Name: kinematic analysis using 3d motion capture of drinking task in people with and without upper-extremity impairments
Uploaded: 2018-03-28T13:30:00.000+00:00
Duration: 8 min 45 s
Description: Watch this Scientific Journal Video about Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments at JoVE.com

Special thanks to Bo Johnels, Nasser Hosseini, Roy Tranberg and Patrik Almstr&#246;m for help with the initiation of this project. The research data presented in this protocol was gathered at the Sahlgrenska University Hospital.

All methods described here have been part of the studies approved by the Regional Ethical Review Board in Gothenburg, Sweden (318-04, 225-08).
1. Setting up the Motion Capture System
<ol>
	<li>Mount 4 cameras on the wall approximately 1.5 - 3 m away from the measurement area at the height of 1.5 - 2.5 m facing the measurement area. Mount one camera on ceiling just above the measurement area (Figure 1). Start the camera system.</li>
	<li>Place the L-shape calibration frame on the table with the short axis in line with the edge of the table and the long axis pointing forward. 
	NOTE: The coordinate system is defined with X-axes directed forward (anteriorly in the sagittal plane), Y-axis directed laterally (in the frontal plane), and Z-axis directed upward (superiorly, perpendicular to the transversal plane).</li>
	<li>Open the 3D tracking and data acquisition software (Track Manager), start calibration by selecting Capture | Calibrate, enter the calibration time of 30 s and click OK.</li>
	<li>Move the wand in all directions throughout the entire measurement area (75 &#215; 75 &#215; 65 cm) above the chair and table to ensure that all 5 cameras capture the wand in as many orientations as possible16,17. After the calibration, the results show on the screen. Accept calibration residuals below 0.5 mm.</li>
	<li>Have the subject, wearing a sleeveless top, sit in a in a height adjustable chair with their back against the chair&#180;s back, the upper arm in neutral adducted position, the palm of the hand resting at the table and the wrist aligned to the edge of the table. Check that the knee, hip, and elbow angles are approximately 90&#176;.</li>
	<li>Place the retroreflective passive markers with double-adhesive tape on the skeletal landmarks18 on the tested hand (third metacarpophalangeal joint), wrist (styloid process of ulna), elbow (lateral epicondyle), right and left shoulder (middle part of acromion), thorax (upper part of sternum), and forehead (notch between eyebrows).</li>
	<li>Place two markers on the cup (upper and lower edge).</li>
</ol>
2. Procedures for Motion Capture of the Drinking Task
<ol>
	<li>Place the hard-plastic cup (diameter of 7 cm, height of 9.5 cm) with 100 mL of water 30 cm from the table edge, in the midline of the body. The position of cup on the table is selected deliberately to keep the task performance natural and close to real-life situation.</li>
	<li>Ask the subject to perform the drinking task in a comfortable self-paced speed by i) reaching and grasping the cup, ii) lifting the cup from the table towards mouth, iii) taking a drink (one sip), iv) placing the cup back on the table behind a marked line (30 cm from the table edge), and v) returning to the initial position with the hand on the edge of the table.</li>
	<li>Ensure that subject understands the instructions and can reach the cup comfortably with the less-affected arm without leaning forward.</li>
	<li>Prior each recording, ensure that the start position (initial position) is correct, ask the subject to be ready, start the capture manually and give verbal instruction &#34;you can start now.&#34;</li>
	<li>When the subject finishes the task, stop the recording manually.</li>
	<li>Record five trials with short pause between each trial (approximately 30 s), starting with the less-affected arm.</li>
	<li>Check that the data acquisition has been successful (95 - 100% data for each identified marker). 
	NOTE: Marker data is automatically transferred in real-time to the data acquisition software (Track Manager). A pre-defined Automatic Marker Identification (AIM) model is used for automatic identification of the markers.</li>
	<li>When incomplete data are detected, perform extra trials after identifying the problem and adjusting the sitting or marker positions to ensure full visibility of the markers in order to obtain at least 3 successful trials. 
	NOTE: Possible problems that may occur are that markers may fall off or they are occluded from the cameras viewing angle, which results in incomplete data. However, the camera and marker set-up, as used in this protocol, produces data loss due to gaps only in very rare occasions. In total, the motion capture session takes approximately 10 - 15 min to complete.</li>
</ol>
3. Data Analysis
<ol>
	<li>Transfer the recorded data from Track Manager directly into Matlab by clicking File | Export | Directly into Matlab.</li>
	<li>Use the Matlab command at the command prompt: (&#62;&#62; workspace) to see the set of Matlab variables. 
	NOTE: Key Matlab variables containing data to be used in the instructions and in creating the analysis are: 
	QTMmeasurements.Frames - the number of frames captured 
	QTMmeasurements.FrameRate - the number of frames captured per second (240) 
	QTMmeasurements.Trajectories.Labeled.Count - number of labels (10) 
	QTMmeasurements.Trajectories.Labeled.Labels - labels as defined in the Track Manager 
	QTMmeasurements.Trajectories.Labeled.Data - measurement data in a 3D array of 10 x 3 x number&#160;of frames, where for each frame and each label the 3 coordinates are recorded</li>
	<li>In Matlab, filter the x, y, z values using the (butter) and (filtfilt) instructions with a 6Hz second-order Butterworth filter in in both forward and reverse directions, giving a zero-phase distortion and forth-order filtering. 
	NOTE: Example 
	[b, a] = butter(2, 6/240/2); % Cutoff frequency 6Hz and with respect to &#189; sampling frequency 
	xfiltered = filtfilt(b, a, QTMmeasurements.Trajectories.Labeled(1,1,:));</li>
	<li>In Matlab, create a program to use the x, y, z values for each frame sample and each label to calculate the kinematic variables such as tangential velocity of the hand, and joint angles. The kinematic variables are shown in Table 2.</li>
	<li>In Matlab, create a program to break the sequence of samples into 5 logical phases: reaching, forward transport, drinking, back transport, and returning the hand to the initial position (Figure 2). Definitions for start and end of each phase are showed in detail in Table 1.</li>
	<li>In Matlab, use the (plot) instruction to create plots of positions, velocities, joints angles, and angle-angle diagrams.</li>
</ol>

<ol>
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S., Johnels, B., Willen, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Three-dimensional+kinematic+motion+analysis+of+a+daily+activity+drinking+from+a+glass:+a+pilot+study.">Three-dimensional kinematic motion analysis of a daily activity drinking from a glass: a pilot study.</a> J Neuroeng Rehabil. 3, 18 (2006).</li><li>Alt Murphy, M., Willen, C., Sunnerhagen, K. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Movement+kinematics+during+a+drinking+task+are+associated+with+the+activity+capacity+level+after+stroke.">Movement kinematics during a drinking task are associated with the activity capacity level after stroke.</a> Neurorehab Neural Re. 26 (9), 1106-1115 (2012).</li><li>Alt Murphy, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Development and validation of upper extremity kinematic movement analysis for people with stroke. Reaching and drinking from a glass. , (2013).</li><li>Persson, H. C., Alt Murphy, M., Danielsson, A., Lundgren-Nilsson, A., Sunnerhagen, K. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+cohort+study+investigating+a+simple,+early+assessment+to+predict+upper+extremity+function+after+stroke+-+a+part+of+the+SALGOT+study.">A cohort study investigating a simple, early assessment to predict upper extremity function after stroke - a part of the SALGOT study.</a> BMC Neurol. 15, 92 (2015).</li><li>Hoonhorst, M. H., 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=How+Do+Fugl-Meyer+Arm+Motor+Scores+Relate+to+Dexterity+According+to+the+Action+Research+Arm+Test+at+6+Months+Poststroke?.">How Do Fugl-Meyer Arm Motor Scores Relate to Dexterity According to the Action Research Arm Test at 6 Months Poststroke?.</a> Arch Phys Med Rehab. 96 (10), 1845-1849 (2015).</li><li>Pang, M. Y., Harris, J. E., Eng, J. 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The authors have nothing to disclose.

The protocol can successfully be used to quantify the movement performance and quality in individuals with moderate and mild upper extremity sensorimotor impairments at all stages after stroke. The feasibility of this protocol has been proved in a clinical setting as early as 3 days post stroke, and showed that the system can be used by trained health professional without specific technical qualifications. Technical expertise is, however, needed to create and develop a program for data analysis. From this aspect, the upper extremity motion capture differs from gait analysis, in which ready-made analysis programs are generally directly provided by the manufacturers. In everyday life, arms and hands can be used in many different tasks involving manipulation and interaction with different object in various sizes, locations, and affordances. This makes each set-up unique. Further, different goals and constraints of the task will also affect the kinematic outcome, since the kinematics are highly task-specific. In the future, more efforts should be made to create a standardized protocol for kinematic analysis of basic tasks, such as, drinking, eating, taking hand to the mouth, and bimanual object manipulation, which would allow a better comparison of the results between different studies.
Based on our early experiences, with a 3-camera capture system, in which the problem with segmentations and gaps was observed, it can be suggested that a 5-camera system that allows different positions for cameras (and one above the measurement area) is optimal for the upper extremity analysis. For a clinically feasible measurement set-up, a simple set-up with limited number of markers and simplified analysis, as described in this protocol can be advocated. When the assessment of movement performance and quality aims to follow patients&#39; recovery, make prediction of future outcomes, select optimal treatment options, or evaluate the effectiveness of treatment and rehabilitation interventions, a simple, easy to use method would be enough. On the other hand, a more comprehensive biomechanical analysis using cluster-based markers would be required for more detailed modeling, particularly when the axial joint rotations and shoulder complex are of interest.
Increased clinical use of kinematic analysis is advocated by many researches in the area of neurology and stroke rehabilitation. Objective and valid methods for evaluation of motor function during natural activities and tasks are of high interest among clinicians and researchers. A recent consensus paper recommends adding kinematic measures in future stroke trials alongside with clinical assessments to distinguish between true recovery and compensation11. Challenges remain though, to determine a core set of kinematic outcomes and tasks for inclusion in trials, and to encourage broader collaboration between investigators to reach consensus11. The current 3D motion capture protocol together with published validation studies of this protocol can be one step on that direction.

Kinematic analysis describes the movements of the body through space and time, including linear and angular displacements, velocities, and accelerations. The optoelectronic motion capture systems use multiple high-speed cameras that either send out infra-red light signals to capture the reflections from passive markers placed on the body or transmit the movement data from active markers containing infrared emitting diodes. These systems are considered as &#39;gold standard&#39; for the acquisition of kinematic data1. These systems are valued for their high accuracy and flexibility in measurements of diverse tasks. Kinematic measures have shown to be effective in capturing smaller changes in movement performance and quality that may be undetected with traditional clinical scales2,3. It has been suggested that kinematics should be used for distinction between true recovery (restoration of premorbid movement characteristics) and the use of compensatory (alternative) movement patterns during the accomplishment of a task4,5.
Upper extremity movements can be quantified using end-point kinematics, generally obtained from a hand marker, and angular kinematics from joints and segments (i.e., trunk). End-point kinematics provide information about trajectories, speed, temporal movement strategies, precision, straightness, and smoothness, while angular kinematics characterize movement patterns in terms of temporal and spatial joint and segment angles, angular velocities, and interjoint coordination. End-point kinematics, such as, movement time, speed, and smoothness are effective to capture the deficits and improvements in movement performance after stroke6,7,8 and angular kinematics show whether the movements of joints and body segments are optimal for a specific task. Kinematics from people with impairments are often compared with movement performance in individuals without impairments8,9. End-point and angular kinematics are correlated in a way that a movement performed with effective speed, smoothness, and precision will require good movement control, coordination, and use of effective and optimal movement patterns. For example, a patient with stroke who moves slowly usually also shows decreased smoothness (increased number of movement units), lower maximum velocity, and increased trunk displacement8. On the other hand, improvements in endpoint kinematics, such as movement speed and smoothness might occur independently from the changes of compensatory movement strategies of trunk and arm10. It has been established that kinematic analysis may provide additional and more precise information about how the task is accomplished after an injury or disease, which in turn is essential for individualized effective treatment to reach optimal motor recovery11. Kinematic analysis is increasingly used in clinical studies to describe the movements in people with upper extremity impairments after stroke8,9, to evaluate motor recovery7,12,13 or to determine the effectiveness of therapeutic interventions10,14.
Movement tasks often studied in stroke are pointing and reaching, although the use of functional tasks that incorporate manipulation of real everyday objects is increasing1. Since kinematics of reaching depend on the experimental constraints such as the selection of objects and the goal of the task15, it is essential to assess movements during purposeful and functional tasks in which the real difficulties in individual&#39;s daily life will be reflected more closely.
Thus, the aim of this paper is to provide a detailed description of a simple standardized protocol used for kinematic analysis of a purposeful and functional task, drinking task, applied to individuals with upper extremity impairments in acute and chronic stages after stroke. Results from the validation of this protocol for individuals with moderate and mild stroke impairment will be summarized.

<table><tbody><tr><td>5 camera optoelectronic ProReflex Motion capture system (MCU 240 Hz)</td><td>Qualisys AB, Gthenburg, Sweden</td><td>N/A</td><td>Movement analysis system with passive retroreflective markers</td></tr><tr><td>Markers</td><td>Qualisys AB, Gthenburg, Sweden</td><td>N/A</td><td>Retroleflective passive circular markers, diameter of 12 mm</td></tr><tr><td>Calibration frame and wand</td><td>Qualisys AB, Gthenburg, Sweden</td><td>N/A</td><td>L-shape calibration frame (defines the origin and orientation of the coordinate system); T-shape wand (300 mm)</td></tr><tr><td>Qualisys Track Manager</td><td>Qualisys AB, Gthenburg, Sweden</td><td>N/A</td><td>3D Tracking software</td></tr><tr><td>Matlab</td><td>Mathworks, Inc, Natick, Ca</td><td>N/A</td><td>Data analysis software</td></tr></tbody></table>

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.

The protocol described in this article has been applied to individuals with stroke and healthy controls2,6,8,19,20,21. In total, kinematic data from 111 individuals with stroke and 55 healthy controls have been analyzed in different studies. The upper extremity impairment after stroke was defined as moderate (FMA-UE score 32-57) or mild (FMA-UE score 58-66)8,22,23,24. In healthy controls, no significant differences were found between the dominant and non-dominant arm except for the peak velocity and therefore the non-dominant arm was chosen for comparison 2,8. The majority of data was collected within a large longitudinal cohort study, the Stroke Arm Longitudinal study at University of Gothenburg (SALGOT), which includes a non-selected sample of 122 individuals with stroke and involves the assessments at 3 days post stroke and follow up at 10 days, 4 weeks, 3, 6, and 12 months25.
In summary, our results show that the protocol is feasible in clinical settings since a large number of patients were tested as early as 3 days post-stroke at the acute hospitals stroke unit. Feasibility was also proven by the fact that two experienced physiotherapists managed to calibrate and use the motion capture system on daily basis without any larger technical problems (no support from the system providers was needed during the 3 years of data collection). The data quality was good and the pre-programmed automatic procedures for analyses could generally be applied. Only in few recordings, the phases were not detected correctly, often due to the extra movements in the beginning/end of the movement or when the movement speed was extremely low in patients with more severe impairments. In these cases, the extra trials were often used after a manual inspection of the plotted data. The test protocol demonstrated a good consistency in test-retest in healthy individuals and provided clear and accurate results19.
The movements in every phase of the drinking task and for the entire task are slower (Table 3) in people with stroke, although the relative time spend in each phase is similar to controls21. Similarly, both tangential and angular velocities are lower in people with stroke compared to healthy controls (Table 3). The peak velocity occurred approximately at 38% of the total reaching time in stroke and a 46% in controls, which means that the deceleration phase was prolonged in stroke. This indicates that individuals with stroke need to rely more on the feedback driven movement control during the second half of reaching.
The velocity profiles in people with stroke are segmented and show multiple peaks, which is reflected in the high number of the movement units (NMU). The mean value for the NMU is significantly larger in individuals with stroke compared to controls. Individuals with stroke reach the cup with a more flexed elbow (less elbow extension) and with the shoulder more abducted while drinking compared to the healthy participants, which reflects the compensatory movement pattern in stroke. Even though the glass was positioned within the arm reach, individuals with stroke did lean forward (trunk displacement) approximately 8 cm compared to 3 cm in controls while performing the drinking task. Decreased interjoint coordination between shoulder and elbow joint in reaching was only observed in individuals with higher degree of impairment (moderate stroke) compared to controls. The exact values for kinematics and the magnitude of effect sizes for all groups are shown in Table 3.
The analysis of construct validity of kinematic variables from drinking task showed that movements after stroke can be described with two major factors, the end-point kinematics and kinematics describing angular movement patterns8. Altogether, five measures (movement time, peak velocity, number of movement units, peak angular velocity of elbow joint, and trunk displacement) explained 86% of the variance in kinematic data8. These results are in line with concurrent validity analyses, in which three kinematic variables, movement time (MT), movement smoothness (NMU) and trunk displacement (TD), together explained 67% of the total variance in scores of clinical assessments as assessed with Action Research Arm Test20. The discriminative validity between groups with mild and moderate arm impairment after stroke and controls was good for majority of kinematics, but the largest effect sizes were noted for the smoothness, total movement time, peak angular velocity of elbow (PAVE) and trunk displacement (Table 3)8. Shoulder abduction during drinking is also discriminative between moderate and mild stroke groups. In addition, the same four kinematic variables: MT, NMU, PAVE, and TD demonstrated to be effective in detecting real clinical improvement during the first 3 months after stroke6. Thus, it can be concluded that these four kinematic variables (MT, NMU, PAVE, TD) are reliable, valid and sensitive to changes (responsive) for assessment of upper extremity function and activity after stroke.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57228/57228fig1.jpg" /> 
Figure 1: The 5-camera motion capture system set-up for drinking task. From each camera, infra-red light flashes reach retro-reflective markers and reproduce the 2D position of the marker in the cameras image sensor with high spatial resolution and accuracy in real-time. The 3D coordinates of the marker are created when two cameras are viewing the same marker from two different angles. Four cameras are mounted on the walls around the testing area facing slightly downward at approximately 2 m distance and one camera is mounted facing down from the ceiling above the measurement area. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57228/57228fig1large.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/57228/57228fig2.jpg" /> 
Figure 2: Representative velocity profiles for a healthy control (A) and an individual with moderate stroke impairment (B). The phases of the drinking task are shown. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57228/57228fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Phase name</td>
			<td>Start</td>
			<td>Detected by</td>
			<td>End</td>
			<td>Detected by</td>
		</tr>
		<tr>
			<td>Reaching 
			(includes grasping)</td>
			<td>Hand movement begins</td>
			<td>The hand marker velocity exceeds 2% of the peak velocity (searched backward from the peak velocity); if this value is higher than 20mm/s the start is tracked backward to a point where the velocity is&#160; not less or equal to 20 mm/s&#160;</td>
			<td>Hand begins to move towards the&#160; mouth with the glass&#160;</td>
			<td>Velocity of the glass exceeds 15 mm/s</td>
		</tr>
		<tr>
			<td>Forward transport 
			(glass to mouth)</td>
			<td>Hand begins to move towards the mouth with the glass</td>
			<td>Velocity of the glass exceeds 15 mm/s</td>
			<td>Drinking begins</td>
			<td>Distance between the face and glass marker is below 15% of steady state* during drinking</td>
		</tr>
		<tr>
			<td>Drinking</td>
			<td>Drinking begins</td>
			<td>Distance between the face and glass marker is below 15% of steady state during drinking</td>
			<td>Drinking ends</td>
			<td>Distance between the face and glass marker exceeds 15% of steady state during drinking</td>
		</tr>
		<tr>
			<td>Back transport (glass to table, includes release of grasp)</td>
			<td>Hand begins to move to put the glass back to table</td>
			<td>Distance between the face and glass marker exceeds 15% of steady state during drinking</td>
			<td>Hand releases the glass and begins to move back to initial position</td>
			<td>Velocity of the glass below 10 mm/s</td>
		</tr>
		<tr>
			<td>Returning 
			(hand back to initial position)</td>
			<td>Hand releases the glass and begins to move back to initial position</td>
			<td>Velocity of the glass below 10 mm/s</td>
			<td>Hand is resting in initial position</td>
			<td>Hand marker velocity returned to 2% of the peak velocity&#160;</td>
		</tr>
		<tr>
			<td colspan="5">*Steady state in the drinking phase indicates an averaged value of the 100 frames around the shortest distance between the face and glass marker</td>
		</tr>
	</tbody>
</table>
Table 1: Phase definitions for the start and end of each phase of the drinking task.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Variable</td>
			<td>Specification</td>
		</tr>
		<tr>
			<td>End-point kinematics</td>
			<td>Calculated from the hand marker</td>
		</tr>
		<tr>
			<td>Movement time, s</td>
			<td>Calculated for each phase and as total movement time for the entire task; definitions for start and stop are provided in Table 1</td>
		</tr>
		<tr>
			<td>Peak tangential velocity, mm/s</td>
			<td>Calculated for reaching phase, combines both arm and trunk movement</td>
		</tr>
		<tr>
			<td>Time to peak hand velocity, s, %</td>
			<td>Absolute and relative values for reaching, characteraizes movement strategy (acceleration and deceleration time)</td>
		</tr>
		<tr>
			<td>Time to first velocity peak, s, %</td>
			<td>Absolute and relative values for reaching, characteraizes the initial movement effort</td>
		</tr>
		<tr>
			<td>Number of movement units, n</td>
			<td>Calculated for reaching, forward transport, back transport, and returning phase. One movement unit is defined as a difference between a local minimum and next maximum velocity value that exceeds the amplitude limit of 20 mm/s, and the time between two subsequent peaks has to be at least 150 ms. The minimum value for drinking task is 4, at least one unit per movement phase. Those peaks reflect repetitive acceleration and deceleration during reaching and correspond to movement smoothness and efficiency.</td>
		</tr>
		<tr>
			<td>Angular kinematics, degrees</td>
			<td>Calculated for shoulder and elbow joint</td>
		</tr>
		<tr>
			<td>Elbow extension</td>
			<td>Minimum angle of elbow flexion detected in the reaching phase, determined by the angle between the vectors joining the elbow and wrist markers and the elbow and shoulder markers</td>
		</tr>
		<tr>
			<td>Shoulder abduction</td>
			<td>Maximum angle in frontal plane detected during reaching and drinking phase, respectively; determined by the angle between the vectors joining the shoulder and elbow markers and the vertical vector from the shoulder marker toward the hip</td>
		</tr>
		<tr>
			<td>Shoulder flexion</td>
			<td>Maximum angle in sagittal plane detected during reaching and drinking, respectively; determined by the angle between the vectors joining the shoulder and elbow markers and the vertical vector from the shoulder marker toward the hip</td>
		</tr>
		<tr>
			<td>Peak angular velocity of the elbow joint, degrees/s</td>
			<td>Peak velocity of the elbow extension detected during the reaching phase</td>
		</tr>
		<tr>
			<td>Interjoint coordination, r</td>
			<td>Temporal cross-correlation of zero time lag between the shoulder flexion and elbow extension during the reaching phase. A Pearson&#39;s correlation coefficient closer to 1 indicates stronger correlation and indicates that joint motion of the two joints is tightly coupled.</td>
		</tr>
		<tr>
			<td>Trunk displacement, mm</td>
			<td>Maximum displacement of the thorax marker from the initial position during the entire drinking task</td>
		</tr>
	</tbody>
</table>
Table 2: Definitions of kinematic variables used in studies presented in the representative results.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Kinematic variables, mean (SD)&#160;</td>
			<td>Healthy</td>
			<td>Stroke</td>
			<td>Effect size (healthy vs stroke)</td>
			<td>Mild 
			stroke</td>
			<td>Effect size 
			(healthy vs&#160; mild stroke)</td>
			<td>Moderate stroke&#160;</td>
			<td>Effect size 
			(mild stroke vs&#160; moderate stroke)</td>
		</tr>
		<tr>
			<td>End-point kinematics</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Total movement time, s</td>
			<td>6.49 (0.83)</td>
			<td>11.4 (3.1)</td>
			<td>0.54*</td>
			<td>9.30 (1.68)</td>
			<td>0.46*</td>
			<td>13.3 (2.9)</td>
			<td>0.44*</td>
		</tr>
		<tr>
			<td>Number Movement Units, (smoothness), n</td>
			<td>2.3 (0.3)</td>
			<td>8.4 (4.2)</td>
			<td>0.54*</td>
			<td>5.4 (2.1)</td>
			<td>0.42*</td>
			<td>11.1 (3.6)</td>
			<td>0.50*</td>
		</tr>
		<tr>
			<td>Peak velocity in reach, mm/s</td>
			<td>616 (93.8)</td>
			<td>431 (82.7)&#160;</td>
			<td>0.54*</td>
			<td>471 (87.7)</td>
			<td>0.37*</td>
			<td>395 (62.0)</td>
			<td>0.22*</td>
		</tr>
		<tr>
			<td>Peak angular velocity elbow in reach, &#176;/s</td>
			<td>121.8 (25.3)</td>
			<td>64.9 (20.5)</td>
			<td>0.62*</td>
			<td>78.0 (19.3)</td>
			<td>0.57*</td>
			<td>53.3 (13.6)</td>
			<td>0.38*</td>
		</tr>
		<tr>
			<td>Time to peak velocity in reach, %</td>
			<td>46.0 (6.9)</td>
			<td>38.4 (8.6)</td>
			<td>0.20*</td>
			<td>39.5 (8.7)</td>
			<td>0.15*</td>
			<td>37.5 (8.8)</td>
			<td>0.01</td>
		</tr>
		<tr>
			<td>Time to first peak in reach, %</td>
			<td>42.5 (6.9)</td>
			<td>27.1 (12.2)</td>
			<td>0.39*</td>
			<td>33.0 (9.9)</td>
			<td>0.25*</td>
			<td>21.8 (11.9)</td>
			<td>0.22*</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Angular joint kinematics</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Elbow extension in reach-to-grasp, degree</td>
			<td>53.5 (7.8)</td>
			<td>64.1 (11.5)</td>
			<td>0.24*</td>
			<td>60.5 (10.4)</td>
			<td>0.13</td>
			<td>67.2 (11.9)</td>
			<td>0.09</td>
		</tr>
		<tr>
			<td>Shoulder abduction in drinking, degree</td>
			<td>30.1 (10.1)</td>
			<td>47.6 (14.9)</td>
			<td>0.33*</td>
			<td>37.2 (5.3)</td>
			<td>0.07</td>
			<td>57.1 (14.5)</td>
			<td>0.47*</td>
		</tr>
		<tr>
			<td>Trunk Displacement, mm</td>
			<td>26.7 (16.8)</td>
			<td>77.2 (48.6)</td>
			<td>0.34*</td>
			<td>50.1 (22.9)</td>
			<td>0.26*</td>
			<td>101.7 (53.4)</td>
			<td>0.30*</td>
		</tr>
		<tr>
			<td>Interjoint coordination, Pearson r</td>
			<td>0.96 (0.02)</td>
			<td>0.82 (0.35)</td>
			<td>0.08</td>
			<td>0.95 (0.02)</td>
			<td>0.03</td>
			<td>0.69 (0.46)</td>
			<td>0.14</td>
		</tr>
		<tr>
			<td colspan="8">* p&#60;0.05; Effect size statistics are calculated as eta squared, &#951;2</td>
		</tr>
	</tbody>
</table>
Table 3: Kinematic variables for individuals with stroke, for subgroups of moderate&#160;and mild upper extremity impairment&#160;along with healthy controls. Effect sizes for discrimination between groups above 0.4 (very large effect) are marked bold.

Watch this Scientific Journal Video about Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments at JoVE.com

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

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 ...

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Unit 1

3-Dimensional Kinetics of a Rigid Body

SCIENTISTS IN ACTION

10.6K Views.  University of Gothenburg. Dit protocol beschrijft een objectieve methode om te evalueren van de prestaties van de beweging en de sensomotorische functie van de bovenste extremiteit toegepast op personen met een beroerte en gezonde controles. Een gestandaardiseerde testprocedure, Kinematische analyse en resultaten variabelen voor drie-dimensionale motion-video vastleggen van het drinken van taak worden geleverd.

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

Corticospinal Excitability Modulation During Action Observation

This study used the transcranial magnetic stimulation/motor evoked potential (TMS/MEP) technique to pinpoint when the automatic tendency to mirror someone else's action becomes anticipatory simulation of a complementary act. TMS was delivered to the left primary motor cortex corresponding to the hand to induce the highest level of MEP activity from the abductor digiti minimi (ADM; the muscle serving little finger abduction) as well as the first dorsal interosseus (FDI; the muscle serving index finger flexion/extension) muscles. A neuronavigation system was used to maintain the position of the TMS coil, and electromyographic (EMG) activity was recorded from the right ADM and FDI muscles. Producing original data with regard to motor resonance, the combined TMS/MEP technique has taken research on the perception-action coupling mechanism a step further. Specifically, it has answered the questions of how and when observing another person's actions produces motor facilitation in an onlooker's corresponding muscles and in what way corticospinal excitability is modulated in social contexts.

Single-pulse transcranial magnetic stimulation over the primary motor cortex, neuronavigation, and registration of electromyographic activity of hand muscles were used in this study to explore corticospinal excitability while participants were observing action sequences.

This study used the transcranial magnetic stimulation/motor evoked potential (TMS/MEP) technique to pinpoint when the automatic tendency to mirror someone ...

Behavior

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans

Prehension, the act of reaching to grasp an object, is central to the human experience. We use it to feed ourselves, groom ourselves, and manipulate objects and tools in our environment. Such behaviors are impaired by many sensorimotor disorders, yet our current understanding of their neural control is far from complete. Current technologies for investigating human reach-to-grasp movements often utilize motion tracking systems that can be expensive, require the attachment of markers or sensors to the hands, impede natural movement and sensory feedback, and provide kinematic output that can be difficult to interpret. While generally effective for studying the stereotypical reach-to-grasp movements of healthy sighted adults, many of these technologies face additional limitations when attempting to study the unpredictable and idiosyncratic reach-to-grasp movements of young infants, unsighted adults, and patients with neurological disorders. Thus, we present a novel, inexpensive, and highly reliable yet flexible protocol for quantifying the temporal and kinematic structure of idiosyncratic reach-to-grasp movements in humans. High speed video cameras capture multiple views of the reach-to-grasp movement. Frame-by-frame video analysis is then used to document the timing and magnitude of pre-defined behavioral events such as movement start, collection, maximum height, peak aperture, first contact, and final grasp. The temporal structure of the movement is reconstructed by documenting the relative frame number of each event while the kinematic structure of the hand is quantified using the ruler or measure function in photo editing software to calibrate 2 dimensional linear distances between two body parts or between a body part and the target. Frame-by-frame video analysis can provide a quantitative and comprehensive description of idiosyncratic reach-to-grasp movements and will enable researchers to expand their area of investigation to include a greater range of naturalistic prehensile behaviors, guided by a wider variety of sensory modalities, in both healthy and clinical populations.

This protocol describes how to use frame-by-frame video analysis to quantify idiosyncratic reach-to-grasp movements in humans. A comparative analysis of reaching in sighted versus unsighted healthy adults is used to demonstrate the technique, but the method can also be applied to the study of developmental and clinical populations.

Prehension, the act of reaching to grasp an object, is central to the human experience. We use it to feed ourselves, groom ourselves, and manipulate ...

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.

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 ...

Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality

The inability to complete instrumental activities of daily living (IADL) is a precursor to various neuropsychological diseases. Questionnaire-based assessments of IADL are easy to use but prone to subjective bias. Here, we describe a novel virtual reality (VR) test to assess two complex IADL tasks: handling financial transactions and using public transportation. While a participant performs the tasks in a VR setting, a motion capture system traces the position and orientation of the dominant hand and head in a three-dimensional Cartesian coordinate system. Kinematic raw data are collected and converted into &#39;kinematic performance measures,&#39; i.e., motion trajectory, moving distance, and time to completion. Motion trajectory is the path of a particular body part (e.g., dominant hand or head) in space. Moving distance refers to the total distance of the trajectory, and time to completion is how long it took to complete an IADL task. These kinematic measures could discriminate patients with cognitive impairment from healthy controls. The development of this kinematic measuring protocol allows detection of early IADL-related cognitive impairments.

We designed a virtual reality test to assess instrumental activities of daily living (IADL) with a motion capture system. We propose a detailed kinematic analysis to interpret the participant&#39;s various movements, including trajectory, moving distance, and time to completion to evaluate IADL capabilities.

The inability to complete instrumental activities of daily living (IADL) is a precursor to various neuropsychological diseases. Questionnaire-based ...

3D Kinematic Gait Analysis for Preclinical Studies in Rodents

The utility of three-dimensional (3D) kinematic motion analysis systems is limited in rodents. Part of the reason for this inadequacy is the use of complex algorithms and mathematical modeling that accompany 3D data collection and analysis procedures. This work provides a simple, user-friendly, step-by-step detailed methodology for 3D kinematic gait analysis during treadmill locomotion in healthy and neurotraumatic rats using a six-camera motion capture system. Also provided are details on 1) calibration of the system in an experimental set-up customized for quadrupedal locomotion, 2) data collection for treadmill locomotion in adult rats using markers positioned on all four limbs, 3) options available for video tracking and processing, and 4) basic 3D kinematic data generation and visualization and quantification of data using the built-in data collection software. Finally, it is suggested that the utility of this motion capture system be expanded to studying a variety of motor behaviors before and after neurotrauma.

Presented here is a protocol to collect and analyze three-dimensional kinematics of quadrupedal locomotion in rodents for preclinical studies.

The utility of three-dimensional (3D) kinematic motion analysis systems is limited in rodents. Part of the reason for this inadequacy is the use of ...

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 ...

Bioengineering

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 ...

The Impact of Motor Task Conditions on Goal-Directed Arm Reaching Kinematics and Trunk Compensation in Chronic Stroke Survivors

Trunk compensation is the most common movement strategy to substitute for upper extremity (UE) motor deficits in chronic stroke survivors. There is a lack of evidence examining how task conditions impact trunk compensation and goal-directed arm reaching kinematics. This protocol aims to investigate the impact of task conditions, including task difficulty and complexity, on goal-directed arm reaching kinematics. Two non-disabled young adults and two chronic stroke survivors with mild UE motor impairment were recruited for testing the protocol. Each participant performed goal-directed arm reaches with four different task conditions (2 task difficulties [large vs. small targets] X 2 task complexities [pointing vs. picking up]). The task goal was to reach and point at a target or pick up an object located 20 cm in front of the home position as quickly as possible with a stylus or a pair of chopsticks, respectively, in response to an auditory cue. Participants performed ten reaches per task condition. A 3-dimensional motion capture camera system was used to record trunk and arm kinematics. Representative results showed that there was a significant increase in movement duration, movement jerkiness, and trunk compensation as a function of task complexity, but not task difficulty in all participants. Chronic stroke survivors showed significantly slower, jerkier, and more feedback-dependent arm reaches and significantly more compensatory trunk movements than non-disabled adults. Our representative results support that this protocol can be used to investigate the impact of task conditions on motor control strategies in chronic stroke survivors with mild UE motor impairment.

This protocol is intended to investigate the impact of task conditions on movement strategies in chronic stroke survivors. Further, this protocol can be used to examine if a restriction in elbow extension induced by neuromuscular electrical stimulation causes trunk compensation during goal-directed arm reaches in non-disabled adults.

Trunk compensation is the most common movement strategy to substitute for upper extremity (UE) motor deficits in chronic stroke survivors. There is a lack ...

Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients

Eighteen stroke patients were recruited for this study involving the evaluation of cognition and walking ability and multitask gait analysis. Multitask gait analysis consisted of a single walking task (Task 0), a simple motor dual-task (water-holding, Task 1), and a complex motor dual-task (crossing obstacles, Task 2). The task of crossing obstacles was considered to be equivalent to the combination of a simple walking task and a complex motor task as it involved more nervous system, skeletal movement, and cognitive resources. To eliminate heterogeneity in the results of the gait analysis of the stroke patients, the dual-task gait cost values were calculated for various kinematic parameters. The major differences were observed in the proximal joint angles, especially in the angles of the trunk, pelvis, and hip joints, which were significantly larger in the dual motor tasks than in the single walking task. This research protocol aims to provide a basis for the clinical diagnosis of gait function and an in-depth study of motor control in stroke patients with motor control deficits through the analyses of dual-motor walking tasks.

This paper presents a protocol specifically for dual motor task gait analysis in stroke patients with motor control deficits.

Eighteen stroke patients were recruited for this study involving the evaluation of cognition and walking ability and multitask gait analysis. Multitask ...

Comprehensive Understanding of Inactivity-Induced Gait Alteration in Rodents

It is well known that disuse affects neural systems and that joint motions become altered; however, which outcomes properly exhibit these characteristics is still unclear. The present study describes a motion analysis approach that utilizes three-dimensional (3D) reconstruction from video captures. Using this technology, disuse-evoked alterations of walking performances were observed in rodents exposed to a simulated microgravity environment by unloading their hindlimb by their tail. After 2 weeks of unloading, the rats walked on a treadmill, and their gait motions were captured with four charge-coupled device (CCD) cameras. 3D motion profiles were reconstructed and compared to those of control subjects using the image processing software. The reconstructed outcome measures successfully portrayed distinct aspects of distorted gait motion: hyperextension of the knee and ankle joints and higher position of the hip joints during the stance phase. Motion analysis is useful for several reasons. First, it enables quantitative behavioral evaluations instead of subjective observations (e.g., pass/fail in certain tasks). Second, multiple parameters can be extracted to fit specific needs once the fundamental datasets are obtained. Despite hurdles for broader application, the disadvantages of this method, including labor intensity and cost, may be alleviated by determining comprehensive measurements and experimental procedures.

The present protocol describes three-dimensional motion tracking/evaluation to depict gait motion alteration of rats after exposure to a simulated disuse environment.

It is well known that disuse affects neural systems and that joint motions become altered; however, which outcomes properly exhibit these characteristics ...

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

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