Posts Tagged Kinect
[Abstract] Upper Limb Three-Dimensional Reachable Workspace Analysis Using the Kinect Sensor in Hemiplegic Stroke Patients
A reachable workspace evaluation using the Kinect sensor was previously introduced as a novel upper limb outcome measure in neuromuscular and musculoskeletal conditions. This study investigated its usefulness in hemiplegic stroke patients.
Forty-one patients with hemiplegic stroke were included. Kinect-based reachable workspace analysis was performed on both paretic and nonparetic sides. Upper limb impairment was measured using the Fugl-Meyer Assessment and the Motricity Index on the paretic side. Disability was assessed using the shortened Disabilities of the Arm, Shoulder, and Hand questionnaire. Correlations between the relative surface areas, impairment scores, and disability were analyzed.
Quadrants 1, 3, and 4 as well as the total relative surface area of the paretic side were significantly reduced compared with the nonparetic side. The total relative surface area of the paretic side correlated with the Fugl-Meyer Assessment scores, the Motricity Index for Upper Extremity, and the Disabilities of the Arm, Shoulder, and Hand questionnaire score. Furthermore, quadrant 3 was the most important determinant of upper limb impairment and disability.
A reachable workspace (a sensor-based measure that can be obtained relatively quickly and unobtrusively) could be a useful and alternative outcome measure for upper limb in hemiplegic stroke patients.
[Abstract] GAMEREHAB@HOME: a new engineering system using serious game and multi-sensor fusion for functional rehabilitation at home
[ARTICLE] Feasibility, Safety and Efficacy of a Virtual Reality Exergame System to Supplement Upper Extremity Rehabilitation Post-Stroke: A Pilot Randomized Clinical Trial and Proof of Principle – Full Text
Continue —-> IJERPH | Free Full-Text | Feasibility, Safety and Efficacy of a Virtual Reality Exergame System to Supplement Upper Extremity Rehabilitation Post-Stroke: A Pilot Randomized Clinical Trial and Proof of Principle | HTML
[Abstract] Feature Evaluation of Upper Limb Exercise Rehabilitation Interactive System Based on Kinect – Full Text PDF
The virtual rehabilitation system combining virtual rehabilitation environment and upper limb rehabilitation technology is interactive and interesting, which can improve the enthusiasm and initiative of patients for rehabilitation training, improve the efficiency of rehabilitation training and improve the effect of rehabilitation treatment. This paper firstly conducts in-depth research and analysis on the research progress of the upper limb rehabilitation robot system, and deeply studies the principle and rehabilitation principle of the stroke caused by hemiplegic dyskinesia, and summarizes the goals and methods of the upper limb rehabilitation system design. Secondly, the hand motion tracking is realized by Kinect’s bone tracking, and the optimal tracking distance is determined experimentally, which verifies the stability and robustness of the tracking. Static gesture recognition adopts two gesture recognition schemes based on Kinect depth image and color space model respectively. Finally, using the rehabilitation robot and Kinect sensor as the hardware platform, the virtual rehabilitation training system experimental platform is constructed, and the horizontal rehabilitation exercise and the three-dimensional space rehabilitation exercise are respectively studied experimentally, and the exercise data obtained by using healthy subjects as the experimental object is analyzed. Based on this, the validity and feasibility of the Kinect-based upper limb exercise rehabilitation interactive system were verified.
[ARTICLE] Influence of New Technologies on Post-Stroke Rehabilitation: A Comparison of Armeo Spring to the Kinect System – Full Text
Background: New technologies to improve post-stroke rehabilitation outcomes are of great interest and have a positive impact on functional, motor, and cognitive recovery. Identifying the most effective rehabilitation intervention is a recognized priority for stroke research and provides an opportunity to achieve a more desirable effect. Objective: The objective is to verify the effect of new technologies on motor outcomes of the upper limbs, functional state, and cognitive functions in post-stroke rehabilitation. Methods: Forty two post-stroke patients (8.69 ± 4.27 weeks after stroke onset) were involved in the experimental study during inpatient rehabilitation. Patients were randomly divided into two groups: conventional programs were combined with the Armeo Spring robot-assisted trainer (Armeo group; n = 17) and the Kinect-based system (Kinect group; n = 25). The duration of sessions with the new technological devices was 45 min/day (10 sessions in total). Functional recovery was compared among groups using the Functional Independence Measure (FIM), and upper limbs’ motor function recovery was compared using the Fugl–Meyer Assessment Upper Extremity (FMA-UE), Modified Ashworth Scale (MAS), Hand grip strength (dynamometry), Hand Tapping test (HTT), Box and Block Test (BBT), and kinematic measures (active Range Of Motion (ROM)), while cognitive functions were assessed by the MMSE (Mini-Mental State Examination), ACE-R (Addenbrooke’s Cognitive Examination-Revised), and HAD (Hospital Anxiety and Depression Scale) scores. Results: Functional independence did not show meaningful differences in scores between technologies (p > 0.05), though abilities of self-care were significantly higher after Kinect-based training (p < 0.05). The upper limbs’ kinematics demonstrated higher functional recovery after robot training: decreased muscle tone, improved shoulder and elbow ROMs, hand dexterity, and grip strength (p < 0.05). Besides, virtual reality games involve more arm rotation and performing wider movements. Both new technologies caused an increase in overall global cognitive changes, but visual constructive abilities (attention, memory, visuospatial abilities, and complex commands) were statistically higher after robotic therapy. Furthermore, decreased anxiety level was observed after virtual reality therapy (p < 0.05). Conclusions: Our study displays that even a short-term, two-week training program with new technologies had a positive effect and significantly recovered post-strokes functional level in self-care, upper limb motor ability (dexterity and movements, grip strength, kinematic data), visual constructive abilities (attention, memory, visuospatial abilities, and complex commands) and decreased anxiety level.
Insufficient motor control compromises the ability of Stroke Patients (SP) to perform activities of daily living and will likely have a negative impact on the quality of life. Improving Upper Limb (UL) function is an important part of post-stroke rehabilitation in order to reduce disability . Recovery in the context of motor ability may refer to the return of pre-stroke muscle activation patterns or to compensation involving the appearance of alternative muscle activation patterns that attempt to compensate for the motor function deficit . The past decades have seen rapid development of a wide variety of assistive technologies that can be used in UL rehabilitation. These include electromyographic biofeedback, virtual reality, electromechanical and robotic devices, electrical stimulation, transcranial magnetic stimulation, direct current stimulation, and orthoses . Currently, two effective technologies that provide external feedback to SP during training, improve the retention of learned skills, and may be able to enhance the motor recovery are discussed .
Virtual Reality (VR): The Microsoft TM Kinect-based system provides feedback on movement execution and/or goal attainment . Incorporating therapy exercises into virtual games can make therapy more enjoyable and more realistic, such that task-based exercises have increased applicability in the clinical environment [6,7], increasing motivation and therefore adherence, which are useful for navigating this virtual environment; this has been identified as the most feasible for future implementation .
Electromechanical and robotic devices can move passive UL along more secure movement trajectories and provide either assistance or resistance to movement of a single joint or control of inter-segmental coordination. Recent technological advances have the ability to control multiple joints accurately at the same time, enabling them to produce more realistic task-based exercises for SP . Compared to manual therapy, robots have the potential to provide intensive rehabilitation consistently for a longer duration . Recovery of sensorimotor function after CNS damage is based on the exploitation of neuroplasticity, with a focus on the rehabilitation of movements needed for self-independence. This requires physiological limb muscle activation, which can be achieved through functional UL movement exercises and activation of the appropriate peripheral receptors . The Armeo Spring robot-assisted trainer device may improve UL motor function recovery as predicted by reshaping of cortical and transcallosal plasticity, according to the baseline cortical excitability . Knowledge of the potential brain plasticity reservoir after brain damage constitutes a prerequisite for an optimal rehabilitation strategy [12,13]. There is evidence that robot training for the hand is superior; during post-stroke rehabilitation, hand training is likely to be the most useful [8,13].
Previous studies have shown that the use of systems based on VR environments, motion sensors, and robotics can improve motor function. Currently, no high-quality evidence can be found for any interventions that are currently used as part of routine practice, and evidence is insufficient to enable comparison of the relative effectiveness of interventions [14,15,16].
The objectives of the study are to clarify in which area of functional UL recovery these new technologies are more suitable and effective and how much these interventions affect functional state and cognitive functions.
We raise the hypothesis that a robot-assisted device and virtual reality both have a positive effect on functional independence recovery in stroke-affected patients; however, having a different influence on UL motor function and cognitive changes. We assume that the robot-assisted device is more efficient and more accurately allows selecting tasks for developing specific motor function (range of motion, strength or dexterity of the affected arm), while Kinect-based games provide more free movements that are less suitable for specific motor function development and may be more targeted for cognitive functions.
[Abstract] Vision-Based Serious Games and Virtual Reality Systems for Motor Rehabilitation: A Review Geared Toward a Research Methodology
Nowadays, information technologies are being widely adopted to promote healthcare and rehabilitation. Owing to their affordability and use of hand-free controllers, vision-based systems have gradually been integrated into motor rehabilitation programs and have greatly drawn the interest of healthcare practitioners and the research community. Many studies have illustrated the effectiveness of these systems in rehabilitation. However, the report and design aspects of the reported clinical trials were disregarded.
In this paper, we present a systematic literature review of the use of vision-based serious games and virtual reality systems in motor rehabilitation programs. We aim to propose a research methodology that engineers can use to improve the designing and reporting processes of their clinical trials.
We conducted a review of published studies that entail clinical experiments. Searches were performed using Web of Science and Medline (PubMed) electronic databases, and selected studies were assessed using the Downs and Black Checklist and then analyzed according to specific research questions.
We identified 86 studies and our findings indicate that the number of studies in this field is increasing, with Korea and USA in the lead. We found that Kinect, EyeToy system, and GestureTek IREX are the most commonly used technologies in studying the effects of vision-based serious games and virtual reality systems on rehabilitation. Findings also suggest that cerebral palsy and stroke patients are the main target groups, with a particular interest on the elderly patients in this target population. The findings indicate that most of the studies focused on postural control and upper extremity exercises and used different measurements during assessment.
Although the research community’s interest in this area is growing, many clinical trials lack sufficient clarity in many aspects and are not standardized. Some recommendations have been made throughout the article.
[Abstract] Comparison of Kinect2Scratch game-based training and therapist-based training for the improvement of upper extremity functions of patients with chronic stroke: a randomized controlled single-blinded trial
BACKGROUND: Virtual reality and interactive video games could decrease the demands on the time of the therapists. However, the cost of a virtual reality system and the requirement for technical support limits the availability of these systems. Commercial exergames are not specifically designed for therapeutic use, most patients with hemiplegic stroke are either too weak to play the games or develop undesirable compensatory movements.
AIM: To develop Kinect2Scratch games and compare the effects of training with therapist-based training on upper extremity (UE) function of patients with chronic stroke.
DESIGN: A randomized controlled single-blinded trial.
SETTING: An outpatient rehabilitation clinic of a tertiary hospital.
POPULATIONS: Thirty-three patients with chronic hemiplegic stroke.
METHODS: We developed 8 Kinect2Scratch games. The participants were randomly assigned to either a Kinect2Scratch game group or a therapist-based training group. The training comprised 24 sessions of 30 minutes over 12 weeks. The primary outcome measure was the Fugl-Meyer UE scale and the secondary outcome measures were the Wolf Motor Function Test and Motor Activity Log. Patients were assessed at baseline, after intervention, and at the 3-month follow-up. We used the Pittsburgh participation scale (PPS) to assess the participation level of patients at each training session and an accelerometer to assess the activity counts of the affected UE of patients was used at the 12th and 24th training sessions.
RESULTS: Seventeen patients were assigned to the Kinect2Scratch group and 16 were assigned to the therapist-based training group. There were no differences between the two groups for any of the outcome measures post-intervention and at the 3-month follow-up (all p>.05). The level of participation was higher in the Kinect2Scratch group than in the therapist-based training group (PPS 5.25vs. 5.00, p=0.112). The total activity counts of the affected UE was significantly higher in the Kinect2Scratch group than in the therapist-based training group (p<.001).
CONCLUSIONS: Kinect2Scratch game training was feasible, with effects similar to those of therapist-based training on UE function of patients with chronic stroke.
via Comparison of Kinect2Scratch game-based training and therapist-based training for the improvement of upper extremity functions of patients with chronic stroke: a randomized controlled single-blinded trial – European Journal of Physical and Rehabilitation Medicine 2019 Feb 15 – Minerva Medica – Journals
Interactive technologies are beneficial to stroke recovery as rehabilitation interventions; however, they lack evidence for use as assessment tools. Mystic Isleis a multi-planar full-body rehabilitation game developed using the Microsoft Kinect® V2. It aims to help stroke patients improve their motor function and daily activity performance and to assess the motions of the players. It is important that the assessment results generated from Mystic Isle are accurate. The Kinect V2 has been validated for tracking lower limbs and calculating gait-specific parameters. However, few studies have validated the accuracy of the Kinect® V2 skeleton model in upper-body movements. In this paper, we evaluated the spatial accuracy and measurement validity of a Kinect-based game Mystic Isle in comparison to a gold-standard optical motion capture system, the Vicon system. Thirty participants completed six trials in sitting and standing. Game data from the Kinect sensor and the Vicon system were recorded simultaneously, then filtered and sample rate synchronized. The spatial accuracy was evaluated using Pearson’s r correlation coefficient, signal to noise ratio (SNR) and 3D distance difference. Each arm-joint signal had an average correlation coefficient above 0.9 and a SNR above 5. The hip joints data had less stability and a large variation in SNR. Also, the mean 3D distance difference of joints were less than 10 centimeters. For measurement validity, the accuracy was evaluated using mean and standard error of the difference, percentage error, Pearson’s r correlation coefficient and intra-class correlation (ICC). Average errors of maximum hand extent of reach were less than 5% and the average errors of mean and maximum velocities were about 10% and less than 5%, respectively. We have demonstrated that Mystic Isle provides accurate measurement and assessment of movement relative to the Vicon system.
In the past decade and quite rapidly in the past five years, Natural User Interfaces (NUIs) and video games have grown in popularity in both consumer applications and in healthcare [1–3]. Specifically, physical rehabilitation (e.g., physical and occupational therapy) has embraced novel NUI applications in clinics, hospitals, nursing homes, and the community [4–6]. Robotic systems have long included game-based and NUI-based user interfaces and most robotic devices provide some form of physical assistance to the patient and/or haptic feedback [7, 8]. With the release of the Nintendo Wii in 2008, many NUI applications for healthcare moved away from bulky, expensive robotics and embraced the portable nature of movement and gesture recognition devices and systems. One of the biggest breakthroughs for this field came in 2010 when Microsoft released the Kinect sensor to accompany its Xbox console system. Within days and weeks of the Kinect’s release, hackers, universities, and companies began to exploit its markerless movement sensing abilities for educational and healthcare use. Since then, there has been an exponential increase in the number of studies that report the use of the Kinect as the input device for a NUI-based rehabilitation game or feedback application [9, 10].
In 2014, Jintronix was the first company to receive FDA approval for its rehabilitation game system that uses the Microsoft Kinect. There are a number of similar companies that utilize the Kinect sensor including SeeMee , VirtualRehab , Reflexion Health , MIRA , MotionCare360 , and 5Plus Therapy . Many of these systems are marketed for delivering rehabilitation therapy in the home setting. This type of delivery is termed “tele-rehabilitation” and can involve remote monitoring by the therapist or virtual sessions over teleconferencing software [17, 18]. For telerehabilitation or remote sessions, it is imperative that the data the therapist receives from the system or movement-sensing device (such as the Microsoft Kinect) are accurate and reliable. If the therapist plans to use the data for documentation or for reimbursement from a health insurance company, the data ought to be as accurate as current clinical tools (e.g., goniometers).
Only one of the listed companies has validated the measurement capabilities of their systems and of the Microsoft Kinect. Kurillo and colleagues evaluated their system used in 5Plus Therapy against the Impulse motion-capture system (PhaseSpace Inc., San Leandro, CA) and found that it had good accuracy of joint positions and small to large percentage errors in joint angle measurements . However, this study had a small sample size of only 10 subjects and used the first version of the Kinect sensor in its validation. Additionally, the movements used in the assessment were only within a single plane for each movement and all participants were seated during data collection.
Other researchers have validated the Kinect’s measurement and tracking capabilities for both general and specific applications. Hondori and Khademi  provide an excellent summary of the work completed prior to 2014. It should be noted that all of these studies evaluated the first version of the Kinect. Following the release of the Kinect V2 sensor, most researchers have focused their validation efforts on gait and posture applications [21–24]. The Kinect V2 has good-to-excellent tracking and measurement capabilities for gait-specific parameters and clinical outcomes. However, many of these studies tracked only the lower limbs. Furthermore, gait is a relatively consistent, rhythmic motion that is consistent across participants, even in rehabilitation populations (i.e., one foot in front of the other). The full-body movements that participants are not limited to specific planes and could choose to use either hand have not been studied in current and prior comparisons of the Microsoft Kinect and optical marker-based motion capture systems.
We have developed software called Mystic Isle that utilizes the Microsoft Kinect V2 sensor as the input device . Mystic Isle is designed as a rehabilitation game and has shown good results in improving motor function and daily activity performance in persons with chronic stroke . The software initially used the first version (V1) of the Microsoft Kinect as the input device and we completed a study that compared it to the OptiTrack optical system . Based on a visual analysis, we demonstrated that for the hand and elbow, the Kinect V1 has good accuracy in calculating trajectory of movement. For the shoulder, the Kinect V1 tracking abilities limit its validity. Although these findings are promising, the types and number of movements used in the study were limited to those in a seated position and mostly in one plane of movement (e.g., sagittal). Furthermore, the tracking capabilities of the Kinect V2 have substantially improved in the past 7 years and include more data points (joints) for comparison.
The current Mystic Isle game involves multi-planar, full body movements. Designed for individuals with diverse abilities, games can be played in a sitting or standing position, depending on the therapy treatment plan. In standing, the player is able to move around in the 3-dimensional space, akin to real-world rehabilitation. Few studies have evaluated the tracking and measurement capabilities of the Microsoft Kinect V2 for full-body, multi-planar movements in both sitting and standing. The purpose of this study was to determine the spatial accuracy and measurement validity of the Microsoft Kinect V2 sensor in a NUI rehabilitation game in comparison to a gold-standard marker-based motion capture system (Vicon™).
Materials and methods
Participants were recruited via convenience sample at the University of Missouri- Columbia campus. Participants were included if they: 1) were over the age of 18, 2) could understand conversational English, and 3) had no medical conditions which prevented them from playing video games. The study has been approved by the Health Sciences Institutional Review Board at the University of Missouri with the approval number IRB 2005896 HS. All potential participants were screened and all subjects provided written informed consent before beginning the study.
Mystic Isle is a platform for rehabilitation that allows a user to interact with a virtual environment by using their body (Fig 1). The Mystic Isle software was created in Unity 3D and Mystic Isle allows the tracked user to interact with virtual environments and objects in a 3-D world. Using Mystic Isle, specific movements, distances, and locations of objects can be tailored to the abilities and requirements of the user. The system uses the Microsoft Kinect V2 camera to track participant movements. The Kinect V2 tracks 20 discrete points/joints on the body of the user. Both gross motor (stepping, jumping, squatting) and fine motor (waving the hand, turning the palm facing up, open/close hand) movements can be tracked. The Kinect V2 tracks the user in 3-dimensional space and then inputs the data in real time to the associated software, Mystic Isle. The Kinect V2 tracks and records the x, y, and z coordinates (and confidence) of each discrete joint at either 15 or 30 frames per second.
[ARTICLE] Development of a Novel Home Based Multiscene Upper Limb Rehabilitation Training and Evaluation System for Post-stroke Patients – Full Text PDF
Upper limb rehabilitation requires long-term, repetitive rehabilitation training and assessment, and many patients cannot pay for expensive medical fees in the hospital for so long time. It is necessary to design an effective, low cost, and reasonable home rehabilitation and evaluation system. In this paper, we developed a novel home based multi-scene upper limb rehabilitation training and evaluation system (HomeRehabMaster) for post-stroke patients. Based on the Kinect sensor and posture sensor, multi-sensors fusion method was used to track the motion of the patients. Multiple virtual scenes were designed to encourage rehabilitation training of upper limbs and trunk. A rehabilitation evaluation method integrating Fugl-meyer assessment (FMA) scale and upper limb reachable workspace relative surface area (RSA) was
proposed, and a FMA-RSA assessment model was established to assess upper limb motor function.
Correlation based dynamic time warping (CBDTW) was used to solve the problem of inconsistent upper limb movement path in different patients. Two clinical trials were conducted. The experimental results show that the system is very friendly to the subjects, the rehabilitation assessment results by this system are highly correlated with the therapist’s (the highest forecast accuracy was 92.7% in the 13th item), and longterm rehabilitation training can improve the upper limb motor function of the patients statistically significant (p=0.02<0.05). The system has the potential to become an effective home rehabilitation training and evaluation system.[…]
Full Text PDF —> IEEE Xplore Full-Text PDF:
[Abstract] Towards Bilateral Upper-Limb Rehabilitation after Stroke using Kinect Game – IEEE Conference Publication