Hand acceleration measurement by Kinect for rehabilitation applications (original) (raw)
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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.
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.
BioMedical Engineering OnLine, 2015
Using virtual reality systems for stroke rehabilitation is a flourishing field in physical and neurological rehabilitation. Such systems can help patients have a more intensive and entertaining training. They are commonly composed of a sensory device to capture the patient's movements, and a computer interface to communicate with the patient and Abstract Background: Performance indices provide quantitative measures for the quality of motion, and therefore, assist in analyzing and monitoring patients' progress. Measurement of performance indices requires costly devices, such as motion capture systems. Recent developments of sensors for game controllers, such as Microsoft Kinect, have motivated many researchers to develop affordable systems for performance measurement applicable to home and clinical care. In this work, the capability of Kinect in finding motion performance indices was assessed by analyzing intra-session and intersession test-retest reliability.
Motor Rehabilitation and Biotelemetry Data Acquisition with Kinect
2020
Accessibility and inclusiveness of people with disabilities is a recurring theme that is already perceived as an issue in the field of human rights. Ramps, elevators, among other devices aim at the inclusion of these individuals with limited mobility. Various types of motor limitations, specially partial limitations, are linked to corresponding physical-motor rehabilitation process, with the purpose of reducing or eliminating the patient's dependence on a caregiver or devices for adaptation. Patients with motor disabilities must practice physiotherapeutical exercises along a physician in order to perform body and muscle analysis to ensure the patient's well-being. To reach a more accurate analysis, physiotherapists use a range of devices to acquire patient data, such as the spirometer, to acquire the patient's breath intensity and lung capacity. Similarly, there are other technologies capable of acquiring motion data and quantifying them. This work aims to develop a system that, paired together with an exercise game project (exergame), can acquire and transmit the motion data acquired in-game for an easier and faster analysis of the patient's growth, relying on graphs, tables, and other visual indicators to improve the evaluation of physiotherapeutic treatments. The usage together with an exergame also has benefits such as increased patient compliance with the treatment and improvements in well-being.
Development of a Kinect Rehabilitation System
International Journal of Online Engineering (iJOE), 2013
Microsoft Kinect camera has been used in serious games applications, like for rehabilitation purposes, almost since it became available in the market. This article presents a clinical view regarding home-based physiotherapy for patients that suffered a stroke and details on the development of the rehabilitation system -Kinect-RehabPlay. This system uses the Kinect sensor together with the Unity3D game engine software to create the animation and visual environment. Currently, it is able to track, recording and comparing movements (doctor versus patient), and adjust the game configuration in real-time.
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.
Arm movement speed assessment via a Kinect camera: A preliminary study in healthy subjects
BioMedical Engineering OnLine, 2014
Background: Many clinical studies have shown that the arm movement of patients with neurological injury is often slow. In this paper, the speed of arm movements in healthy subjects is evaluated in order to validate the efficacy of using a Kinect camera for automated analysis. The consideration of arm movement appears trivial at first glance, but in reality it is a very complex neural and biomechanical process that can potentially be used for detecting neurological disorders.
Patient performance evaluation using Kinect and Monte Carlo-based finger tracking
2012
The growing use of Virtual Reality (VR) in rehabilitation is justified by a number of advantages, such as an increase of patient motivation, repetitiveness of learning trials, possibility to tailor treatment to individual subject, safety of the environment, quantitative patient improvement assessment, and remote data access. This paper proposes a novel lowcost evaluation method of patient performance in task-oriented hand rehabilitation grounded on two key elements: a Virtual Environment (VE) which the patient has to interact with, and the Microsoft Kinect motion sensing device, which is used to fully interact with the VE and to feed back patient movements in order to perform an off-line analysis. To this purpose, the VE is equipped with a virtual hand and virtual objects the patient has to interact with. In order to make the interaction between patient and VE possible, a robust marker-based finger tracking algorithm has been developed by using Bayesian estimation methods. In the proposed framework, the hand movements involved in daily activities are performed off-line by the therapist and are tracked by using the Kinect camera. The estimated hand joint trajectories are provided in input to a virtual hand model developed with the Matlab Virtual Reality Toolbox. The virtual hand reproduces the movements performed by the therapist and the patient is asked to imitate them. User motor improvements can be monitored by the Kinect camera, superimposing the therapist finger trajectories on the patient finger trajectories. The error between the two trajectories can be used for evaluating the patient residual mobility. The proposed system can be easily applied to home-based rehabilitation.
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.
Improving Movement Analysis in Physical Therapy Systems Based on Kinect Interaction
Electronic Workshops in Computing, 2017
This study uses machine learning methods to analyse Kinect body gestures involved in the user interaction with exergaming systems designed for physical rehabilitation. We propose a method to improve gesture recognition accuracy and motion analysis, by extracting from the full body motion data recorded by the Kinect sensor three important features which are relevant to physical therapy exercises: body posture, movement trajectory and range of motion. By applying the Hidden Markov Model (HMM) and Dynamic Time Warping (DTW) algorithms, we obtained an improved accuracy by selecting specific features from the public UTD-MHAD full body gestures database (with up to 56% for HMM and 32% for DTW). Preliminary results show a positive correlation between the movement amplitude and the envelope feature (r = 0.92). Thus, this approach has the potential to improve gesture recognition accuracy and provide user feedback on how to improve the movement performed, in particular the movement amplitude. We propose further improvements and method validations to be the basis of creating an intelligent virtual rehabilitation assistant.