time postural measurement tool has attracted the attention of many human postural-related researchers. This study has developed and performed a validation analysis on a new innovative system for sampling and finding the angles of motions of each posture with its related joints using Kinect camera. The validation investigated the static and dynamic accuracy analyses by comparing to a Jamar goniometer and ErgoFellow system. The results showed that Mean Absolute Errors of Kinect in static and dynamic motions are 15.12% and 45.33% respectively. It is concluded that the postural measurement system developed by this study requires further improvements.
Modifying Kinect placement to improve upper limb joint angle measurement accuracy
Journal of Hand Therapy, 2016
Study design: Repeated measures. Introduction: The Kinect is widely used for tele-rehabilitation applications including rehabilitation games and assessment. Purpose: To determine effects of the Kinect location relative to a person on measurement accuracy of upper-limb joint angles. Methods: Kinect error was computed as difference in the upper-limb joint range of motion (ROM) during target reaching motion, from the Kinect vs. 3D Investigator™ Motion Capture, and compared across nine Kinect locations. Results: The ROM error was the least when the Kinect was elevated 45° in front of the subject, tilted toward the subject. This error was 54% less than the conventional location in front of a person without elevation/tilting. The ROM error was the largest when the Kinect was located 60° contralateral to the moving arm, at the shoulder height, facing the subject. The ROM error was the least for the shoulder elevation and largest for the wrist angle. Conclusion: This information facilitates implementation of Kinect-based upper-limb rehabilitation applications with adequate accuracy.
Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies, 2016
Many systems have been developed to facilitate upper limb rehabilitation procedures in human subjects affected by trauma or pathologies and to retrieve information about patient performance. The Microsoft Kinect sensor can be used in this context to track body motion and detect objects. In order to evaluate the usability of this device in the upper limb rehabilitation field, a comparison with a marker-based system is presented in this paper. The upper limb motion is specifically considered and the performance on its detection and tracking is evaluated. The effect of the relative location between the Kinect and the observed subject is also investigated through experimental tests performed in different configurations.
Assessment of Joint Parameters in a Kinect Sensor Based Rehabilitation Game
2019
A Kinect sensor based basketball game is developed for delivering post-stroke exercises in association with a newly developed elbow exoskeleton. Few interesting features such as audiovisual feedback and scoring have been added to the game platform to enhance patient's engagement during exercises. After playing the game, the performance score has been calculated based on their reachable points and reaching time to measure their current health conditions. During exercises, joint parameters are measured using the motion capture technique of Kinect sensor. The measurement accuracy of Kinect sensor is validated by two comparative studies where two healthy subjects were asked to move elbow joint in front of Kinect sensor wearing the developed elbow exoskeleton. In the first study, the joint information collected from Kinect sensor was compared with the exoskeleton based sensor. In the next study, the length of upperarm and forearm measured by Kinect were compared with the standard anthropometric data. The measurement errors between Kinect and exoskeleton are turned out to be in the acceptable range; 1% for subject 1 and 0.44% for subject 2 in case of joint angle; 5.55% and 3.58% for subject 1 and subject 2 respectively in case of joint torque. The average errors of Kinect measurement as compared to the anthropometric data of the two subjects are 16.52% for upperarm length and 9.87% for forearm length. It shows that Kinect sensor can measure the activity of joint movement with a minimum margin of error.
2012 Fourth International Conference on Intelligent Networking and Collaborative Systems, 2012
New and powerful hardware like Kinect introduces the possibility of changing biomechanics paradigm, usually based on expensive and complex equipment. Kinect is a markerless and cheap technology recently introduced from videogame industry. In this work we conduct a comparison study of the precision in the computation of joint angles between Kinect and an optical motion capture professional system. We obtain a range of disparity that guaranties enough precision for most of the clinical rehabilitation treatments prescribed nowadays for patients. This way, an easy and cheap validation of these treatments can be obtained automatically, ensuring a better quality control process for the patient's rehabilitation.
Accuracy evaluation of the Kinect v2 sensor during dynamic movements in a rehabilitation scenario
— In this paper, the accuracy evaluation of the Kinect v2 sensor is investigated in a rehabilitation scenario. The accuracy analysis is provided in terms of joint positions and angles during dynamic postures used in low-back pain rehabilitation. Although other studies have focused on the validation of the accuracy in terms of joint angles and positions, they present results only considering static postures whereas the rehabilitation exercise monitoring involves to consider dynamic movements with a wide range of motion and issues related to the joints tracking. In this work, joint positions and angles represent clinical features, chosen by medical staff, used to evaluate the subject's movements. The spatial and temporal accuracy is investigated with respect to the gold standard, represented by a stereophotogrammetric system, characterized by 6 infrared cameras. The results provide salient information for evaluating the reliability of Kinect v2 sensor for dynamic postures.
Accuracy and robustness of Kinect pose estimation in the context of coaching of elderly population
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2012
The Microsoft Kinect camera is becoming increasingly popular in many areas aside from entertainment, including human activity monitoring and rehabilitation. Many people, however, fail to consider the reliability and accuracy of the Kinect human pose estimation when they depend on it as a measuring system. In this paper we compare the Kinect pose estimation (skeletonization) with more established techniques for pose estimation from motion capture data, examining the accuracy of joint localization and robustness of pose estimation with respect to the orientation and occlusions. We have evaluated six physical exercises aimed at coaching of elderly population. Experimental results present pose estimation accuracy rates and corresponding error bounds for the Kinect system.
Reliability of Kinect measurements for assessing the movement of operators in ergonomic studies
Analyzing human poses at workstations is a key issue in ergonomics in order to evaluate potentials risks of musculoskeletal disorders. Kinect looks promising in measuring 3D joint kinematics on site but few studies have estimated the accuracy of the kinematic data delivered by this sensor in real situations. Thus, this study aims at evaluating the accuracy of the Kinect sensor for work-related motions. To this end we compared ISB Euler joint angles estimated with a Kinect to those obtained with a wellestablished marker-based device. Several repetitions of four of the main manual tasks described in MTM-2 classification were performed. Results showed that the RMSE between the data obtained with the two systems were 11(±3)° and 25(±4)° for the shoulder and the elbow joints respectively. Moreover, the measurement error was not randomly distributed but strongly depended on the joint configuration. Better knowledge on the Kinect measurement errors should help scientists and engineers to design appropriate protocols to measure joint kinematics in ergonomic assessments with this promising device.
Upper Limb Posture Estimation in Robotic and Virtual Reality-Based Rehabilitation
BioMed Research International, 2014
New motor rehabilitation therapies include virtual reality (VR) and robotic technologies. In limb rehabilitation, limb posture is required to (1) provide a limb realistic representation in VR games and (2) assess the patient improvement. When exoskeleton devices are used in the therapy, the measurements of their joint angles cannot be directly used to represent the posture of the patient limb, since the human and exoskeleton kinematic models differ. In response to this shortcoming, we propose a method to estimate the posture of the human limb attached to the exoskeleton. We use the exoskeleton joint angles measurements and the constraints of the exoskeleton on the limb to estimate the human limb joints angles. This paper presents (a) the mathematical formulation and solution to the problem, (b) the implementation of the proposed solution on a commercial exoskeleton system for the upper limb rehabilitation, (c) its integration into a rehabilitation VR game platform, and (d) the quantitative assessment of the method during elbow and wrist analytic training. Results show that this method properly estimates the limb posture to (i) animate avatars that represent the patient in VR games and (ii) obtain kinematic data for the patient assessment during elbow and wrist analytic rehabilitation.