The authors thank the participants and their families for their time and effort expended for participation in the study. As well, the authors acknowledge Dr. Yichuan Liu and Dr. Hasan Ayaz for their assistance with the timing calculation of the HR monitoring and Dr. Paul Diefenbach for development of the KOLLECT Active Video Gaming software. Funding for this work was provided by Coulter Foundation Grants #00006143 (O&#8217;Neil; Diefenbach, PIs) and #00008819 (O&#8217;Neil; Diefenbach, PIs).

&#160;&#160;Institutional Review Board approval was obtained. All youth provided written assent and parents provided consent prior to participation.
1.&#160;AVG data collection sessions
<ol>
	<li>The AVG game session
	<ol>
		<li>In this study, have youth with CP participate in an AVG session which is comprised of three 20 min games. See Table 5 for Youth Demographics. It was expected that a total of 30 games would be played; however, 29 games were completed because one subject only played 2 games in his AVG session.</li>
		<li>Have the subjects wear a HR monitor throughout the session to record HR and ECG responses.</li>
		<li>In the AVG session, have youth play each AVG while seated on a bench with feet flat on the floor and knees and hips flexed to 90 degrees (90/90 sitting) for postural support and stability.</li>
		<li>Use the following three gaming conditions for collection objects: 1) hand icons only; 2) feet icons only; and 3) both hand and feet icons. Use a counterbalanced order between subjects. Choose these three conditions to determine which is more effective in promoting physical activity and fitness and not too demanding to cause early, undue fatigue. 
		NOTE: Each game was designed using the phases of exercise prescription: warm-up, conditioning and cool-down. [Please see Table 1]. Additionally, there was a rest phase before game play began to document baseline HR and a recovery phase after game play to document time to return to baseline HR.</li>
		<li>Allow subjects a rest period between games for HR to return to baseline level.</li>
	</ol>
	</li>
	<li>Calculating HRV from ECG Data
	<ol>
		<li>Organize data into 5 min time intervals to ensure comparable data for each phase. Therefore, there were 6 phases defined for these calculations: 1) Rest; 2) Warm-up; 3) Conditioning 1 (first 5 min); 4) Conditioning 2 (second 5 min); 5) Cool-Down (5 min) and 6) Recovery. Dividing the conditioning phase into two 5 min phases allows examination of subject aerobic performance in shorter intervals to account for fatigue due to deconditioning 12 (Table 4).</li>
		<li>To properly calculate HRV measures for each segment of a subject&#39;s session, perform R-peak detection on the raw ECG signal12,13. Use the raw signal to avoid manipulations that could skew the data.</li>
		<li>To process the data, obtain the start times of each recording session and convert from &#39;datetime&#39; variables (MM/DD/YYYY HH:MM:SS.SS) to seconds. None of the sessions occurred across two days which allowed the MM/DD/YYYY portion to be ignored during these calculations. Acquire the start time of the game of interest from the timing table to locate each game session within the electrocardiogram (ECG) file; this time was converted to seconds after it had been extracted from the timing file. The timing file contained start times for each phase of the game as well as the end of the recovery period (Table 2).</li>
		<li>Calculate the rest period as the 5 min prior to the game start and the Recovery Phase as the 5 min after the end of the Cooldown Phase. Once these times were obtained, obtain the location (S) of the game phase of interest within the ECG file through the following equation: 
		<img alt="Equation 1" src="/files/ftp_upload/59230/59230eq1.jpg" />&#160; (1) 
		where Phase is set to either Rest, Warmup, Conditioning 1, Conditioning 2, Cooldown, or Recovery; the time was divided by 1/Frequency to account for the ECG sample rate. The HR monitor had a sampling rate of 250 Hz and therefore contained a measure every 4 ms.
		<ol>
			<li>Change this number by altering the sampling rate with the first prompt from the Peak_Detection.m program to account for the use of alternate recording devices. Choose which 5 min segment to work with while running the peak detection program. This was done via a prompt to the user. Set the end time to 5 min after the start time and take the frequency of the recording device into consideration.</li>
		</ol>
		</li>
		<li>Once the 5 min section had been chosen, calculate a threshold for peak-detection based upon the average and standard deviation of the waveform.
		<ol>
			<li>Set the threshold as&#160;<img alt="Equation 2" src="/files/ftp_upload/59230/59230eq2.jpg" />but this can be increased in the program if the data are uniform to reduce false-positive detection from T peaks which are higher than their corresponding R peaks. Examples of these false positives can be seen in Figure 1.</li>
			<li>Along with a minimum height for the R peak, assign a minimum distance between peaks to minimize the detection of incorrect peaks around the desired R. Set this value to 75 which corresponded to 0.3 s between peaks or 200 beats per min (bpm) (this value changes with frequency). The value of 200 bpm is higher than any HR achieved by the subjects in this study and can be changed based on the population being studied.</li>
		</ol>
		</li>
		<li>Once the threshold was calculated, let the program run through the waveform and attempt to discern all the R&#39;s for RR interval and HRV calculations. Generate a preliminary plot so that the user could review it for irregularities such as those shown in Figure 1 or Figure 2.
		<ol>
			<li>Correct these irregularities manually by editing the Detection variable which contains the microvolt (&#181;V) reading of the peak in column 1 and the location in the current game session (s/0.004) in the second column. In most cases the proper R peaks can easily be found by zooming into the problem location as seen in Figure 1. Many data sessions are fairly uniform as shown in Figure 3 and will therefore only require a few corrections. Some cases, however are fairly messy and require more time to review and obtain proper R locations.</li>
			<li>If the fluctuations in the waveform make it excessively difficult to properly locate a peak, ignore small segments ~1-2 s and attribute to ectopic beats which are not used in HRV calculations12.</li>
		</ol>
		</li>
		<li>After the R&#39;s have been located, run the HRV_Measures program. Calculate RR intervals first as they are the basis of the HRV measures used in this study12.
		<ol>
			<li>Obtain a matrix of intervals and ignore any interval greater than 1.5 s (40 bpm) as it was due to the aforementioned ectopic beats being removed from the calculations. Save these RR intervals for further calculations and verification of data. Use these intervals to calculate the Root Mean Square of the Successive Differences (RMSSD) with the following equation: 
			<img alt="Equation 3" src="/files/ftp_upload/59230/59230eq3.jpg" />&#160;RMSSD = (2) 
			Where N = Number of RR Intervals (R-R)i = Interval between neighboring QRS Peaks (R-R)i+1 = Interval between subsequent set of peaks</li>
		</ol>
		</li>
		<li>Choose this variable as it has been shown to be efficacious on intervals ranging from 1 min to 24 h in length13,14,15,16,17 and can therefore be used to assess these 5 min intervals in the game phases. Along with RMSSD, obtain the Standard Deviation of NN intervals to measure changes in HR throughout the phase14,16,18.</li>
		<li>Use the RR intervals to calculate NN50, the number of intervals that differ from the previous interval by more than 50 ms12 which has also been used on intervals ranging from one min to 24 h16,17,19,20,21.
		<ol>
			<li>Calculate the NN50 variable via a simple count function that checked whether or not the difference between consecutive RR interval lengths was greater than 50 ms. Once NN50 was obtained in this manner, divide by the total number of intervals to calculate pNN50 which is the percentage of intervals that differ by more than 50 ms. This calculation allowed the measured data to be compared across subjects, games, and even sessions of varying lengths as it is a unit-less variable13,14,16,17.</li>
		</ol>
		</li>
		<li>Calculate mean RR interval length for each phase and subject as a separate HRV measure16,17,19,22,23,24. Use this measure to calculate Average HR by dividing the mean RR interval by 60 s. Both of these measures are easily comparable across game sessions to observe the trend of the subject&#39;s activity16,17,19,22,23,24.</li>
		<li>Once these measures were calculated, calculate the Low Frequency and High Frequency Power Spectral Density (PSD) for both the raw ECG of the 5-min interval and the RR interval matrix by obtaining PSD from Fast-Fourier transforms13,14,17,19,25. All these data were then stored in a table, an example of which is shown in Table 4.</li>
	</ol>
	</li>
</ol>
2. Acquire ECG Data from the Patient
<ol>
	<li>Prepare the HR monitor chest strap and Bluetooth module for application to the subject.
	<ol>
		<li>Ensure that the Bluetooth module has been fully charged (3 h) using the charge cradle.</li>
		<li>Plug the module into the data computer via the charge cradle and open the config tool. Enter a name for logging purposes.</li>
		<li>Select the HR device, click the Time tab and select Set Date/Time to sync the module to the correct time and date. The device can now be removed from the charge cradle.</li>
		<li>Moisten the conductive areas (beige) on the HR monitor chest strap by placing a hand in water and rubbing the conductive areas.</li>
		<li>Place the HR monitor Bluetooth module into the chest strap with the conductive surfaces of the module lined up with those of the chest strap: it will click into place.</li>
		<li>Press and hold the button on the module until the lights flash. The module is now on and recording.</li>
		<li>Apply the HR monitor chest strap (with Bluetooth module) to the player with the module aligned with the left mid-axillary line and the strap just under the pectoral muscles. Once properly positioned, tighten the device so that it will not move during the session but is not uncomfortable for the player.</li>
	</ol>
	</li>
	<li>Acquire a signal and view the live feed.
	<ol>
		<li>Plug the connector into the USB port of computer that will be used to view the data.</li>
		<li>Open the Live View program and enter Setup Mode by clicking the icon with the wrench and screwdriver.</li>
		<li>Choose a player from the list if appropriate or add a new subject with the New button in the bottom left corner of the screen.</li>
		<li>Enter Subject information as desired for identification purposes (name, age, gender, height, weight).</li>
		<li>Click on the Hardware tab and select the current subject.</li>
		<li>Click Assign in the bottom of the tab and select the current device (listed as 01 if no other devices are present). Then click assign in the pop-up box.</li>
		<li>Click on the Team tab. Highlight the subject and then click the right arrow button to place the player on Team A.</li>
		<li>Click on the Deployment tab and then move the newly created team to the first tab.</li>
		<li>Open the Live Mode tab by clicking the blue Wi-Fi symbol in the top left corner.</li>
		<li>Use the Live Mode tab to monitor HR, respiratory rate, and posture of the subject in real time. 
		NOTE: Signal strength, battery power, and confidence of the measures can also be viewed.</li>
		<li>Record accurate timing (MM/DD/YYYY HH:MM:SS) of the start and end of each session and phase for processing.</li>
	</ol>
	</li>
	<li>Download the ECG Data from the HR monitor.
	<ol>
		<li>Remove the strap from the player at the end of the session and remove the Bluetooth Module from the chest strap.</li>
		<li>Place the module in the charge cradle and plug it into a computer with the software program installed.</li>
		<li>Open the log.</li>
		<li>Select the device from the dropdown menu. All sessions currently on the device are displayed with dates and times.</li>
		<li>Uncheck the box that says Use Default Save Location and chose a new save location.</li>
		<li>Click Save. A progress bar will then appear. Saving may take up to an hour depending on the length of the session.</li>
		<li>Rename the date, once it has been saved.</li>
	</ol>
	</li>
</ol>
3. Data Analysis and Calculation of Heart Rate Variability Measures
<ol>
	<li>Prepare files for processing .
	<ol>
		<li>Name ECG files as &#39;KOLLECT_Subject#_AVG4&#39; (e.g., KOLLECT_01_AVG4.csv&#39;).</li>
		<li>Generate a timing table in comma separated variable (.csv) format to draw timing data from during data processing. See Table 1 for an example of the correct format.</li>
		<li>Import the Date-time data from the .csv file and right click on the name of the newly created variable and change it to &#39;Timing.mat&#39;.</li>
	</ol>
	</li>
	<li>Preliminary R peak detection.
	<ol>
		<li>Open and run Peak _ Detection . m.</li>
		<li>Enter the frequency of ECG recording device when prompted by the program.</li>
		<li>Enter the player number for the data to be analyzed when prompted. 
		NOTE: Some players did not complete active video game 4 (AVG4) and therefore only players 1-10 are used for this study. Other numbers will provide an error message.</li>
		<li>Enter the number of the game to be analyzed (1, 2, or 3) when prompted.</li>
		<li>Enter the phase to be analyzed (Rest, Warmup (WU), Conditioning (Con), Rest, or Recovery).
		<ol>
			<li>Enter an offset in minutes if desired, or enter 0 for no offset.</li>
		</ol>
		</li>
		<li>Select the magnifying tool and select an area of the plot that is output to create a window with a width of approximately 2,000 (s/0.004) and a height that will show the full waveform as shown in Figure 3. Zoom in or out if the window is not easily inspected visually.</li>
		<li>Visually inspect the graph to evaluate if the peaks detected are correctly labeled. See Figure 1 for example of incorrectly detected and missed peaks caused by irregular ECG data (Figure 2).</li>
	</ol>
	</li>
	<li>Peak Correction
	<ol>
		<li>Correct the incorrectly detected or missing peaks by locating the Detection variable and double clicking in the workspace.</li>
		<li>Utilize the Data Cursor tool on the plot of the ECG waveform to obtain the x and y coordinates of the incorrect peak; X (time*frequency) is the first column in Detection.mat and Y (Voltage) is the second column (Figure 3).
		<ol>
			<li>Right click the text box that appears and click Select Cursor Update Function.</li>
			<li>Select TooltipUpdate.m from the folder containing the files used for this analysis. This will allow the tooltip to display more exact values.</li>
		</ol>
		</li>
		<li>If the point is a false positive, remove it from the array by clicking on its row in the Detection.mat variable and pressing Control and the Minus key. An example of false positive detection can be seen in Figure 3.</li>
		<li>Edit incorrectly marked peaks that are adjacent to unmarked peaks, as shown by the two T peaks marked as R in Figure 1, by changing their values to match that of the unmarked peak.</li>
		<li>Obtain the value of the missed peak can be obtained with the Data Cursor tool.</li>
		<li>Add additional rows to Detection. mat using control and the plus key for peaks missed due to low voltage levels.</li>
		<li>Enter the values in numerical order to avoid negative values during the calculation process (i.e., add the peak located at 11000 between the peaks at 10908 and 11167) (Figure 5).</li>
		<li>Ensure that values are entered correctly before continuing through the full session as numbers are occasionally clipped off when entered.</li>
		<li>Repeat step 2.3 until all peaks have been checked and/or corrected. 
		NOTE: Some files have limited variability in waveform amplitude and are quicker to check, as seen in Figure 4 while others are more variable and may require closer zoom to accurately locate peaks during visual inspection.</li>
	</ol>
	</li>
	<li>Obtain HRV measure calculations.
	<ol>
		<li>Save the original plot generated from Peak_Detection.m for later reference.</li>
		<li>Run HRV_Measures.m to generate the correctly labeled plot. A sample of corrected data is shown in Figure 6.
		<ol>
			<li>Change the plot title by using Insert | Title on the plot window and changing it to the desired title.</li>
			<li>Check the window for output, the program will notify the user of the location incorrectly entered data if any exists.</li>
		</ol>
		</li>
		<li>Save the variable named interval.</li>
		<li>Open the variable entitled HRV from the Workspace window to view Mean RR (ms), Average HR (bpm), RMSSD (ms), SDNN (ms), NN50 (count), pNN50 (%), low frequency (LF)/ high frequency (HF) (ECG), LF/HF RR, Low Frequency Power RR, and High Frequency Power (RR)). Save the h values of these variable to a table such as the one shown in Table 4.</li>
		<li>Repeat Sections 3.2 - 3.4 for all other segments, sessions, and subjects that need analysis.</li>
	</ol>
	</li>
</ol>

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Doctoral thesis. , (2009).</li><li>Murai, K., Hayashi, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+mental+workload+for+ship+handling+using+physiological+indices.">Evaluation of mental workload for ship handling using physiological indices.</a> , 604-608 (2009).</li><li>Taelman, J., Vandeput, S., Spaepen, A., Van Huffel, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+mental+stress+on+heart+rate+and+heart+rate+variability.">Influence of mental stress on heart rate and heart rate variability.</a> Heart. 29 (1), 1366-1369 (2009).</li><li>Durantin, G., Gagnon, J. F., Tremblay, S., Dehais, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+near+infrared+spectroscopy+and+heart+rate+variability+to+detect+mental+overload.">Using near infrared spectroscopy and heart rate variability to detect mental overload.</a> Behavioural Brain Research. 259, 16-23 (2014).</li><li>Buchheit, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Monitoring+training+status+with+HR+measures:+Do+all+roads+lead+to+Rome?.">Monitoring training status with HR measures: Do all roads lead to Rome?.</a> Frontiers in Physiology. 5, (2014).</li><li>Achten, J., Jeukendrup, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heart+rate+monitoring:+Applications+and+limitations.">Heart rate monitoring: Applications and limitations.</a> Sports Medicine. 33 (8), 517-538 (2012).</li><li>Amichai, T., Katz-Leurer, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heart+rate+variability+with+cerebral+palsy:+Review+of+literature+and+meta-analysis.">Heart rate variability with cerebral palsy: Review of literature and meta-analysis.</a> NeuroRehabilitation. 35, 113-122 (2014).</li><li>Billman, G., Haikuri, H., Sacha, J., Trimmel, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+introduction+to+heart+rate+variability:+Methodological+considerations+and+clinical+applications.">An introduction to heart rate variability: Methodological considerations and clinical applications.</a> Frontiers in Physiology. 6, (2015).</li><li>Beffara, B., Bret, A., Vermeulen, N., Mermillod, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Resting+high+frequency+heart+rate+variability+selectively+predicts+cooperative+behavior.">Resting high frequency heart rate variability selectively predicts cooperative behavior.</a> Physiology &amp; Behavior. 164, 417-428 (2016).</li><li>Fogt, D., Cooper, P., Freeman, C., Kalns, J., Cooke, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heart+rate+variability+to+assess+combat+readiness.">Heart rate variability to assess combat readiness.</a> Military Medicine. 174, 491-495 (2009).</li><li>Kerppers, I. L., Arisawa, E. A. L., Oliveira, L. V. F., Sarmpaio, L. M. M., Oliverira, C. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heart+rate+variability+in+individual+with+cerebral+palsy.">Heart rate variability in individual with cerebral palsy.</a> Archives of Medical Science. 5, 45-50 (2009).</li><li>Giggins, O. M., Persson, U. M., Caulfield, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biofeedback+in+Rehabilitation.">Biofeedback in Rehabilitation.</a> Journal of Neuroengineering and Rehabilitation. 10, (2013).</li><li>Shaffer, F., Ginsberg, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+overview+of+heart+rate+variability+metrics+and+norms.">An overview of heart rate variability metrics and norms.</a> Frontiers in Public Health. 5, 258 (2017).</li><li>Shaffer, F., McCarty, R., Zeir, C. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+healthy+heart+is+not+a+metronome:+an+integrative+review+of+the+heart&#8217;s+anatomy+and+heart+rate+variability.">A healthy heart is not a metronome: an integrative review of the heart&#8217;s anatomy and heart rate variability.</a> Frontiers in Psychology. 5, 1040 (2014).</li></ol>

At this time, the authors (CL and PAS) have nothing to disclose. Dr. O&#39;Neil is a co-founder of enAbleGames, LLC and Kollect is one of the games offered by this web-based company. enAbleGames is in game development phase and is not a public company at this time (www.enAbleGames.com).&#160;

Ten youth with CP participated in this study (mean + SD) [ age (yrs) = 15.53 &#177; 3.57 ; height (cm) 154.8 &#177; 12.6; weight (kg) 50.69 &#177; 11.1; body mass index (BMI) 50.46 &#177; 29.2; mHR 9 bpm) = 186.8 &#177; 12.4]. Please see Table 5 for patient demographics.
There are some considerations for use of HR monitors and the associated measures of HR and HRV which relate to modifications and troubleshooting. Two issues that are apparent, regardless of the technology empl...

Cerebral palsy (CP) is the most common physical disability of childhood1. CP is caused by a neurologic insult to the developing brain and is associated with motor impairments such as muscle weakness, spasticity, deconditioning, and decreased motor control and balance2,3. CP is a non-progressive condition but with age, children become less physically active and more sedentary compared to their peers with typical development (TD) mostly because of the increased demands of growth on their compromised neuromuscular and musculoskeletal systems4.
Youth with CP usually receive physical therapy (PT) services to improve functional mobility and promote physical activity and fitness (e.g. aerobic and muscular endurance)2. Oftentimes, there is limited access to PT services and community resources to achieve and sustain these PT goals5,6.Active video games (AVGs) may be a feasible strategy in activity-based PT interventions in clinic, home or community settings7,8. Commercial AVGs have limited flexibility to adapt game play and meet the specific needs and PT goals for youth with CP9. However, customized AVGs provide flexible gaming parameters to challenge youth with CP while promoting physical activity and fitness10.
Our team has developed a customized AVG (called KOLLECT) to examine youth exercise responses (e.g., physical activity and aerobic fitness). The game uses a motion sensor to track youth motion during game play. The goal of the game is to &#39;collect&#39; as many objects as possible for a high score and to avoid the hazards to avoid losing points. Objects may be collected with hand and/or feet icons as determined by the therapist in the flexible game parameters.
Designing activity-based PT interventions that dose physical activity intensity to promote aerobic fitness is critical for youth with CP11. Custom AVGs may be an effective strategy to dose intensity and engage youth in physical activity to promote fitness10. Heart rate (HR) monitors are often used in clinical PT practice to determine aerobic performance and activity intensity. Therefore, HR monitors will help determine feasibility of AVGs in dosing physical activity intensity to promote aerobic fitness9.&#160;ECG data generated from a HR monitor can be used to calculate heart rate variability (HRV). Analytic methods were used to generate HRV from ECG data to examine aerobic workload. Recent applications of HRV indicate that short-term measurements (5 min bouts) are appropriate and that HRV biofeedback may help improve symptoms and the quality of life in a variety of health conditions32,33,34. The application of short-term HRV measures is an appropriate means of assessing cardiovascular function during AVG sessions. Given that HRV is derived from the R-R interval of an ECG, we used selected time-domain and frequency-domain measures. Time-domain measure of HRV quantify the amount of variablility in the interbeat intervals which represents the time between successive heartbeats. We used the AVNN (average NN interval), RMSSD (root mean square of successive differences), SDNN (standard deviation of NN interval), NN50 (number of NN intervals &#62;50 ms) and PNN50 (percentage of NN intervals). Frequency domain measures estimate the distributionof absolute or relative power into possibly four frequency bands, we specifically addressed on two bands, low frequency (LF) power and high frequency (HF) power along with the LF/HF ratio. Although HR is a well-accepted clinical measure, HRV may be useful because it provides information about autonomic system function, recovery, adaptation, and provides an estimate of aerobic workload during an AVG session28.
The purpose of this study was to examine the feasibility of using AVG strategies to promote physical activity and fitness. A second purpose was to present the AVG data collection protocol and the methodology to calculate HRV from ECG data obtained via a HR monitor. These measures and this protocol may prove relevant to clinicians to monitor and dose PT intervention sessions.

<table border="0" cellpadding="0" cellspacing="0" style="width:1067px;" width="1067">
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		<tr height="21">
			<td height="21" style="height:21px;width:352px;">BioHarness Bluetooth Module (Electronics sensor)&#160;</td>
			<td style="width:81px;">Zephyr</td>
			<td align="right" style="width:159px;">9800.0189</td>
			<td style="width:475px;">Detects Heart Rate, Resiration Rate, Posture, and Skin Temperature.</td>
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		<tr height="21">
			<td height="21" style="height:21px;width:352px;">BioHarness Chest Strap</td>
			<td style="width:81px;">Zephyr</td>
			<td style="width:159px;">9600.0189, 9600.0190</td>
			<td>Sizes Small XS-M, Large M-XL</td>
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		<tr height="21">
			<td height="21" style="height:21px;width:352px;">BioHarness Charge Cradle &#38; USB Cable</td>
			<td style="width:81px;">Zephyr</td>
			<td align="right" style="width:159px;">9600.0257</td>
			<td>Used to Transfer Data from the Module to a Computer for Analysis.</td>
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		<tr height="21">
			<td height="21" style="height:21px;width:352px;">BioHarness Echo Gateway</td>
			<td style="width:81px;">Zephyr</td>
			<td align="right" style="width:159px;">9600.0254</td>
			<td>Allows for Realtime Viewing of Subject&#39;s Heart Rate.</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:352px;">MATLAB R2016a</td>
			<td style="width:81px;">Mathworks</td>
			<td style="width:159px;">1.7.0_.60</td>
			<td>Used for All Programming.</td>
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calculating heart rate variability from ecg data from youth with cerebral palsy during active video game sessions

The aim of this study was to generate a method for calculating heart rate variability (HRV) from electrocardiogram (ECG) waveforms. The waveforms were recorded by a HR monitor that participants (youth with cerebral palsy (CP)) wore during active video game (AVG) sessions. The AVG sessions were designed to promote physical activity and fitness (aerobic performance) in participants. The goal was to evaluate the feasibility of AVGs as a physical therapy (PT) intervention strategy. The maximum HR (mHR) was determined for each participant and the Target Heart Rate Zone (THRZ) was calculated for each of three exercise phases in the 20 min AVG session: (warm-up at 40-60% mHR, conditioning at 60-80% mHR, and cool down at 40-60% mHR). Each participant played three 20 min games during the AVG session. All games were played while sitting on a bench because many youth with CP cannot stand for extended periods of time. Each game condition differed with participants using hand icons only, hand and feet icons together or feet icons only to collect objects. The objective of the game (called KOLLECT) is to collect objects to gain points and avoid hazards to not lose points. Hazards were used in the warm-up and cool down phases only to promote slower, controlled movement to maintain HR in the target heart rate zone (THRZ). There were no hazards in the conditioning phase to promote higher levels and more intense physical activity. Analytic methods were used to generate HRV (selected time-domain and frequency-domain measures) from ECG data to examine aerobic workload. Recent applications of HRV indicate that short-term measurements (5 min bouts) are appropriate and that HRV biofeedback may help improve symptoms and the quality of life in a variety of health conditions. Although HR is a well-accepted clinical measure to examine aerobic performance and intensity in PT interventions, HRV may provide information of the autonomic system functions, recovery and adaptation during AVG sessions.

This method provides data for use in analyzing the effect that a newly developed method has on the subject&#39;s Heart Rate Variability (HRV). It does this by locating the R portion of the QRS waveform of a subject&#39;s ECG data, as shown in Figure 6, and by calculating various HRV values from it. If the HR monitor is making proper contact with the subject, the data will be uniform, substantially reducing the need for corrections (as seen in <strong class="x...

Watch this Scientific Journal Video about Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions at JoVE.com

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

This protocol describes a method for calculating Heart Rate Variability (HRV) from electrocardiogram (ECG) waveforms. Waveforms from continuous heart rate (HR) recordings during active video game (AVG) sessions were used to measure the aerobic performance of youth with cerebral palsy (CP).

The aim of this study was to generate a method for calculating heart rate variability (HRV) from electrocardiogram (ECG) waveforms. The waveforms were ...

calculating-heart-rate-variability-from-ecg-data-from-youth-with

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19.7K Views.  Drexel University. This protocol describes a method for calculating Heart Rate Variability (HRV) from electrocardiogram (ECG) waveforms. Waveforms from continuous heart rate (HR) recordings during active video game (AVG) sessions were used to measure the aerobic performance of youth with cerebral palsy (CP).

Video: Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

Method for Simultaneous fMRI/EEG Data Collection during a Focused Attention Suggestion for Differential Thermal Sensation

In the present work, we demonstrate a method for concurrent collection of EEG/fMRI data. In our setup, EEG data are collected using a high-density 256-channel sensor net. The EEG amplifier itself is contained in a field isolation containment system (FICS), and MRI clock signals are synchronized with EEG data collection for subsequent MR artifact characterization and removal. We demonstrate this method first for resting state data collection. Thereafter, we demonstrate a protocol for EEG/fMRI data recording, while subjects listen to a tape asking them to visualize that their left hand is immersed in a cold-water bath and referred to, here, as the cold glove paradigm. Thermal differentials between each hand are measured throughout EEG/fMRI data collection using an MR compatible temperature sensor that we developed for this purpose. We collect cold glove EEG/fMRI data along with simultaneous differential hand temperature measurements both before and after hypnotic induction. Between pre and post sessions, single modality EEG data are collected during the hypnotic induction and depth assessment process. Our representative results demonstrate that significant changes in the EEG power spectrum can be measured during hypnotic induction, and that hand temperature changes during the cold glove paradigm can be detected rapidly using our MR compatible differential thermometry device.

We present a protocol for concurrent collection of EEG/fMRI data, and synchronized MR clock signal recording. We demonstrate this method using a unique paradigm whereby subjects receive &#x2018;cold glove&#x2019; instructions during scanning, and EEG/fMRI data are recorded along with hand temperature measurements both before and after hypnotic induction.

In the present work, we demonstrate a method for concurrent collection of EEG/fMRI data. In our setup, EEG data are collected using a high-density ...

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Simultaneous EEG-fMRI allows the excellent temporal resolution of EEG to be combined with the high spatial accuracy of fMRI. The data from these two modalities can be combined in a number of ways, but all rely on the acquisition of high quality EEG and fMRI data. EEG data acquired during simultaneous fMRI are affected by several artifacts, including the gradient artefact (due to the changing magnetic field gradients required for fMRI), the pulse artefact (linked to the cardiac cycle) and movement artifacts (resulting from movements in the strong magnetic field of the scanner, and muscle activity). Post-processing methods for successfully correcting the gradient and pulse artifacts require a number of criteria to be satisfied during data acquisition. Minimizing head motion during EEG-fMRI is also imperative for limiting the generation of artifacts.
Interactions between the radio frequency (RF) pulses required for MRI and the EEG hardware may occur and can cause heating. This is only a significant risk if safety guidelines are not satisfied. Hardware design and set-up, as well as careful selection of which MR sequences are run with the EEG hardware present must therefore be considered.
The above issues highlight the importance of the choice of the experimental protocol employed when performing a simultaneous EEG-fMRI experiment. Based on previous research we describe an optimal experimental set-up. This provides high quality EEG data during simultaneous fMRI when using commercial EEG and fMRI systems, with safety risks to the subject minimized. We demonstrate this set-up in an EEG-fMRI experiment using a simple visual stimulus. However, much more complex stimuli can be used. Here we show the EEG-fMRI set-up using a Brain Products GmbH (Gilching, Germany) MRplus, 32 channel EEG system in conjunction with a Philips Achieva (Best, Netherlands) 3T MR scanner, although many of the techniques are transferable to other systems.

Simultaneous electroencephalography (EEG) and functional Magnetic Resonance imaging (fMRI) is a powerful neuroimaging tool. However, the inside of an MRI scanner forms a difficult environment for EEG data recording and safety must be considered whenever operating EEG equipment inside a scanner. Here, we present an optimised EEG-fMRI data acquisition protocol.

Simultaneous  EEG-fMRI allows the excellent temporal resolution of EEG to be combined with the  high spatial accuracy of fMRI. The data  from these two ...

Event-related Potentials During Target-response Tasks to Study Cognitive Processes of Upper Limb Use in Children with Unilateral Cerebral Palsy

Unilateral Cerebral Palsy (CP) is a neurodevelopmental disorder that is a very common cause of disability in childhood. It is characterized by unilateral motor impairments that are frequently dominated in the upper limb. In addition to a reduced movement capacity of the affected upper limb, several children with unilateral CP show a reduced awareness of the remaining movement capacity of that limb. This phenomenon of disregarding the preserved capacity of the affected upper limb is regularly referred to as Developmental Disregard (DD). Different theories have been postulated to explain DD, each suggesting slightly different guidelines for therapy. Still, cognitive processes that might additionally contribute to DD in children with unilateral CP have never been directly studied. The current protocol was developed to study cognitive aspects involved in upper limb control in children with unilateral CP with and without DD. This was done by recording event-related potentials (ERPs) extracted from the ongoing EEG during target-response tasks asking for a hand-movement response. ERPs consist of several components, each of them associated with a well-defined cognitive process (e.g., the N1 with early attention processes, the N2 with cognitive control and the P3 with cognitive load and mental effort). Due to its excellent temporal resolution, the ERP technique enables to study several covert cognitive processes preceding overt motor responses and thus allows insight into the cognitive processes that might contribute to the phenomenon of DD. Using this protocol adds a new level of explanation to existing behavioral studies and opens new avenues to the broader implementation of research on cognitive aspects of developmental movement restrictions in children.

Several children with unilateral Cerebral Palsy seem to disregard the preserved capacity of their affected upper limb. This Developmental Disregard is extensively described in the literature but the involved cognitive processes have not been studied. To study underlying cognitive factors of upper limb control, an event-related potential protocol was developed.

Unilateral Cerebral Palsy (CP) is a neurodevelopmental disorder that is a very common cause of disability in childhood. It is characterized by unilateral ...

Reversible Cooling-induced Deactivations to Study Cortical Contributions to Obstacle Memory in the Walking Cat

On complex, naturalistic terrain, sensory information about an environmental obstacle can be used to rapidly adjust locomotor movements for avoidance. For example, in the cat, visual information about an impending obstacle can modulate stepping for avoidance. Locomotor adaptation can also occur independent of vision, as sudden tactile inputs to the leg by an expected obstacle can modify the stepping of all four legs for avoidance. Such complex locomotor coordination involves supraspinal structures, such as the parietal cortex. This protocol describes the use of reversible, cooling-induced cortical deactivation to assess parietal cortex contributions to memory-guided obstacle locomotion in the cat. Small cooling loops, known as cryoloops, are specially shaped to deactivate discrete regions of interest to assess their contributions to an overt behavior. Such methods have been used to elucidate the role of parietal area 5 in memory-guided obstacle avoidance in the cat.

Complex locomotion in naturalistic environments requiring careful coordination of the limbs involves regions of the parietal cortex. The following protocol describes the use of reversible cooling-induced deactivation to demonstrate the role of parietal area 5 in memory-guided obstacle avoidance in the walking cat.

On complex, naturalistic terrain, sensory information about an environmental obstacle can be used to rapidly adjust locomotor movements for avoidance. For ...

Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation

Stroke rehabilitation requires repetitive, intensive, goal-oriented therapy. Virtual reality (VR) has the potential to satisfy these requirements. Game-based therapy can promote patients&#39; engagement in rehabilitation therapy as a more interesting and a motivating tool. Mobile devices such as smartphones and tablet PCs can provide personalized home-based therapy with interactive communication between patients and clinicians. In this study, a mobile VR upper extremity rehabilitation program using game applications was developed. The findings from the study show that the mobile game-based VR program effectively promotes upper extremity recovery in patients with stroke. In addition, patients completed two weeks of treatment using the program without adverse effects and were generally satisfied with the program. This mobile game-based VR upper extremity rehabilitation program can substitute for some parts of the conventional therapy that are delivered one-on-one by an occupational therapist. This time-efficient, easy to implement, and clinically effective program would be a good candidate tool for tele-rehabilitation for upper extremity recovery in patients with stroke. Patients and therapists can collaborate remotely through these e-health rehabilitation programs while reducing economic and social costs.

Here, we present a protocol to develop and apply a mobile game-based virtual reality program for the recovery of upper limb dysfunction in patients with stroke. The present study shows that the mobile program is feasible and effectively promotes upper limb recovery in stroke patients.

Stroke rehabilitation requires repetitive, intensive, goal-oriented therapy. Virtual reality (VR) has the potential to satisfy these requirements. ...

The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

The memory game paradigm is a behavioral procedure to explore the relationship between language, spatial memory, and object knowledge. Using two different versions of the paradigm, spatial language use and memory for object location are tested under different, experimentally manipulated conditions. This allows us to tease apart proposed models explaining the influence of object knowledge on spatial language (e.g., spatial demonstratives), and spatial memory, as well as understanding the parameters that affect demonstrative choice and spatial memory more broadly. Key to the development of the method was the need to collect data on language use (e.g., spatial demonstratives: &#34;this/that&#34;) and spatial memory data under strictly controlled conditions, while retaining a degree of ecological validity. The language version (section 3.1) of the memory game tests how conditions affect language use. Participants refer verbally to objects placed at different locations (e.g., using spatial demonstratives: &#34;this/that red circle&#34;). Different parameters can be experimentally manipulated: the distance from the participant, the position of a conspecific, and for example whether the participant owns, knows, or sees the object while referring to it. The same parameters can be manipulated in the memory version of the memory game (section 3.2). This version tests the effects of the different conditions on object-location memory. Following object placement, participants get 10 seconds to memorize the object&#39;s location. After the object and location cues are removed, participants verbally direct the experimenter to move a stick to indicate where the object was. The difference between the memorized and the actual location shows the direction and strength of the memory error, allowing comparisons between the influences of the respective parameters.

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

We present a protocol to explore the relationship between spatial language production, spatial memory, and object knowledge. The procedure allows experimental manipulation of, and control over, conditions of object knowledge, language at instruction, and physical location, thus teasing apart cognitive and linguistic models describing interactions between these variables.

The memory game paradigm is a behavioral procedure to explore the relationship between language, spatial memory, and object knowledge. Using two different ...

Measuring Active and Passive Tameness Separately in Mice

Domesticated animals such as dogs and laboratory mice show a high level of tameness, which is important for humans to handle them easily. Tameness has two behavioral components: a reluctance to avoid humans (passive tameness) and a motivation to approach humans (active tameness). To quantify these components in mice, we previously developed behavioral tests for active tameness, passive tameness, and the willingness to stay on a human hand, each designed to be completed within 3 min. The data obtained were used for selective breeding, with a large number of mice analyzed per generation. The active tameness test measures the movement of the mouse toward a human hand and the contact it engages in. The passive tameness test measures the duration of time that a mouse tolerates human touch. In the stay-on-hand test, a mouse is placed on a human hand and touched slowly using the thumb of that hand; the duration of time that the animal remains on the hand is measured. Here, we describe the test set-up and apparatus, explain the procedures, and discuss the appropriate data analysis. Finally, we explain how to interpret the results.

Tameness in animals includes a reduction of avoidance responses to humans (passive tameness) and an increase in active approaches to humans (active tameness). Here, we describe detailed protocols for three behavioral tests (active tameness, passive tameness, and stay-on-hand tests) to separately measure the active and passive tameness of mice.

Domesticated animals such as dogs and laboratory mice show a high level of tameness, which is important for humans to handle them easily. Tameness has two ...

Elevated Plus Maze Test Combined with Video Tracking Software to Investigate the Anxiolytic Effect of Exogenous Ketogenic Supplements

The overall goal of this study is to describe the methodology of the elevated plus maze (EPM) test in combination with a video tracking software. The purpose of the method is to document the effect of various potential anxiolytic treatments on laboratory rodent models. The EPM test is based on the rodents&#39; proclivity toward protected, enclosed dark spaces and unconditioned fear of open spaces and heights, and their innate intense motivation to explore novel environments. The EPM test is a widely used behavioral test for investigating the anxiolytic or anxiogenic responses of rodents given drugs that are known to affect behavior. Observation demonstrating a decreased proportion of time spent on closed arms, an increased proportion of time spent on open arms, a reduced number of entries to closed arms, and an elevated number of entries to open arms measured by the EPM test may reflect reduced anxiety levels. Using this method, the effect of exogenous ketone supplements on anxiety-related behavior is tested in Sprague Dawley (SPD) rats. Exogenous ketone supplements are chronically fed to the rats for 83 days or subchronically and acutely orally gavaged, daily for 7 days, before conducting the EPM test. Behavioral data collection is performed using the SMART video tracking system by a blinded observer at the end of the treatments. The main findings indicate that the EPM test is an effective method to detect the ketone supplement-induced anxiolytic effect and can be considered a sensitive measure to assess changes in anxiety behavior associated with drug- or metabolic-based therapies.

Here, we present a protocol to investigate changes in the anxiety level of rodent animal models. The elevated plus maze (EPM) test, used together with a video tracking software, provides a reliable method to document the effect of various potential anxiolytic treatments in preclinical laboratory scenarios.

The overall goal of this study is to describe the methodology of the elevated plus maze (EPM) test in combination with a video tracking software. The ...

Modified Blood Collection from Tail Veins of Non-anesthetized Mice with a Vacuum Blood Collection System and Eyeglass Magnifier

Blood sample collection is the basis of experimental animal research. It is of importance to obtain adequate blood samples for various research purposes. The tail veins of mice are small, and it is sometimes difficult to obtain the required blood volume by conventional puncture methods. This study investigates the superiority of repeated blood sample collection from tail veins of mice through use of a vacuum blood collection system and eyeglass magnifier (experimental group) compared to conventional blood sampling methods (conventional group), performed by beginners and experts, respectively. With the help of an eyeglass magnifier, a butterfly needle tip is inserted into the tail vein of each mouse in the experimental group. When the vein is penetrated successfully, a blood sample is collected in the vacuum collection tube by insertion of the rubber end of a butterfly needle into the vacuum blood collection tube. The plunger is then used to collect blood without the help of the eyeglass magnifier in the conventional group. Success rates of blood sample collection by the beginners and experts were shown to be 70% vs. 100% (p &#60; 0.01) in the experimental group and 35% vs. 85% (p &#60; 0.01) in the conventional group. For both beginners and experts, puncture times required for obtaining required blood sample were significantly lower in the experimental group compared to the conventional group (2.40 &#177; 0.75 vs. 2.90 &#177; 0.31, p &#60; 0.05; 1.15 &#177; 0.37 vs. 1.55 &#177; 0.76, p &#60; 0.05). In conclusion, the presented blood sampling technique is feasible and easy to practice and enables frequent sampling of adequate blood volumes from non-anesthetized mice.&#160;

This study reports blood sampling from tail vein in mice using a vacuum extraction tube system with eyeglass magnifier. Our method is easy to practice and could be used for repeat blood sampling in mice.

Blood sample collection is the basis of experimental animal research. It is of importance to obtain adequate blood samples for various research purposes. ...

Applying Incongruent Visual-Tactile Stimuli during Object Transfer with Vibro-Tactile Feedback

The application of incongruent sensory signals that involves disrupted tactile feedback is rarely explored, specifically with the presence of vibrotactile feedback (VTF). This protocol aims to test the effect of VTF on the response to incongruent visual-tactile stimuli. The tactile feedback is acquired by grasping a block and moving it across a partition. The visual feedback is a real-time virtual presentation of the moving block, acquired using a motion capture system. The congruent feedback is the reliable presentation of the movement of the block, so that the subject feels that the block is grasped and see it move along with the path of the hand. The incongruent feedback appears as the movement of the block diverts from the actual movement path, so that it seems to drop from the hand when it is actually still held by the subject, thereby contradicting the tactile feedback. Twenty subjects (age 30.2 &#177; 16.3) repeated 16 block transfers, while their hand was hidden. These were repeated with VTF and without VTF (total of 32 block transfers). Incongruent stimuli were presented randomly twice within the 16 repetitions in each condition (with and without VTF). Each subject was asked to rate the difficulty level of performing the task with and without the VTF. There were no statistically significant differences in the length of the hand paths and durations between transfers recorded with congruent and incongruent visual-tactile signals &#8211; with and without the VTF. The perceived difficulty level of performing the task with the VTF significantly correlated with the normalized path length of the block with VTF (r = 0.675, p = 0.002). This setup is used to quantify the additive or reductive value of VTF during motor function that involves incongruent visual-tactile stimuli. Possible applications are prosthetics design, smart sport-wear, or any other garments that incorporate VTF.

We present a protocol to apply incongruent visual-tactile stimuli during an object transfer task. Specifically, during block transfers, performed while the hand is hidden, a virtual presentation of the block shows random occurrences of false block drops. The protocol also describes adding vibrotactile feedback while performing the motor task.

The application of incongruent sensory signals that involves disrupted tactile feedback is rarely explored, specifically with the presence of vibrotactile ...