Upper extremity 3-dimensional reachable workspace assessment in amyotrophic lateral sclerosis by Kinect sensor

Oskarsson, Björn; Joyce, Nanette C.; Bie, Evan de; Nicorici, Alina; Bajcsy, Růžena; Kurillo, Gregorij; Han, Jay J.

doi:10.1002/mus.24703

Cited by 27 publications

(25 citation statements)

References 15 publications

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“…No correlation was observed between the Z-score and the ALSFRS-R score. Although the ALSFRS-R score does correlate with overall disease progression in ALS [13] it has been shown as inadequate to accurately describe functional loss in performing arm movements [6]. The lack of correlation between the Z-score and the ALSFRS-R score may be due to a similar limitation of the ALSFRS-R to quantify functional loss in head movements.…”

Section: Discussionmentioning

confidence: 99%

“…These devices are portable, easy to use and relatively inexpensive. Nevertheless, they evaluate only isolated strength over specific muscle groups and do not provide an assessment of the overall function of a joint [6]. Thus, an assessment based only on muscle strength evaluation conveys limited information with regard the actual level of progression of the disease and there is a need for the development of tools that enable a more function based objective quantitative assessment of execution of movement.…”

Section: Introductionmentioning

confidence: 99%

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An Objective Functional Characterisation of Head Movement Impairment in Individuals with Neck Muscle Weakness Due to Amyotrophic Lateral Sclerosis

et al. 2017

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BackgroundNeck muscle weakness and head drop are well recognised in patients with Amyotrophic lateral sclerosis (ALS), but an objective characterisation of the consequent head movement impairment is lacking. The aim of this study was to quantitatively characterise head movements in ALS compared to aged matched controls.MethodsWe evaluated two groups, one of thirteen patients with ALS and one of thirteen age-matched controls, during the execution of a series of controlled head movements, performed while wearing two inertial sensors attached on the forehead and sternum, respectively. We quantified the differences between the two groups from the sensor data using indices of velocity, smoothness and movement coupling (intended as a measure of undesired out of plane movements).FindingsResults confirmed a general limitation in the ability of the ALS patients to perform and control head movements. High inter-patient variability was observed due to a wide range of observed functional impairment levels. The ability to extend the head backward and flex it laterally were the most compromised, with significantly lower angular velocity (P < 0.05, Cohen’s d > 0.8), reduced smoothness and greater presence of coupled movements with respect to the controls. A significant reduction of angular velocity (P < 0.05, Cohen’s d > 0.8) in extension, axial rotation and lateral flexion was observed when patients were asked to perform the movements as fast as possible.InterpretationThis pilot study is the first study providing a functional objective quantification of head movements in ALS. Further work involving different body areas and correlation with existing methods of evaluating neuromuscular function, such as dynamometry and EMG, is needed to explore the use of this approach as a marker of disease progression in ALS.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Objective Functional Characterisation of Head Movement Impairment in Individuals with Neck Muscle Weakness Due to Amyotrophic Lateral Sclerosis

et al. 2017

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“…The Kinect depth camera can completely overcome the above shortcomings. This camera can reliably measure the 3D reachable workspace [8], which is inexpensive and can be easily integrated into practical applications [9] to identify target crops. At present, Microsoft’s Kinect depth camera includes Kinect v1 and Kinect v2.…”

Section: Kinect V2 and Camera Calibrationmentioning

confidence: 99%

Extraction and Research of Crop Feature Points Based on Computer Vision

Cui

Zhang

Sun

et al. 2019

Sensors

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Based on computer vision technology, this paper proposes a method for identifying and locating crops in order to successfully capture crops in the process of automatic crop picking. This method innovatively combines the YOLOv3 algorithm under the DarkNet framework with the point cloud image coordinate matching method, and can achieve the goal of this paper very well. Firstly, RGB (RGB is the color representing the three channels of red, green and blue) images and depth images are obtained by using the Kinect v2 depth camera. Secondly, the YOLOv3 algorithm is used to identify the various types of target crops in the RGB images, and the feature points of the target crops are determined. Finally, the 3D coordinates of the feature points are displayed on the point cloud images. Compared with other methods, this method of crop identification has high accuracy and small positioning error, which lays a good foundation for the subsequent harvesting of crops using mechanical arms. In summary, the method used in this paper can be considered effective.

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“…Chang et al [20] developed a Kinect-based rehabilitation system for physical rehabilitation in public school settings. Han et al demonstrated the clinical significance and effectiveness of the Kinect-based reachable workspace in Duchenne muscular dystrophy (DMD) and amyotrophic lateral sclerosis (ALS) [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of Cloud Upper Limb Rehabilitation System Based on Motion Tracking for Post-Stroke Patients

Bai

Song

2019

Applied Sciences

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In order to improve the convenience and practicability of home rehabilitation training for post-stroke patients, this paper presents a cloud-based upper limb rehabilitation system based on motion tracking. A 3-dimensional reachable workspace virtual game (3D-RWVG) was developed to achieve meaningful home rehabilitation training. Five movements were selected as the criteria for rehabilitation assessment. Analysis was undertaken of the upper limb performance parameters: relative surface area (RSA), mean velocity (MV), logarithm of dimensionless jerk (LJ) and logarithm of curvature (LC). A two-headed convolutional neural network (TCNN) model was established for the assessment. The experiment was carried out in the hospital. The results show that the RSA, MV, LC and LJ could reflect the upper limb motor function intuitively from the graphs. The accuracy of the TCNN models is 92.6%, 80%, 89.5%, 85.1% and 87.5%, respectively. A therapist could check patient training and assessment information through the cloud database and make a diagnosis. The system can realize home rehabilitation training and assessment without the supervision of a therapist, and has the potential to become an effective home rehabilitation system.

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Upper extremity 3-dimensional reachable workspace assessment in amyotrophic lateral sclerosis by Kinect sensor

Cited by 27 publications

References 15 publications

An Objective Functional Characterisation of Head Movement Impairment in Individuals with Neck Muscle Weakness Due to Amyotrophic Lateral Sclerosis

An Objective Functional Characterisation of Head Movement Impairment in Individuals with Neck Muscle Weakness Due to Amyotrophic Lateral Sclerosis

Extraction and Research of Crop Feature Points Based on Computer Vision

Design and Analysis of Cloud Upper Limb Rehabilitation System Based on Motion Tracking for Post-Stroke Patients

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