We thank Bastian Strahm for taking part in the test demonstration and Laura Aguiar for proofreading this manuscript.

The test was performed in an observational, longitudinal study at University Children&#39;s Hospital of Basel (UKBB) with both adults and children. More demographics and clinical information about the study can be found in the previous publication13,14. All the procedures have been approved by the local ethics committee Ethikkomission UKBB and conducted according to the principles expressed in the Declaration of Helsinki. Written informed consent and compliance with the study protocol according to International Conference on Harmonisation (ICH) and local regulations were provided by all the subjects or the legally authorized representatives for children below the age of legal consent.
NOTE: A prototype game &#34;Wardrobe&#34; aimed at measuring the joints&#39; range of movement was designed and implemented. It was then exported as an executable application that can run on any Windows 8 or higher operating system, as long as the necessary 3-D sensor drivers are also installed. The prototype game based on Kinect sensor v1 or the second version (see Discussion) based on Kinect sensor v2 can be provided upon request (see Supplemental Coding Files).
1. Setup and preparation for the test
<ol>
	<li>Install the 3-D sensor (e.g., Kinect) drivers and the designed application on the computer.</li>
	<li>Place the computer on a suitable surface, like a table, with a height of 0.5 - 1 meter.</li>
	<li>Place the 3-D device also on the same surface aligned to the middle of the computer, so that the height of the 3-D camera is fixed by the table about 0.5 - 1 meter above the ground. Adjust the elevation angle of the 3-D sensor manually as needed to correctly capture the subject (see 2.4.2 below).</li>
	<li>Connect all the cables properly, including the 3-D adapter to the computer and the power cable to the power supply.</li>
	<li>Place a height-adjustable chair in front of the table with the computer and the 3-D sensor at about 2 meters.</li>
</ol>
2. Conducting the test
<ol>
	<li>Start the computer and turn on the sound to an optimal volume. If automatic data transmission is desired, make sure the Internet is connected.</li>
	<li>Instruct the subject to sit on the chair.</li>
	<li>Start the application on the computer and enter the subject ID on the first page.</li>
	<li>Click on the &#34;START&#34; button on the first page to enter into the second page, which shows the &#34;Wardrobe&#34; game:
	<ol>
		<li>Note the skeleton figure (upper body only) on the screen, which represents the body of the subject in front of a big wardrobe. When the figure is not seen, instruct the subject to wave and move forwards and backwards if he is in a wheelchair or stand up until the 3-D sensor captures the person.</li>
		<li>Read the instructions on the screen and adjust the distance, height, and lateral position of the chair and the subject accordingly until all the instructions are in green font. 
		NOTE: The distance, height, and lateral position of the chair can be adjusted by the subject himself or a helper who should step out of the field afterwards. If the optimal height cannot be achieved, manually adjust the elevation angle of the 3-D camera. The optimal position is 2 meters away from the device, with the neck of the subject displayed in the middle of the screen.</li>
		<li>Press the TRAIN button to start the training session without data recording. 
		NOTE: If the subject is already familiar with the game and knows what to do, press the START button directly and skip the training.</li>
		<li>Let the subject follow the instructions on the screen and perform the following movements:
		<ol>
			<li>Extend the requested arm (right or left) to reach the flickering virtual object until the object is in the virtual hand.</li>
			<li>Flex the same arm and physically touch the indicated points on the body in order to place the object.</li>
			<li>Continue the arm extension and flexion movements until all 20 objects are reached and placed. 
			&#8203;NOTE: There are two rounds with 10 objects in each round. Within a round there are 5 objects on the right side that require the right arm followed by 5 objects on the left side for which the left arm is asked. This concludes the training session.</li>
		</ol>
		</li>
		<li>Press the &#34;START&#34; button to perform the same tasks again from the beginning, as in step 2.4.3, but this time recording the locations of the 9 upper body points into an encrypted file. The game automatically finishes by either placing all the 20 objects or when the pre-defined time (4 min) runs out. 
		NOTE: It is also possible to quit the game at any time in case the subject is too weak or any other situations happen.</li>
	</ol>
	</li>
	<li>Enter into the last page automatically which shows one spider plot indicating the joints&#39; ranges from the &#34;Wardrobe&#34; game.</li>
	<li>Exit the game by pressing the END button or repeat the test by pressing the REPEAT button.</li>
</ol>
3. After the test - data handling
<ol>
	<li>Save the recorded and encrypted data and log files on the hard disk, and optionally transfer via internet to a data analyst. A detailed description of data handling and analysis has been provided in a previous publication13.
	<ol>
		<li>Confirm the completeness and validity of the data by checking the log file and comparing clinical reports with the 3-D records.</li>
		<li>Compute and extract 27 movement characteristics as numeric features, like joint angles, velocity and acceleration, hand path length, body compensation movement, reachable space, and so on.</li>
		<li>Calculate, plot and select the features in order to understand and interpret the 3-D sensor data as well as for the following statistical analysis.</li>
		<li>Perform PCA (Principal Component Analysis), ANOVA (analysis of variance), correlation analysis and linear mixed effect models as statistical analysis.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Hobart, J. C., Cano, S. J., Zajicek, J. P., Thompson, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rating+scales+as+outcome+measures+for+clinical+trials+in+neurology:+problems,+solutions,+and+recommendations.">Rating scales as outcome measures for clinical trials in neurology: problems, solutions, and recommendations.</a> Lancet Neurol. 6, 1094-1105 (2007).</li><li>Cano, S. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rasch+analysis+of+clinical+outcome+measures+in+spinal+muscular+atrophy.">Rasch analysis of clinical outcome measures in spinal muscular atrophy.</a> Muscle Nerve. 49, 422-430 (2014).</li><li>Monani, U. R., De Vivo, D. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neurodegeneration+in+spinal+muscular+atrophy:+from+disease+phenotype+and+animal+models+to+therapeutic+strategies+and+beyond.">Neurodegeneration in spinal muscular atrophy: from disease phenotype and animal models to therapeutic strategies and beyond.</a> Future Neurol. 9, 49-65 (2014).</li><li>Kolb, S. J., Kissel, J. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spinal+Muscular+Atrophy.">Spinal Muscular Atrophy.</a> Neurol Clin. 33, 831-846 (2015).</li><li>Shotton, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proceedings+of+the+2011+IEEE+Conference+on+Computer+Vision+and+Pattern+Recognition.">Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition.</a> IEEE Computer Society. , 1297-1304 (2011).</li><li>Plantard, P., Shum, H. P., Le Pierres, A. S., Multon, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validation+of+an+ergonomic+assessment+method+using+Kinect+data+in+real+workplace+conditions.">Validation of an ergonomic assessment method using Kinect data in real workplace conditions.</a> Appl Ergon. , (2016).</li><li>Galna, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accuracy+of+the+Microsoft+Kinect+sensor+for+measuring+movement+in+people+with+Parkinson's+disease.">Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease.</a> Gait Posture. 39, 1062-1068 (2014).</li><li>Otte, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accuracy+and+Reliability+of+the+Kinect+Version+2+for+Clinical+Measurement+of+Motor+Function.">Accuracy and Reliability of the Kinect Version 2 for Clinical Measurement of Motor Function.</a> PLoS One. 11, 0166532 (2016).</li><li>Clark, R. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reliability+and+concurrent+validity+of+the+Microsoft+Xbox+One+Kinect+for+assessment+of+standing+balance+and+postural+control.">Reliability and concurrent validity of the Microsoft Xbox One Kinect for assessment of standing balance and postural control.</a> Gait Posture. 42, 210-213 (2015).</li><li>Milgrom, R., Foreman, M., Standeven, J., Engsberg, J. R., Morgan, K. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reliability+and+validity+of+the+Microsoft+Kinect+for+assessment+of+manual+wheelchair+propulsion.">Reliability and validity of the Microsoft Kinect for assessment of manual wheelchair propulsion.</a> J Rehabil Res Dev. 53, 901-918 (2016).</li><li>Bonnechere, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validity+and+reliability+of+the+Kinect+within+functional+assessment+activities:+Comparison+with+standard+stereophotogrammetry.">Validity and reliability of the Kinect within functional assessment activities: Comparison with standard stereophotogrammetry.</a> Gait &amp; Posture. 39, 593-598 (2014).</li><li>Huber, M. E., Seitz, A. L., Leeser, M., Sternad, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validity+and+reliability+of+Kinect+skeleton+for+measuring+shoulder+joint+angles:+a+feasibility+study.">Validity and reliability of Kinect skeleton for measuring shoulder joint angles: a feasibility study.</a> Physiotherapy. 101, 389-393 (2015).</li><li>Chen, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Feasibility+of+Using+Microsoft+Kinect+to+Assess+Upper+Limb+Movement+in+Type+III+Spinal+Muscular+Atrophy+Patients.">Feasibility of Using Microsoft Kinect to Assess Upper Limb Movement in Type III Spinal Muscular Atrophy Patients.</a> Plos One. 12, (2017).</li><li>Bonati, U., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Longitudinal+characterization+of+biomarkers+for+spinal+muscular+atrophy.">Longitudinal characterization of biomarkers for spinal muscular atrophy.</a> Ann Clin Transl Neur. 4, 292-304 (2017).</li></ol>

The authors Xing Chen, Detlef Wolf, Juliane Siebourg-Polster, Christian Czech, Omar Khwaja and Martin Strahm are employees of F. Hoffmann-La Roche, which funded all of the research in this article.

The proposed Kinect-based assessment provided an objective and comprehensive movement analysis while providing a child-friendly, low-cost, and portable solution with respect to the traditional clinical rating scales or sophisticated marker-based video systems. With a gaming test that lasted less than 5 minutes, multiple body points were intensively examined at the same time, and many spatiotemporal and kinematic characteristics were analyzed with high accuracy including velocity, joint angles, and so on. The entire set-up and training efforts were also much less demanding compared to the rating scales or video systems.
The most critical step in this approach was the test design. In order to capture the underlying movement symptoms, the designed task should cover the corresponding capability spectrum and avoid floor or ceiling effects. In the example of this specific indication, the common physical symptoms of SMA include muscle weakness, limited joint range, muscle stiffness, fatigue, and so on. The proposed test is appropriate for these symptoms involving the limited joint range, which was typical for SMA type 2 patients. Unfortunately, the prototype was only tested as a first attempt in a planned study that recruited only SMA type 3 patients. Since the capability of those patients were above what the current test could measure (ceiling effect), the desired results could not be achieved. For this ambulant patient group, muscle fatigue and body transfer would be a better measure.
Based on this experience, a second version of the test consisting of a modified version of the &#34;Wardrobe&#34; game and an additional &#34;Boat-Rowing&#34; game were designed. The modified version of the &#34;Wardrobe&#34; game has three difficulty levels. In the first level, the objects are placed near the body so that the subject does not need to fully extend the arm, which targets the weak patient group, who can only sit in the wheelchair and cannot fully extend their arms without support. In the second level, the objects are placed at the distance of whole arm length, which targets the patient group who can lift and extend the arms without any support. In the most difficult level, the objects are placed slightly out of the arm range; therefore, the subject needs to move the upper trunk as an extension. The third level targets the ambulant patient group where the axial and proximal movement is also measured. During the positioning phase of the game, the arm length of the subject is automatically measured and then used to calculate the locations of the objects in the following game; therefore, the difficulty levels are automatically adjusted to the individual&#39;s ability. When the capability limit of each individual is reached and the objects cannot be reached or placed, the level will be skipped either automatically after a certain time or manually by the operator by pressing the &#34;SKIP&#34; button on the screen. The &#34;Boat-Rowing&#34; game targets muscle endurance and it requires the subject to repeat an arm rolling movement as fast as possible for 1 minute. In a future study, the second version is intended to cover the patient spectrum from SMA type 2 to type 3, since the tasks measure the capability of patients with limited arm movement ability to patients with full arm function and limited axial movement.
The other side of the test design is the consideration of the floor effect. Due to the limited spatial and temporal resolution, the 3-D sensor is only able to capture accurately gross movements like walking, arm waving, and so on. For fine movement detection, including figure tapping or hand turning, more sensitive digital devices such as mobile phones or wrist wearables are required. As discussed, the key for the success of such an application is to build up the correct match between underlying disease symptoms, device capabilities and designed task.
Some other considerations during the test design include age group, learning effect, languages, and so on. Since SMA affects mainly children, the test should be as simple and clear as possible while maintaining an attractive gamification trait. In our design, cartoon figures and hand-drawn objects were used. The task imitated the self-dressing behavior which is usually acquired by children after two or three years of age. The movements were kept simple such that subjects could understand and perform the test after a short training phase and learning effects were avoided, which was measured and discussed in our previous publication13.
When performing the test protocol and the data analysis, some issues might arise similarly to other 3-D sensor applications. These issues include sunshine interference, special clothes, more than one subject in the field of view, and irregular sampling times. We found one case where a subject in black clothes was not detected by the 3-D sensor in a sunny room, even when the subject was not directly in the sunshine. When more than one subject appears and disappears from the field of view, the assignment of ID numbers to detected skeletons might jump, which burdens the analysis. Even though the 3-D sensor outputs the signal at the frequency of 30 Hz in theory, the actual output may have gaps of up to one hundred ms. Therefore, it is important to track and export the timestamp.
Our first test was performed on the first version of the 3-D sensor, which has currently been replaced by a second version, and our modified version is implemented based on this second version. Between versions, the underlying drivers are different, and also the application interface (API) has changed. There are no other significant differences when migrating the application. Since both application versions can be provided freely upon request by the authors and the sensor drivers can be downloaded from the Kinect website, this is no concern to the user.
Using the 3-D sensor, we developed an innovative, quantitative, and objective upper extremity function assessment tool incorporating children-friendly game-like technology. The feasibility was explored and analyzed. Our work demonstrated the potential power of 3-D sensor as an alternative and complementary approach to movement assessment.

Comprehensive assessment of muscle function is a critical evaluation in many neuromuscular diseases and an important prerequisite for successful clinical trials. Clinical rating scales are used increasingly as a standardized assessment tool and as a well-established outcome measure1. However, they rely heavily on the subjective judgment of the clinicians and might result in substantial variations leading to inter- and intra-rater inconsistency2 or generated numbers that do not meet the criteria for precise measurements1. In addition, many neuromuscular diseases largely affect children and most of these rating scales are lengthy and boring, which impose extra challenges. An example of a neuromuscular disease is Spinal Muscular Atrophy (SMA), which is a fatal neuromuscular disease characterized by progressive muscle weakness3. Depending on the clinical phenotypes, some patients live with wheelchairs (type 2), and some can stand and walk unaided (type 3)4. There is an increasing demand for a more sensitive and objective assessment tool to measure the muscle function in terms of joint movement range, muscle strength, muscle fatigue and so on, in order to track disease progression and drug efficacy.
The rapid progress in motion sensing technology has made it possible to analyze the movement characteristics at comparatively low costs, among which the value of the 3-D sensor (Kinect) in capturing whole-body movement in a marker-free way has been widely examined. By using the integrated infrared sensor and the implemented machine learning algorithms, the body locations of the tracked persons are inferred through the 3-dimensional positions of 20 anatomical landmarks called body joints or points including head, neck, hands, wrists, elbows, shoulders, spine, hips, knees, ankles, and feet5. The temporal resolution is up to 30 Hz, which is sufficient for most physical movement except for some pathological motor symptoms such as tremors. The spatial accuracy of the 3-D sensor has been validated extensively with the ground truth6 or the gold standard, which is a marker-based 3-dimensional motion analysis system7,8,9,10,11,12. Good concurrent validity and reproducibility have been revealed by different tests, especially from the frontal view12 and for gross movements7. In order to objectively assess the upper limb function for children with SMA, we designed and implemented a game-like test based on a 3-D sensor to measure the capability of joint movement.

<table><tbody><tr><td>Microsoft Kinect for Windows v1 sensor</td><td>Microsoft</td><td>N/A</td><td>The first version of the test was developed on Kinect sensor v1 which is not sold any more. But the second version was developed on the Kinect sensor v2 which can be tested in a similar way by using Microsoft Kinect for Windows v2 sensor (GT3-00003) together with Microsoft Kinect Adapter (9J7-00009)</td></tr><tr><td>DELL XPS 2720 All-In-One PC with windows 8 operating system, 16G RAM, Intel Core i7 and 64-bits</td><td>DELL</td><td>N/A</td><td>In our setup, a All-in-one computer was used, but in fact any laptop or computer which fullfills the following requirements and a big screen for the subjects to see will work: windows 8 or higher operating system; 64-bit processor; dual-core 3.2 GHz or faster processor; dedicated USB 3.0 bus; 2 GB RAM</td></tr></tbody></table>

an objective and child-friendly assessment of arm function by using a 3-d sensor

Progressive and irreversible muscle atrophy characterizes Spinal Muscular Atrophy (SMA) and other similar muscle disorder diseases. Objective assessment of muscle functions is an essential and important, although challenging, prerequisite for successful clinical trials. Current clinical rating scales restrain the movement abnormalities to certain predefined coarse-grained individual items. The Kinect 3-D sensor has emerged as a low-cost and portable motion sensing technology used to capture and track people&#39;s movement in many medical and research fields. A novel approach using this 3-D sensor was developed and a game-like test was designed to objectively measure the upper limb function of patients with SMA. The prototype test targeted joint movement capability. While sitting in a virtual scene, the patient was instructed to extend, flex, and lift the whole arm in order to reach and place some objects. Both kinematic and spatiotemporal characteristics of upper limb movement were extracted and analyzed, e.g., elbow extension and flexion angles, hand velocity, and acceleration. The first study included a small cohort of 18 ambulant SMA patients and 19 age- and gender-matched healthy controls. A comprehensive analysis of arm movement was achieved; however, no significant difference between the groups were found due to the mismatch of patient&#39;s capability and the test difficulty. Based on this experience, a second version of the test consisting of a modified version of the first game with increased difficulties and a second game targeting muscle endurance were designed and implemented. The new test has not been conducted in any patient groups yet. Our work has demonstrated the potential capability of the 3-D sensor in assessing such muscle function and suggested an objective approach to complement the clinical rating scales.

Using the procedure presented above, different movement features are plotted and analysis approaches are explored in order to gain a comprehensive understanding of the movements.
In Figure 1, the traces from the 9 upper body points are plotted restricting to the X and Y axes which represents a 2-dimentional projection (X represents horizontal position of the subject, Y represents the vertical position while Z is the distance to the 3-D sensor). Figure 1 shows what the 3-D sensor actually measures, the spatial locations of body points. By juxtaposing one SMA patient and one healthy control over the 4 visits, it is shown that both the patient and the control completed the tasks with clearly recognizable trajectories. There were certain subject-dependent characteristics that stayed consistent over all the visits, e.g., the trajectory of the hands of the patient. In comparison, the control subject had relatively less trunk movement over time, which is indicated by the smaller crowds of red, yellow, and pink lines (the head, neck, and torso points, respectively). Neither of the two subjects showed any unsymmetrical movement features.
Figure 2 shows some representative features extracted from the time series locations of the body points. Compared to Figure 1, Figure 2 gives an idea of what potential information could be extracted and visualized from the raw 3-D sensor data in order to understand the underlying movement characteristics. Figure 2a is a segmented hands trace plot indicating elbow extension and flexion phases of two rounds from a single subject. The trajectory resembles itself quite well between the two rounds. For the three lower objects, both hands tend to overreach, but this is not the case for the upper two objects. Figure 2b plots the histogram of the hand velocities (while the hand is moving; the resting state is excluded). There is no significant difference between the left and right hands for this subject. Figure 2c shows the total path lengths of the head, neck, and torso points, which can be considered as a kind of trunk compensation movement per object position. For objects 3 and 8, which are placed at the higher corner, the trunk movement is relatively greater compared to the other positions. Moreover, the head has moved more obviously than the other two points. Figures 2d, 2e,&#160;and 2f are boxplots showing the overall distribution from all the 4 visits for some features. Only the first seven patients with all 4 visits are shown for clarity. Figure 2d shows the total hand path length divided by the individual arm length measured by the sensor in order to compensate for the inter-subject arm length difference. During the movements carried out to reach and place objects, the hand path length is around twice the arm length for one object; therefore, for 20 objects the total path length is around 40 times the arm length. Obvious inter-subject differences are observed, for example, in trunk compensation movement between patients 2 and 3 (Figure 2e) or in median hand velocity between patients 1 and 3 (Figure 2f).
Full clinical related analysis and results can be found in our earlier publication13.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57014/57014fig1.jpg" /> 
Figure 1: Representative trace plots of the 9 upper body points during the test. The top 4 plots are from one SMA patient and the bottom 4 plots are from one healthy control. This figure has been modified from our previous publication13. <a href="https://cloudflare.jove.com/files/ftp_upload/57014/57014fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57014/57014fig2.jpg" /> 
Figure 2: Representative summary analysis results: (a), (b), (c) are from a single subject and (d), (e), (f) are the summary from the first 7 SMA patients with complete 4 visits. In (d), (e), and (f), the bottom and top of the box are the first and third quartiles, and the horizontal line inside the box is the median. The lengths of the whiskers (extending vertical lines from the box) are defined as the lowest point within 1.5 interquartile ranges (IQR) of the lower quartile, and the highest point within 1.5 IQR of the upper quartile. The diamonds represent the outliers outside the whiskers. (a) Segmented hands trace plot. Solid lines represent the elbow extension phase when the hands reach out for the objects while dashed lines represent the elbow flexion phase when the hands place the objects on the body. Each color represents the objects at the same position from the two rounds. (b) Histogram of hand velocity during the extension and flexion movements. (c) Total compensation movement lengths from head, neck, and torso points for each individual object. (d) Boxplot of total hand trajectory path length for right and left hands respectively over 4 visits. The y axis shows the total hand path lengths divided by the individual arm lengths. (e) Boxplot of total trunk compensation movement including head, neck, and torso points for movement from right and left side movement respectively over 4 visits. (f) Boxplot of median hand velocity during the movement for right and left hands respectively over 4 visits. <a href="https://cloudflare.jove.com/files/ftp_upload/57014/57014fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor at JoVE.com

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

An objective measure of muscle functions is challenging especially in children. Based on a commercially available digital 3-D sensor, a child-friendly gaming test was developed to assess upper limb function for clinical trials.

Progressive and irreversible muscle atrophy characterizes Spinal Muscular Atrophy (SMA) and other similar muscle disorder diseases. Objective assessment ...

an-objective-child-friendly-assessment-arm-function-using-3-d

Research

JoVE Journal

Behavior

6.9K Views.  F. Hoffmann-La Roche, Ltd.. Een objectieve maatstaf van spier functies is uitdagend vooral bij kinderen. Op basis van een commercieel beschikbare digitale 3D-sensor, werd een kindvriendelijke gaming test ontwikkeld voor de beoordeling van de bovenste extremiteit functie voor klinische proeven.

Video: An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

The analysis of functional abilities and their development in early childhood (0-6 years old) are fundamental aspects among young children with certain types of developmental difficulties that can facilitate prevention, through programmed interventions adapted to the needs of each user (student or patient). There are, however, few investigations to date, that have analyzed the use of automated tools for recording and interpreting the results of the initial assessment. Here, a protocol is presented to examine the functional abilities in early childhood in young children, aged between 3-6 years old, with intellectual disabilities, but the protocol can also be used for ages 0 to 6 years. The protocol makes use of a computer application, eEarlyCare, that facilitates the interpretation of the results of systematic observations, which are recorded in natural environments by professionals trained in early intervention. The software can be used to analyze 11 functional areas (Food Autonomy, Personal Care and Hygiene, Dressing and Undressing Independently, Sphincter Control, Functional Mobility, Communication and Language, Daily Life Routines, Adaptive Behavior and Attention) and a total of 114 different behaviors. Its use facilitates the analysis of the observed abilities and greatly assists early intervention. Compared to other observational methods, it allows a more efficient use of personal and material resources. The use of the computer application facilitates the recording of the observation results, which helps with organization and reflection on the observations. The software displays the observation results on-screen compared to normal developmental parameters. This information can be referred to for decision-making about the most suitable intervention program for each user (student or patient). Likewise, clustering techniques are applied to analyze the relation between the type of intellectual disabilities and functional development identified with the software, a relation that is intended to serve as a guide for early-care professional intervention.

We present a protocol to use a computational tool to record and analyze the functional abilities of children aged 3-6 years old. The protocol facilitates the comparison of these abilities throughout their development and can be used to assess developmental difficulties.

The analysis of functional abilities and their development in early childhood (0-6 years old) are fundamental aspects among young children with certain ...

Integrating VR and Biosensors for Quantitative Motion-Muscle Activity Assessment

Setup for the Quantitative Assessment of Motion and Muscle Activity During a Virtual Modified Box and Block Test

The ability to move allows us to interact with the world. When this ability is impaired, it can significantly reduce one's quality of life and independence and may lead to complications. The importance of remote patient evaluation and rehabilitation has recently grown due to limited access to in-person services. For example, the COVID-19 pandemic unexpectedly resulted in strict regulations, reducing access to non-emergent healthcare services. Additionally, remote care offers an opportunity to address healthcare disparities in rural, underserved, and low-income areas where access to services remains limited. 
Improving accessibility through remote care options would limit the number of hospital or specialist visits and render routine care more affordable. Finally, the use of readily available commercial consumer electronics for at-home care can enhance patient outcomes due to improved quantitative observation of symptoms, treatment efficacy, and therapy dosage. While remote care is a promising means to address these issues, there is a crucial need to quantitatively characterize motor impairment for such applications. The following protocol seeks to address this knowledge gap to enable clinicians and researchers to obtain high-resolution data on complex movement and underlying muscle activity. The ultimate goal is to develop a protocol for remote administration of functional clinical tests. 
Here, participants were instructed to perform a medically-inspired Box and Block task (BBT), which is frequently used to assess hand function. This task requires subjects to transport standardized cubes between two compartments separated by a barrier. We implemented a modified BBT in virtual reality to demonstrate the potential of developing remote assessment protocols. Muscle activation was captured for each subject using surface electromyography. This protocol allowed for the acquisition of high-quality data to better characterize movement impairment in a detailed and quantitative manner. Ultimately, these data have the potential to be used to develop protocols for virtual rehabilitation and remote patient monitoring.

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

The protocol described here aims to enhance the quantitative evaluation of upper limb deficits, with the goal of developing additional technology for remote assessment both in the clinic and at home. Virtual reality and biosensor technologies are combined with standard clinical techniques to provide insights into the functioning of the neuromuscular system.

The ability to move allows us to interact with the world. When this ability is impaired, it can significantly reduce one's quality of life and ...

Engineering

Home-Based Monitor for Gait and Activity Analysis

Current outcomes in neuromuscular disorder clinical trials include motor function scales, timed tests, and strength measures performed by trained clinical evaluators. These measures are slightly subjective and are performed during a visit to a clinic or hospital and constitute therefore a point assessment. Point assessments can be influenced by daily patient condition or factors such as fatigue, motivation, and intercurrent illness. To enable home-based monitoring of gait and activity, a wearable magneto-inertial sensor (WMIS) has been developed. This device is a movement monitor composed of two very light watch-like sensors and a docking station. Each sensor contains a tri-axial accelerometer, gyroscope, magnetometer, and a barometer that record linear acceleration, angular velocity, the magnetic field of the movement in all directions, and barometric altitude, respectively. The sensors can be worn on the wrist, ankle, or wheelchair to record the subject&#8217;s movements during the day. The docking station enables data uploading and recharging of sensor batteries during the night. Data are analyzed using proprietary algorithms to compute parameters representative of the type and intensity of the performed movement. This WMIS can record a set of digital biomarkers, including cumulative variables, such as total number of meters walked, and descriptive gait variables, such as the percentage of the most rapid or longest stride that represents the top performance of patient over a predefined period of time.

This innovative device uses magneto-inertial sensors to permit gait and activity analysis in uncontrolled environments. Currently in the qualification process as an outcome measure in the European Medical Agency, one of the applications will be to serve as a clinical endpoint in clinical trials in neuromuscular diseases.

Current outcomes in neuromuscular disorder clinical trials include motor function scales, timed tests, and strength measures performed by trained clinical ...

Bioengineering

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

A key reason for referral to rehabilitation services after stroke and other neurological conditions is to improve one's ability to function in daily life. It has become important to measure a person's activities in daily life, and not just measure their capacity for activity in the structured environment of a clinic or laboratory. A wearable sensor that is now enabling measurement of daily movement is the accelerometer. Accelerometers are commercially-available devices resembling large wrist watches that can be worn throughout the day. Data from accelerometers can quantify how the limbs are engaged to perform activities in peoples' homes and communities. This report describes a methodology to collect accelerometry data and turn it into clinically-relevant information. First, data are collected by having the participant wear two accelerometers (one on each wrist) for 24 h or longer. The accelerometry data are then downloaded and processed to produce four different variables that describe key aspects of upper limb activity in daily life: hours of use, use ratio, magnitude ratio, and the bilateral magnitude. Density plots can be constructed that visually represent the data from the 24 h wearing period. The variables and their resultant density plots are highly consistent in neurologically-intact, community-dwelling adults. This striking consistency makes them a useful tool for determining if upper limb daily performance is different from normal. This methodology is appropriate for research studies investigating upper limb dysfunction and interventions designed to improve upper limb performance in daily life in people with stroke and other patient populations. Because of its relative simplicity, it may not be long before it is also incorporated in clinical neurorehabilitation practice.

This protocol describes a method to quantify upper limb performance in daily life using wrist-worn accelerometers.

A key reason for referral to rehabilitation services after stroke and other neurological conditions is to improve one's ability to function in daily life. ...

Medicine

Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment

Impaired hand function after neurological injuries can have a major impact on independence and quality of life. Most existing upper limb assessments are carried out in person, which is not always indicative of hand use in the community. Novel approaches to capture hand function in daily life are required to measure the true impact of rehabilitation interventions. Egocentric video combined with computer vision for automated analysis has been proposed to evaluate hand use at home. However, there are limitations to the duration of continuous recordings. We present a protocol designed to ensure that the videos obtained are representative of daily routines while respecting participant privacy. 
A representative recording schedule is selected through a collaborative process between the researchers and participants, to ensure that the videos capture natural tasks and performance, while being useful for hand assessment. Use of the equipment and procedures is demonstrated to the participants. A total of 3 h of video recordings are scheduled over two weeks. To reduce privacy concerns, participants have full control to start and stop recordings, and the opportunity to edit the videos before returning them to the research team. Reminders are provided, as well as help calls and home visits if necessary. 
The protocol was tested with 9 stroke survivors and 14 individuals with cervical spinal cord injury. The videos obtained contained a variety of activities, such as meal preparation, dishwashing, and knitting. An average of 3.11 &plusmn; 0.98 h of video were obtained. The recording periods varied from 12-69 d, due to illness or unexpected events in some cases. Data was successfully obtained from twenty-two out of 23 participants, with 6 participants requiring assistance from the investigators during the home recording period. The protocol was effective for collecting videos that contained valuable information about hand function at home after neurological injuries.

A protocol is proposed to capture natural hand function of individuals with hand impairments during their daily routines using an egocentric camera. The goal of the protocol is to ensure that the recordings are representative of an individual&#39;s typical hand use during activities of daily living at home.

Impaired hand function after neurological injuries can have a major impact on independence and quality of life. Most existing upper limb assessments are ...

Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments

Kinematic analysis is a powerful method for objective assessment of upper extremity movements in a three-dimensional (3D) space. Three-dimensional motion capture with an optoelectronic camera system is considered as golden standard for kinematic movement analysis and is increasingly used as outcome measure to evaluate the movement performance and quality after an injury or disease involving upper extremity movements. This article describes a standardized protocol for kinematic analysis of drinking task applied in individuals with upper extremity impairments after stroke. The drinking task incorporates reaching, grasping and lifting a cup from a table to take a drink, placing the cup back, and moving the hand back to the edge of the table. The sitting position is standardized to the individual&#39;s body size and the task is performed in a comfortable self-paced speed and compensatory movements are not constrained. The intention is to keep the task natural and close to a real-life situation to improve the ecological validity of the protocol. A 5-camera motion capture system is used to gather 3D coordinate positions from 9 retroreflective markers positioned on anatomical landmarks of the arm, trunk, and face. A simple single marker placement is used to ensure the feasibility of the protocol in clinical settings. Custom-made Matlab software provides automated and fast analyses of movement data. Temporal kinematics of movement time, velocity, peak velocity, time of peak velocity, and smoothness (number of movement units) along with spatial angular kinematics of shoulder and elbow joint as well as trunk movements are calculated. The drinking task is a valid assessment for individuals with moderate and mild upper extremity impairment. The construct, discriminative and concurrent validity along with responsiveness (sensitivity to change) of the kinematic variables obtained from the drinking task have been established.

This protocol describes an objective method to evaluate the movement performance and sensorimotor function of the upper extremity applied to individuals with stroke and healthy controls. A standardized test procedure, kinematic analysis and outcome variables for three-dimensional motion capture of drinking task are provided.

Kinematic analysis is a powerful method for objective assessment of upper extremity movements in a three-dimensional (3D) space. Three-dimensional motion ...

Neuroscience

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

The shoulder is one of the human body's most complex joint systems, with motion occurring through the coordinated actions of four individual joints, multiple ligaments, and approximately 20 muscles. Unfortunately, shoulder pathologies (e.g., rotator cuff tears, joint dislocations, arthritis) are common, resulting in substantial pain, disability, and decreased quality of life. The specific etiology for many of these pathologic conditions is not fully understood, but it is generally accepted that shoulder pathology is often associated with altered joint motion. Unfortunately, measuring shoulder motion with the necessary level of accuracy to investigate motion-based hypotheses is not trivial. However, radiographic-based motion measurement techniques have provided the advancement necessary to investigate motion-based hypotheses and provide a mechanistic understanding of shoulder function. Thus, the purpose of this article is to describe the approaches for measuring shoulder motion using a custom biplanar videoradiography system. The specific objectives of this article are to describe the protocols to acquire biplanar videoradiographic images of the shoulder complex, acquire CT scans, develop 3D bone models, locate anatomical landmarks, track the position and orientation of the humerus, scapula, and torso from the biplanar radiographic images, and calculate the kinematic outcome measures. In addition, the article will describe special considerations unique to the shoulder when measuring joint kinematics using this approach.

Biplane videoradiography can quantify shoulder kinematics with a high degree of accuracy. The protocol described herein was specifically designed to track the scapula, humerus, and the ribs during planar humeral elevation, and outlines the procedures for data collection, processing, and analysis. Unique considerations for data collection are also described.

The shoulder is one of the human body's most complex joint systems, with motion occurring through the coordinated actions of four individual joints, ...

Functional MRI in Conjunction with a Novel MRI-compatible Hand-induced Robotic Device to Evaluate Rehabilitation of Individuals Recovering from Hand Grip Deficits

Functional magnetic resonance imaging (fMRI) is a non-invasive magnetic resonance imaging technique that images brain activation in vivo, using endogenous deoxyhemoglobin as an endogenous contrast agent to detect changes in blood-level-dependent oxygenation (BOLD effect). We combined fMRI with a novel robotic device (MR-compatible hand-induced robotic device [MR_CHIROD]) so that a person in the scanner can execute a controlled motor task, hand-squeezing, which is a very important hand movement to study in neurological motor disease. We employed parallel imaging (generalized auto-calibrating partially parallel acquisitions [GRAPPA]), which allowed higher spatial resolution resulting in increased sensitivity to BOLD. The combination of fMRI with the hand-induced robotic device allowed precise control and monitoring of the task that was executed while a participant was in the scanner; this may prove to be of utility in rehabilitation of hand motor function in patients recovering from neurological deficits (e.g., stroke). Here we outline the protocol for using the current prototype of the MR_CHIROD during an fMRI scan.

We performed functional MRI using a novel MRI-compatible hand-induced robotic device to evaluate its utility for monitoring hand motor function in individuals recovering from neurological deficits.

Functional magnetic resonance imaging (fMRI) is a non-invasive magnetic resonance imaging technique that images brain activation in vivo, using endogenous ...

3D Kinematic Analysis for the Functional Evaluation in the Rat Model of Sciatic Nerve Crush Injury

Compared to the Sciatic Functional Index (SFI), kinematic analysis is a more reliable and sensitive method for performing functional evaluations of sciatic nerve injury rodent models. In this protocol, we describe a novel kinematic analysis method that uses a three-dimensional (3D) motion capture apparatus for functional evaluations using a rat sciatic nerve crush injury model. First, the rat is familiarized with treadmill walking. Markers are then attached to the designated bone landmarks and the rat is made to walk on the treadmill at the desired speed. Meanwhile, the posterior limb movements of the rat are recorded using four cameras. Depending on the software used, marker tracings are created using both automatic and manual modes and the desired data are produced after subtle adjustments. This method of kinematic analysis, which uses a 3D motion capture apparatus, offers numerous advantages, including superior precision and accuracy. Many more parameters can be investigated during the comprehensive functional evaluations. This method has several shortcomings that require consideration: The system is expensive, can be complicated to operate, and may produce data deviations due to skin shifting. Nevertheless, kinematic analysis using a 3D motion capture apparatus is useful for performing functional anterior and posterior limb evaluations. In the future, this method may become increasingly useful for generating accurate assessments of various traumas and diseases.

We introduce a kinematic analysis method that uses a three-dimensional motion capture apparatus containing four cameras and data processing software for performing functional evaluations during fundamental research involving rodent models.

Compared to the Sciatic Functional Index (SFI), kinematic analysis is a more reliable and sensitive method for performing functional evaluations of ...

Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand

A robot-assisted hand is used for the rehabilitation of patients with impaired upper limb function, particularly for stroke patients with a loss of motor control. However, it is unclear how conventional occupational training strategies can be applied to the use of rehabilitation robots. Novel robotic technologies and occupational therapy concepts are used to develop a protocol that allows patients with impaired upper limb function to grasp objects using their affected hand through a variety of pinching and grasping functions. To conduct this appropriately, we used five types of objects: a peg, a rectangular cube, a cube, a ball, and a cylindrical bar. We also equipped the patients with a robotic hand, the Mirror Hand, an exoskeleton&#160;hand that is fitted to the subject&#8217;s affected hand and follows the movement of the sensor glove fitted to their unaffected hand (bimanual movement training (BMT)). This study had two stages. Three healthy subjects were first recruited to test the feasibility and acceptability of the training program. Three patients with hand dysfunction caused by stroke were then recruited to confirm the feasibility and acceptability of the training program, which was conducted on 3 consecutive days. On each day, the patient was monitored during 5 min of movement in a passive range of motion, 5 min of robot-assisted bimanual movement, and task-oriented training using the five objects. The results showed that both healthy subjects and subjects who had suffered a stroke in conjunction with the robotic hand could successfully grasp the objects. Both healthy subjects and those who had suffered a stroke performed well with the robot-assisted task-oriented training program in terms of feasibility and acceptability.

This study reports the development of a novel robot-assisted task-oriented program for hand rehabilitation. The developmental process consists of experiments using both healthy subjects and subjects who have had a stroke and suffered from subsequent motor control dysfunction.

A robot-assisted hand is used for the rehabilitation of patients with impaired upper limb function, particularly for stroke patients with a loss of motor ...

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

Samenvatting

Meer video's verkennen