Posts Tagged Microsoft Kinect

[WEB SITE] Cheap Rehab System Powered by Kinect Camera

Physical rehabilitation often involves using expensive, over-engineered equipment that does its job well but is often difficult to afford. It’s one reason the overall cost of rehab can be steep, as buying and maintaining pricey equipment can quickly add up. Some of the more advanced systems for analyzing people’s motion involve multiple cameras that track how the arms, legs, and every other part of the body move in relation to each other. These can be tens of thousands of dollars per room, which means you’re often just better off hiring another rehab specialist. Researchers at University of Missouri have developed a body motion analysis system that uses a Microsoft Kinect 2.0 depth-sensing camera and a laptop that analyzes the incoming data.

The team compared the performance of their tinkered-together body analysis system versus a high-end commercial one, that uses reflective tags placed on different parts of the body, at monitoring lower body movements, specifically when performing drop vertical jumps and lateral leg raises. They found that the two systems provided comparable data and that the new system showed enough detail to be clinically useful by rehab specialists.

Sadly, Microsoft is ending production of the Kinect cameras, but surely there will be other affordable depth sensing cameras with similar capabilities.

Here’s a video from University of Missouri with more about the technology:

Study in Human Kinetics JournalComparison of 3D Joint Angles Measured With the Kinect 2.0 Skeletal Tracker Versus a Marker-Based Motion Capture System…

Via: University of Missouri…

via Cheap Rehab System Powered by Kinect Camera | Medgadget

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[DISSERTATION] Tele-Rehabilitation of Upper Limb Function in Stroke Patients using Microsoft Kinect – Full Text PDF

ENGLISH SUMMARY

Stroke is a major cause of death and disability worldwide. The damage or death of
brain cells caused by a stroke affects brain function and leads to deficits in sensory
and/or motor function. As a consequence, a stroke can have a significantly negative
impact on the patient’s ability to perform activities of daily living and therefore also
affect the patient’s quality of life. Stroke patients may regain function through
intensive physical rehabilitation, but often they do not recover their original
functional level. The incomplete recovery in some patients might be related to e.g.
stroke severity, lack of motivation for training, or insufficient and/or non-optimal
training in the initial weeks following the stroke.
A threefold increase in the number of people living past the age of 80 in 2050,
combined with the increasing number of surviving stroke patients, will very likely
lead to a significant increase in the number of stroke patients in need of
rehabilitation. This will put further pressure on healthcare systems that are already
short on resources. As a result of this, the amount of therapeutic supervision and
support per stroke patient will most likely decrease, thereby affecting negatively the
quality of rehabilitation.
Technology-based rehabilitation systems could very likely offer a way of
maintaining the current quality of rehabilitation services by supporting therapists.
Repetition of routine exercises may be performed automatically by these systems
with only limited or even no need for human supervision. The requirements to such
systems are highly dependent on the training environment and the physical and
mental abilities of the stroke patient. Therefore, the ideal rehabilitation system
should be highly versatile, but also low-cost. These systems may even be used to
support patients at remote sites, e.g. in the patient’s own home, thus serving as telerehabilitation systems.
In this Ph.D. project the low-cost and commercially available Microsoft Kinect
sensor was used as a key component in three studies performed to investigate the
feasibility of supporting and assessing upper limb function and training in stroke
patients by use of a Microsoft Kinect sensor based tele-rehabilitation system. The
outcome of the three studies showed that the Microsoft Kinect sensor can
successfully be used for closed-loop control of functional electrical stimulation for
supporting hand function training in stroke patients (Study I), delivering visual
feedback to stroke patients during upper limb training (Study II), and automatization
of a validated motor function test (Study III).
The systems described in the three studies could be developed further in many
possible ways, e.g. new studies could investigate adaptive regulation of the intensity
used by the closed-loop FES system described in Study I, different types of feedback
to target a larger group of stroke patients (Study II), and implementation of more
sensors to allow a more detailed kinematic analysis of the stroke patients (Study III).
New studies could also test a combined version of the systems described in this
thesis and test the system in the patients’ own homes as part of a clinical trial
investigating the effect of long-term training on motor function and/or non-physical
parameters, e.g. motivational level and quality of life.[…]

Full Text PDF

via Link to publication from Aalborg University

 

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[PDF File] MINDMOTION GO: A PORTABLE VIRTUAL REALITY SYSTEM FOR POST-STROKE REHABILITATION

… After a stroke, patients usually have motor deficiency that reduces the strength and motion range of their limbs. Physiotherapeutic rehabilitation focuses on exercises that train single movements and functions of di8erent body parts: shoulder flexion/extension and abduction/adduction, wrist flexion/extension and radial/ulnar deviation, reaching, hand opening/closing and pinch, trunk axial and lateral rotation, lateral and frontal body weight transfer on the lower limbs.[…]

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[Abstract] Design and Test of a Closed-Loop FES System for Supporting Function of the Hemiparetic Hand Based on Automatic Detection Using the Microsoft Kinect Sensor

Abstract
This paper describes the design of a FES system automatically controlled in a closed loop using a Microsoft Kinect sensor, for assisting both cylindrical grasping and hand opening. The feasibility of the system was evaluated in real-time in stroke patients with hand function deficits. A hand function exercise was designed in which the subjects performed an arm and hand exercise in sitting position. The subject had to grasp one of two differently sized cylindrical objects and move it forward or backwards in the sagittal plane. This exercise was performed with each cylinder with and without FES support. Results showed that the stroke patients were able to perform up to 29% more successful grasps when they were assisted by FES. Moreover, the hand grasp-and-hold and hold-and-release durations were shorter for the smaller of the two cylinders. FES was appropriately timed in more than 95% of all trials indicating successful closed loop FES control. Future studies should incorporate options for assisting forward reaching in order to target a larger group of stroke patients.

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[Abstract] A Validation Study of Rehabilitation Exercise Monitoring Using Kinect – Computer Science & IT Book Chapter

Abstract

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.

 

Introduction

In rehabilitative health care, a carefully designed physical exercise plan could be instrumental to the recovery of a patient provided that the patient. Exercise programs are prescribed to address specific problems, and are often individually tailored by a clinician due to the presence of co-morbidities and additional impairments. It is critical that the patient perform the proscribed program correctly and with adequate practice repetitions (in the range of thousands) (Kleim and Jones, 2008), otherwise, the exercise may be ineffective, or even dangerous (Escamilla et al., 2009; Tino & Hillis, 2010).

Correct adherence to supplemental home exercise is essential for safe, effective, and efficient care. The lack of correct feedback during independent in-home exercise is therefore a serious concern. The use of simple counting devices helps verify the exercise repetitions. However, such simple, commercially available devices cannot fully capture all the required movements beyond the most simple, such as counting steps or recording overall amounts of activity (Wagner et al., 2012; Yang & Hsu, 2010), and are, therefore not useful for most prescribed home exercises.

The release of the Microsoft Kinect sensor, which is equipped with a depth camera capable of measuring 3 dimensional positions of the objects in its view, has triggered tremendous interest in its use to monitor in-home physical therapy exercises (Chang et al., 2013; Chang et al., 2012; Garcia et al., 2012; Gibson et al., 2012; Guerrero & Uribe-Quevedo, 2012; Huang, 2011; Zannatha et al., 2013; Pastor et al., 2012). A Kinect-based system could facilitate proper performance of the exercise or fitness program, increase patient accountability, allow the clinician to correct any errors in exercise performance, and allow program modification or advancement as needed. Hence, the Kinect sensor based system could potentially provide sufficient feedback and guidance to patients performing clinician prescribed in-home exercises, significantly minimizing costly and inconvenient trips to outpatient centers, and improving the effectiveness and outcomes of courses of treatment.

Many existing clinical trials with Kinect-based systems appear to have proceeded without comprehensive validation tests (Chang et al., 2013; Chang et al., 2012; Garcia et al., 2012; Gibson et al., 2012; Guerrero & Uribe-Quevedo, 2012; Huang, 2011; Zannatha et al., 2013; Zhao et al., 2014; Tamei et al., 2015; Ebert et al., 2015). Other studies have aimed to characterize the accuracy of the Kinect sensor; however, these validation studies have focused primarily on the movements within the frontal plane for a subset of the joints or segments (Clark et al., 2013; Obdrzalek et al., 2012; Mobini et al., 2013). In this article, we report our validation study on using a Kinect-based system for physical therapy exercise monitoring. Instead of comparing the joint positions or angles formed by key segments with respect to a (usually far more expensive) reference system, we take a completely different approach by focusing on the feasibility of using such a system to assess the correctness rules for a few common exercises in physical therapy. The correctness rules are readily implementable in a computer program for real-time motion tracking and feedback.

Source: A Validation Study of Rehabilitation Exercise Monitoring Using Kinect: Computer Science & IT Book Chapter | IGI Global

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[ARTICLE] Design and test of an automated version of the modified Jebsen test of hand function using Microsoft Kinect – Full Text

Abstract

Background

The present paper describes the design and evaluation of an automated version of the Modified Jebsen Test of Hand Function (MJT) based on the Microsoft Kinect sensor.

Methods

The MJT was administered twice to 11 chronic stroke subjects with varying degrees of hand function deficits. The test times of the MJT were evaluated manually by a therapist using a stopwatch, and automatically using the Microsoft Kinect sensor. The ground truth times were assessed based on inspection of the video-recordings. The agreement between the methods was evaluated along with the test-retest performance.

Results

The results from Bland-Altman analysis showed better agreement between the ground truth times and the automatic MJT time evaluations compared to the agreement between the ground truth times and the times estimated by the therapist. The results from the test-retest performance showed that the subjects significantly improved their performance in several subtests of the MJT, indicating a practice effect.

Conclusions

The results from the test showed that the Kinect can be used for automating the MJT.

Background

Deficits in motor function, in the form of hemiparesis or hemiplegia, are a frequent consequence of cerebral stroke [1]. Even though motor function may be regained to some extent through intensive rehabilitative training following acute treatment of stroke, deficits in hand function often remain [23]. Following discharge from the rehabilitation unit, patients are typically asked to perform unsupervised self-training in their own home. The lack of supervision during training at home will likely have an impact on the patient’s training compliance and training quality. Therefore, it is important to perform regular evaluations of the patient’s functional level in order to provide useful supervision and to maintain patient motivation. The patients’ performance in a specific motor function test provides valuable insight into whether the training scheme chosen for a patient is effective or it should be changed. Thus, it is very important that the motor function tests being used are objective and reflect the actual functional level of the patient being tested. Several validated motor function tests including assessment of hand function exist, e.g. Jebsen Test of Hand Function [4], Action Research Arm Test [5], Fugl-Meyer Assessment [6], Wolf Motor Function Test (WMFT) [7], Box and Blocks Test [8] and Nine Hole Peg Test [9]. Common for all these tests is that they must be administered by a therapist, which might be a source for variability in the test results, and cause the test results not always to be completely reproducible and objective. In tests including performance time as an outcome measure, e.g. the WMFT, the reaction time of the subject could introduce a bias to the results, as suggested by previous studies [1011]. Likewise, the end time of the test would likely be subjected to a bias, since the examiner has a finite reaction time. Thus, both the reaction time of the examiner and the subject could be potential sources of bias and variability in timed motor function tests. The sensitivity of a motor function test is affected by sources of bias and variability and therefore it is of interest to minimize these, to make detection of even small changes possible.

By automating motor function tests, the objectivity of the tests would be increased. This might also make possible to use the tests at remote sites, without direct supervision, as a part of a tele-rehabilitation service. Finally, automated tests could be administered more frequently. Previous studies have shown that selected parts of the WMFT can be automated by use of motion sensors mounted on the body of healthy subjects [10] and stroke patients [11]. Both systems automated the test by analyzing three-dimensional kinematics data from body-worn sensors (inertial measurement units) mounted on the most affected wrist, arm and shoulder of stroke patients [1011]. Similarly, using inertial measurement unit sensors, Yang et al. (2013) showed that when administering the 10 m walking test, the output from their system was in close agreement with the walking speeds estimated using a stop-watch [12]. These systems require though correct positioning and mounting of the motion sensors [10]. Huang et al. (2012) showed that also a computer vision based approach, consisting of a monitor camera and a Xilinx Virtex II Pro Field Programmable Gate Array (for computation), may be used for automating the WMFT. All participants being tested had to wear a black sweatband on the wrist of the extremity being tested [13]. Another low-price method for capturing the movements of a patient performing a motor function test is the Microsoft Kinect sensor (Kinect). By using a Kinect, the need for body mounted sensors is eliminated, thus lowering the susceptibility to data loss and easing donning and doffing of the system. Furthermore, the Microsoft Kinect sensor is a low-cost commercially available device. In this paper, we describe the design and test of a Kinect based system for automatic evaluation of a standardized, validated motor function test, administered to stroke patients with hand function deficits. The Modified Jebsen Test of Hand Function (MJT) [14], initially proposed by Bovend’Eerdt et al. (2004) as a test for assessment of gross functional dexterity in stroke patients, was selected for automation as this test is easy to administer and takes short time to complete.

Continue —> Design and test of an automated version of the modified Jebsen test of hand function using Microsoft Kinect | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 3 The edge of the table was detected in the binary image (lower) produced by thresholding the depth image (upper) into two parts, one part containing all pixels with a depth value lower than a depth level of 300 mm below the surface of the table and the other part containing pixels with depth values above this threshold

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[Abstract] Applying microsoft kinect for windows to develop a Stroke Rehabilitation System

Abstract:

This study develops a Stroke Rehabilitation System for stroke patients. Patients can stay at home to use this system. The design of this system is based on WHO ICF concept to develop the system and included Barthel scale for patient’s evaluation. The programs design by Microsoft C# programming language to control Microsoft Kinect System. The results demonstrate that our system can easy to implement in patient’s home. We collect patient’s daily rehabilitation data and record it to the database. The data can be statistically analyze and drop a bar chart for visually watch. The results can upload to local hospital Medical Cloud for doctor’s reference. We also demonstrate the system and provide sample code for reference.

Source: Applying microsoft kinect for windows to develop a Stroke Rehabilitation System – IEEE Xplore Document

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[Abstract] Design and Test of a Closed-Loop FES System for Supporting Function of the Hemiparetic Hand Based on Automatic Detection using the Microsoft Kinect sensor

Abstract:

This paper describes the design of a FES system automatically controlled in a closed loop using a Microsoft Kinect sensor, for assisting both cylindrical grasping and hand opening. The feasibility of the system was evaluated in real-time in stroke patients with hand function deficits. A hand function exercise was designed in which the subjects performed an arm and hand exercise in sitting position. The subject had to grasp one of two differently sized cylindrical objects and move it forward or backwards in the sagittal plane. This exercise was performed with each cylinder with and without FES support. Results showed that the stroke patients were able to perform up to 29% more successful grasps when they were assisted by FES. Moreover, the hand grasp-and-hold and hold-and-release durations were shorter for the smaller of the two cylinders. FES was appropriately timed in more than 95% of all trials indicating successful closed loop FES control. Future studies should incorporate options for assisting forward reaching in order to target a larger group of stroke patients.

Source: Design and Test of a Closed-Loop FES System for Supporting Function of the Hemiparetic Hand Based on Automatic Detection using the Microsoft Kinect sensor – IEEE Xplore Document

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[WEB SITE] The emerging role of Microsoft Kinect in physiotherapy rehabilitation for stroke patients – Physiopedia

Introduction

Effective stroke rehabilitation is dependent on patients continuing their exercise programme in the home environment [1]. Microsoft Kinect is a device which can offer innovative and exciting ways to rehabilite, making treatment more enjoyable thus increasing motivation and therefore adherence [2]. This is an important area for physiotherapists to develop their skills in, as technology is beginning to thrive in the health sector and is becoming a part of therapy treatment options. In addition it reduces workload by utilising physiotherapy time effectively while still providing therapy.

The median age of the general population is predicted to significantly rise in upcoming years. This causes much added stress for clinics and hospitals. Stroke is a significant risk factor with age and the need for new rehabilitation is in demand. Technology and its advanced methods are continuously being assessed so that they can be used in a private, home-based setting while still providing rehabilitation instructions and progress tracking. This area of rehabilitation technology is expected to expand and will do so over the coming years [3].

The Kinect allows the patient to interact with the system in a 3D environment, where they perform multiple movement combinations without the need of an attached device or a controller. It is tackling issues related to rehabilitation. Not only is it providing a rehabilitation service which improves adherence to treatment by adding fun and entertaining features, but it also reduces the high cost associated with traditional rehabilitation making it more affordable and accessible to everyone.[4]

Ideally, all stroke rehabilitation exercises would be performed with a therapist in a clinical setting on a daily basis with a recommended session of 45min per patient. However, this demand within hospitals is becoming increasing difficult and logistically impractical.[5]

Microsoft Kinect is the forerunner in commercially available hardware in which development of these new technologies can be built. Information on how it works, what is does, and an insight into Virtual Rehabilitation utilising Microsoft Kinect for the use with stroke patients will be discussed in the following Physiopedia page.[4]

Continue —> The emerging role of Microsoft Kinect in physiotherapy rehabilitation for stroke patients – Physiopedia, universal access to physiotherapy knowledge.

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