We thank Brittany Hill, Ryan Bailey, and Mike Urbin for their contributions to the accelerometry methodology and data. Funding for this project comes from NIH R01 HD068290.

This protocol was approved by the Washington University Human Research Protection Office. 
NOTE: Instructions were written specific to commercially available accelerometers and their related software for data collection (see Table of Materials).
1. Preparing the Accelerometers to Collect Data
<ol>
	<li>Connect the two accelerometers to the computer (via docking station) to charge their batteries; this will ensure recording during the entire wearing time.</li>
	<li>With the accelerometers connected to the computer, open the appropriate software to initialize them.</li>
	<li>Within the software, select &#39;Initialization&#39; to synchronize the accelerometer clocks to each other and to the local computer, and set the data collection parameters as follows.
	<ol>
		<li>Enter (or select from the calendar and clock) the start and end dates and times. Choose the start of data collection based on when the accelerometers will be placed on the participant and an end time at least 24 h later. 
		NOTE: The single day wearing period provides a good representation of daily activity in non-employed adults.12 Longer wearing periods may be more appropriate for adults or children with varying daily schedules.3,18,26</li>
		<li>Select &#39;30 Hz&#39; from the dropdown menu for &#39;Sampling Rate&#39;.</li>
		<li>Leave &#39;LED Options&#39; and &#39;Wireless Options&#39; unchecked.</li>
		<li>To extend battery life, enable &#39;Idle Sleep Mode&#39;.</li>
	</ol>
	</li>
	<li>To complete the initialization process, select &#39;Enter Subject Info&#39;.
	<ol>
		<li>Enter subject specific information for the location of the accelerometer (wrist) and the body side (&#39;Right&#39; or &#39;Left&#39;).</li>
		<li>Choose to fill in other subject specific information as desired; entry will be for identification only and will not affect the data analyses described here.</li>
		<li>When ready, select &#39;Initialize Devices&#39; to complete the process. Once initialization is confirmed, the accelerometers can be safely disconnected from the computer.</li>
	</ol>
	</li>
</ol>
2. Placement and Wearing of the Accelerometers to Collect Data from Participants
<ol>
	<li>Place one accelerometer on each wrist of the participant. 
	NOTE: The accelerometers should fit snuggly, but not too tightly to the wrist, like a large wrist watch. A variety of bands can be used depending on the size, preference, and comfort level of the participant.</li>
	<li>Instruct the participant as follows, and answer any questions the participant might have about the wearing period and activity during this time.
	<ol>
		<li>Ask the participant to do their regular activities throughout the day; the accelerometers may feel strange at first but one soon gets used to them.</li>
		<li>Instruct them that the accelerometers are waterproof and can be worn while showering or doing the dishes. Instruct them to not wear the accelerometers during extended periods of swimming.</li>
		<li>Ask them to keep the accelerometers on during naps and overnight. 
		NOTE: The accelerometers are labeled to identify the left and right sensors. If the accelerometers need to be removed during the wearing period, the labels help identify the correct side when putting them back on. Instruct users to write down on the wearing log when they were taken off and put back on again.The accelerometers are worn at night because, when we let people take them off, they often are not put back on, or are put back on the opposite limbs.</li>
	</ol>
	</li>
	<li>Send the participant home with encouragement to engage in normal daily activities and instructions about when to take the two accelerometers off, and how to bring or mail back the accelerometers and wearing log.</li>
</ol>
3. Download the Data for Visual Inspection
<ol>
	<li>When the accelerometers have been returned after a wearing period of 24 h or more, connect the accelerometers to the computer to download the recorded data.</li>
	<li>Select &#39;Download&#39; within the appropriate software and then choose a location to store the data on the computer using the &#39;Change Location&#39; button.
	<ol>
		<li>Select the option to &#39;Create AGD file&#39;.</li>
		<li>For files that are easy to view, choose &#39;10 s&#39; from the &#39;Epoch&#39; dropdown box. Use these files in Step 3.3.</li>
		<li>Select &#39;Download All Devices&#39;.</li>
	</ol>
	</li>
	<li>Visually inspect the data to confirm the accelerometers were worn for the planned time period and/or that the data matches the wearing log.
	<ol>
		<li>From the top menu, click &#39;File | Open AGD File&#39; and then select the files to open.</li>
		<li>Look at the &#39;Daily Graphs&#39; to see the collected data.</li>
		<li>Confirm that activity occurred during typical waking hours and that there are not extended periods of no activity, except in the night time. The graphs can be scaled to focus in on smaller increments of time and scrolled through if desired.</li>
	</ol>
	</li>
</ol>
4. Download the Data for Processing
<ol>
	<li>Repeat the Download process (Step 3.2) but this time choose &#39;1 s&#39; from the &#39;Epoch&#39; dropdown box. This will bin the data into 1 s epochs,10,11,12 and generate files that will be used for calculations. 
	NOTE: The accelerometers and software used here (see Table of Materials) use proprietary software to filter out high frequency, non-human activity (e.g. accelerations from being in a car ride). Filtering may need to be done with custom-written software if using different devices and software. Custom-written software can also be used to identify and remove upper limb tremor, such as in a person with Parkinson Disease.</li>
	<li>From the 1 s files saved in Step 4.1, calculate a vector magnitude time series of the 3-dimensional data as the square root of (x2+y2+z2) from data from each accelerometer. This time series can then be used to calculate a number of variables to quantify upper limb activity during daily life. 
	NOTE: The processing instructions assume a one day wearing period. If the wearing period is longer, data can be processed in separate single day chunks, or as a single time series with calculated variables adjusted by the by the length of the wearing period when appropriate.</li>
</ol>
5. Variables and Graphical Representations Created from the Accelerometry Data
NOTE: Upper limb movements associated with walking are included in the analyzed data. Previous work has established that walking does not influence the accelerometer ratio variables.15 Although inclusion of walking does not change the non-ratio variables for neurologically-intact adults,27 it is possible that the inclusion of walking could result in a small overestimation of the non-ratio variables for participants with stroke.
<ol>
	<li>Calculate hours of use of each limb by summing all the seconds during the recording period when the activity count was non-zero, and then converting to hours.12,17 
	NOTE: This calculation yields one value for each limb.</li>
	<li>Calculate the use ratio (also called the activity ratio) by dividing the hours of use of the non-dominant limb (or affected limb) by the hours of use of the dominant (or non-affected) limb. 
	NOTE: The use ratio quantifies the total duration of one limb&#39;s activity with respect to the other.12,15 This calculation yields a single value, typically between 0 and 1. A value of 1 indicates the two limbs are used for equal durations throughout the wearing period. A value of zero means that the non-dominant or affected limb was not used at all.</li>
	<li>Calculate the magnitude ratio as follows.
	<ol>
		<li>For each second of data in the time series, compute the natural log of the vector magnitude of the non-dominant limb (or affected limb) divided by the vector magnitude of the dominant (or non-affected) limb.</li>
		<li>Replace values greater than 7 and less than -7, with 7 and -7, respectively, to categorize single limb movement.11 
		NOTE: The magnitude ratio quantifies the contribution of each limb to daily activity on a second-by-second basis.10,11 This is conceptually similar to the use ratio, but takes into account the intensity of movement (acceleration magnitude) of each limb during each second. This calculation yields a time series of values, where values of zero indicate both limbs had equal movement intensities during that instant in time. Positive values indicate greater movement intensity from the non-dominant (or affected) limb and negative values indicate greater movement intensity from the dominant (or unaffected) limb.</li>
	</ol>
	</li>
	<li>Calculate the bilateral magnitude as the sum of the vector magnitude from the two limbs. 
	NOTE: The bilateral magnitude quantifies the intensity of movement in both upper limbs on a second-by-second basis.10,11 This calculation yields a time series of values, where the value indicates the movement intensity, with higher values indicating high intensities.</li>
	<li>Construct density plots to graphically represent the accelerometry data from both limbs11 as follows.
	<ol>
		<li>Plot each second of data as a bivariate histogram with the frequency represented in color. Set the color scale such that cooler colors (blues) indicate less frequent activity and warmer colors (yellow through red) indicate more frequent activity.</li>
		<li>Plot the magnitude ratio, indicating the contribution one limb vs. the other, on the x-axis.</li>
		<li>Plot the bilateral magnitude, indicating the intensity of the movement, on the y-axis.</li>
		<li>Plot the single limb values as separate bars on the far left bar (-7), indicating activity of just the dominant (or unaffected) limb, and on the far right (7), indicating activity of just the non-dominant (or affected) limb. 
		NOTE: The plots provide a context for scientists, clinicians, and participants to interpret two variables together, the magnitude ratio and the bilateral magnitude. An option to create density plots using accelerometry data is available here.44</li>
	</ol>
	</li>
</ol>

The authors declare that they have no conflicting financial interests.

This report details a methodology for measuring upper limb performance in daily life using accelerometers worn on the wrists. Use of this methodology in rehabilitation research and clinical practice affords a significant advancement upon existing methods, i.e. the opportunity to learn how an experimental or typical treatment impacts functional performance in daily life, not just capability in the clinic or laboratory. Accelerometry can be used in conjunction with, or in place of, self-reported measures of daily performance,31,32,33 which may be more susceptible to cognitive deficits or unconscious bias.34,35,36,37 Early adoption of this methodology has yielded data contrary to expectations,5 which could force the field to rethink the content and delivery of rehabilitation services.
Critical steps in the protocol ensure accurate and real data were collected during the wearing period (protocol steps 2.2, 2.3, and 3.3). Failure to follow these steps could result in calculated values that have no meaning. It is relatively easy to make sure that the accelerometers are on the assigned wrists as the person leaves the clinic or laboratory. Visual inspection of the data after the accelerometers are returned is necessary, as participants often behave differently than instructed or anticipated. While relatively rare, participants have been known to remove the accelerometers shortly after leaving the investigative team, putting them back on again on the wrong sides, or trying to encourage others in their family to wear them. Much of this can be avoided if the accelerometers are clearly marked for each side, the wearing log is completed, and the data are inspected shortly after returning, i.e. in case a follow-up telephone call is needed to clarify wearing side and times.
While the accelerometry methodology quantifies general upper limb performance, it does not provide information about movement quality or about specific activities that were performed during the wearing period, such as knowing that a participant was eating; see reference3 for a discussion of this issue. As a tool then, accelerometry will be most useful as an outcome measure when the scientific question or rehabilitation intervention is focused on changing general upper limb performance in daily life, such as the amount of activity and the involvement of bilateral limbs in daily activity. Accelerometry will be less useful as an outcome measure when the scientific question or rehabilitation intervention is focused on changing the quality of movement or changing only a few specific movements in daily life. We anticipate that computational methods will improve over time and future generations of this methodology may be able to overcome these limitations.
In conclusion, accelerometry presents an opportunity for quantitative assessment of upper limb performance in daily life. The methodology described here can be considered the upper limb version of the more common mobility methodologies, where steps per day or minutes of moderate physical activity are recorded on wearable devices.38,39,40,41,42,43 While developed for persons with stroke, the versatility of the methodology will allow future application in a variety of other populations. Additional methodological development is needed in adult and pediatric neurorehabiliation populations other than stroke to help answer clinical and research questions related to bilateral activity of the upper limbs.

Over the last two decades, there has been an explosion of interest in wearable sensors to measure movement. A wearable sensor that has generated a great deal of interest in the neurorehabilitation field is the accelerometer.1,2,3 Accelerometers, as the name implies, measure accelerations in gravitational units (1 g = 9.8 m/s2) or in arbitrary units called activity counts (1 activity count = a manufacturer-specified gravitational value). Accelerations, like human movement, are typically measured and recorded in three dimensions, corresponding to the different axes of the device. The devices are commercially available and resemble large wrist watches; they can be worn during daily activities with minimal disruption. Because of the reasonable cost and their ready availability, the use of accelerometers (termed accelerometry) is being integrated into neurorehabilitation research.
The value of accelerometry to the field of neurorehabilitation is that it offers a non-invasive, unbiased, quantitative measure of upper limb motor activity outside the clinic or laboratory.3 A key goal of rehabilitation services for people with stroke and other neurological conditions is to improve one&#39;s ability to function in daily life, and not just in the clinic or laboratory. The World Health Organization&#39;s International Classification of Function distinguishes between the capacity for activity, as measured in a structured environment with clinical tests, and performance of activity, as measured in an unstructured environment.4 Accelerometry enables measurement of upper limb performance in the unstructured environment, i.e. what someone actually does when they are not in the clinic or laboratory, not just what they could do. Incorporation of accelerometry into stroke rehabilitation research is now challenging the long-held assumption that functional improvements in a structured clinical environment translate to improvements in performance in unstructured, daily life.5,6,7,8
Our group9,10,11,12,13,14 and others7,15,16,17,18,19,20,21,22,23,24 have been spent a great deal of time and effort on developing accelerometry methodology for use in research and clinical practice. Accelerometry has become well established as a valid and reliable tool for measuring upper limb performance post stroke.1,2,15,16,17,25 The most recent challenge has been turning the raw accelerometer data into clinically meaningful information (see reference3 for a summary of this development process). The methodology described here can be used for distinguishing upper limb performance in daily life in healthy control participants10,12 from that in participants who have suffered from stroke6,9,11 or have other disorders. The variables derived from this methodology are responsive to change and quantify improvements over time.14 The accelerometer methodology is appropriate for research studies investigating upper limb dysfunction and interventions designed to improve upper limb performance in daily life in people with stroke and other neurologic populations. Because of its relative simplicity, it may not be long before it is also incorporated in clinical neurorehabilitation practice.

<table><tbody><tr><td>Accelerometers (2)</td><td>Actigraph LLC</td><td>wGT3X-BT</td><td>This is the most common device on the market.&amp;nbsp; Similar products are available from other vendors.&amp;nbsp; http://actigraphcorp.com/products-showcase/activity-monitors/actigraph-wgt3x-bt/</td></tr><tr><td>Hub</td><td>Actigraph LLC</td><td>7 Port USB Hub</td><td>This device connects the accelerometers to the computer allowing for charging and communication. Includes hub, usb cables, power connector. http://actigraphcorp.com/products/7-port-usb-hub-2016/</td></tr><tr><td>Straps&amp;nbsp;</td><td>Actigraph LLC</td><td>Woven Nylon Wrist Band&amp;nbsp;</td><td>Other straps that are velcro or disposable are also available.&amp;nbsp; http://actigraphcorp.com/product-category/accessories/</td></tr><tr><td>Actilife Software</td><td>Actigraph LLC</td><td></td><td>It is best to purchase the software from the same vendor as the accelerometers.&amp;nbsp; Similar products are available from other vendors. http://actigraphcorp.com/products-showcase/software/actilife/</td></tr><tr><td>Computational software</td><td></td><td></td><td>The most common software is MATLAB, but computation could also be done in Excel or other similar products.&amp;nbsp;&amp;nbsp;</td></tr></tbody></table>

a method for quantifying upper limb performance in daily life using accelerometers

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

Data from a referent sample of community-dwelling, neurologically-intact adults can be used to interpret data from participants with stroke or other conditions affecting upper limb performance.10,11,12 Table 1 shows summary statistics for hours of use and the use ratio from a healthy referent sample. Overall, most people are active with their dominant and non-dominant hands for about the same amount of time throughout the day. The average is near 9 h, but there is a wide range, capturing more active and less active people. The average use ratio is just under 1.0 and has a small standard deviation. Thus, regardless of how active one is, the dominant and non-dominant limbs are used for similar durations throughout the day. Further, age does not influence upper limb performance measures in the presence of good health.12 Calculated values substantially outside these referent values (&#177; 3-4 SDs) should be carefully checked to ensure that they are real, as suggested by Uswatte and colleagues.16
<table fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td></td>
			<td>Average</td>
			<td>Standard Deviation</td>
			<td>Minimum&#160;</td>
			<td>Maximum</td>
		</tr>
		<tr>
			<td>Hours of dominant limb use</td>
			<td>9.1</td>
			<td>1.9</td>
			<td>4.4</td>
			<td>14.2</td>
		</tr>
		<tr>
			<td>Hours of non-dominant limb use</td>
			<td>8.6</td>
			<td>2</td>
			<td>4.1</td>
			<td>15.5</td>
		</tr>
		<tr>
			<td>Use ratio</td>
			<td>0.95</td>
			<td>0.06</td>
			<td>0.79</td>
			<td>1.1</td>
		</tr>
	</tbody>
</table>
Table : Summary Accelerometry Statistics from Neurologically-intact, Community Dwelling Adults. Values are from referent sample of 74 community dwelling adults (average age 54 &#177; 11, 53% female, 84% right hand dominant), from reference12.
The density plots allow one to take a closer look at the data. Figure 1 is a representative density plot from a healthy adult, with data collected and processed as described above. Plots like this provide important information about upper limb performance in daily life. There are three key features of this plot that are highly consistent across adults of all ages.3,11 First, the picture is symmetrical. This indicates that the upper limbs are active together throughout the day, with the dominant and non-dominant limbs used similarly. The similarity of movement may not present be at a specific instance in time, with each limb taking its turn leading or lagging during various activities, but can be seen over the course of the day. Even the bars on either side at -7 and 7 (indicating solely dominant and solely non-dominant activity) are similar in color. The symmetry is contrary to common perceptions about hand dominance. Second, the plot is tree-shaped with a wide bottom portion and rounded edges. The &#39;rims&#39; or rounded edges of the bottom portion represent activity where one limb is moving while the other is relatively still. An example of this would be placing objects in a container with one hand while holding the container with the other.10 The symmetry in the rounded edges indicates that both hands are active to perform and to stabilize similarly over the course of the day. The top peak represents the less frequent, higher intensity activities, such as placing large objects on a high shelf with both hands.10 And third, there is a warm glow in the center. This indicates that the most frequent upper limb movements are low intensity with approximately equal contributions from both limbs. Examples of this would be typing or cutting with a knife and fork.10
<img alt="Figure 1" src="/files/ftp_upload/55673/55673fig1.jpg" /> 
Figure 1: Representative Example from a Neurologically-intact Adult. The density plot shows 24 h of upper limb use in daily life, plotted on a second-by-second basis. The x-axis (magnitude ratio) indicates the contribution of each limb to activity. The y-axis (bilateral magnitude) indicates the intensity of movement. The color represents frequency, with the large color bar scale on the right side of the figure, where brighter colors indicate greater frequencies. The small bars at -7 and 7 represent unilateral dominant and non-dominant activity, respectively. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55673/55673fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Across this sample of adults, the density plots are remarkably similar in shape and color.11 People who are relatively inactive tend to have shorter, wider, pictures with cooler colors. People who are very active tend to have taller pictures with warmer colors. The striking consistency across adults makes it easy to identify participants with upper limb performance that is different than from these norms.
Figure 2 is an example of a density plot in a person with stroke. This person is a right-handed male who had an ischemic stroke affecting his brain on the right side 11 months prior to these data being collected. The right side of the brain controls the left side of the body, and his left upper limb had moderate paresis and dysfunction, as indicated by a Motricity Index28 score of 60/100 and an Action Research Arm Test29 score of 38/57. During the 24 h wearing period, the paretic, left limb was active for 1.5 h and the non-paretic, right limb was active for 5.8 h. His use ratio was 0.47, approximately half of the normal value. Compared to the density plot in Figure 1, this density plot is decidedly asymmetrical, indicating that the paretic upper limb was rarely active during daily life. The cool colors of the middle portion of the plot compared to the dark red colors of the single bar at -7 indicate a high frequency of movement with just the non-paretic limb. The overall peak is low, indicating only low intensity activities. Overall, the density plot indicates that the paretic limb participates only minimally in daily activity.
<img alt="Figure 2" src="/files/ftp_upload/55673/55673fig2.jpg" /> 
Figure 2: Representative Example from a Person with Stroke. The density plot shows 24 h of upper limb use in daily life, plotted on a second-by-second basis. The x-axis (magnitude ratio) indicates the contribution of each limb to activity. The y-axis (bilateral magnitude) indicates the intensity of movement. The color represents frequency, with the large color bar scale on the right side of the figure, where brighter colors indicate greater frequencies. The small bars at -7 and 7 represent unilateral dominant and non-dominant activity, respectively. Compare the symmetry, peak height, and color to Figure 1. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55673/55673fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
While the accelerometry methodology has been developed for use in persons with stroke, the utility of this methodology extends to other populations. It can be beneficial for evaluating outcomes in a variety of patient populations. Figure 3 is an example of a density plot in a person with an upper limb amputation below the elbow. This individual was a 75 year old male, injured in an accident approximately 8 years ago. His right, previously dominant, hand was amputated at the time of the accident. He owns an upper limb prosthesis but wears it only 1-2 times per month to lift heavy objects. Most of the time, as in this figure, he does not wear it. During the 24 h wearing period, the intact, left limb was active for 6.9 h and the residual, right limb was active for 4.7 h (accelerometer was worn distally on the residual limb). His use ratio was 0.68, indicating a preference for engaging the intact limb over the residual limb. This density plot is less symmetrical and has cooler colors than that of a control subject (Figure 1), but is more symmetrical and shows more activity than the person with stroke shown in Figure 2. Thus, this person favors the intact limb, but still engages the residual limb in activities during daily life.
<img alt="Figure 3" src="/files/ftp_upload/55673/55673fig3.jpg" /> 
Figure 3: Representative Example from a Person with Upper Limb Amputation. The density plot shows 24 h of upper limb activity in daily life, plotted on a second-by-second basis. The x-axis (magnitude ratio) indicates the contribution of each limb to activity at the moment in time. The y-axis (bilateral magnitude) indicates the intensity of movement. The color represents frequency, with the large color bar scale on the right side of the figure, where brighter colors indicate greater frequencies. The small bars at -7 and 7 represent unilateral dominant and non-dominant activity, respectively. Compare the symmetry, peak height, and color to Figures 1 and 2. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55673/55673fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Another example of how this methodology might be used is in persons with limited mobility who need to increase activity. Figure 4 is an example of a density plot from an elderly, right-handed individual staying in a skilled nursing facility. This person was debilitated after an acute illness and was receiving nursing and rehabilitation services in order to regain independence and return home. The dominant limb was active for 2.4 h and the non-dominant limb was active for 2.0 h. The use ratio was 0.84, which is on the low end of the normative range (see Table 1). This density plot is nearly symmetrical, as would be expected from a general medical condition, but the peak is very low and the colors are mostly cool, indicating little activity during the wearing period.
<img alt="Figure 4" src="/files/ftp_upload/55673/55673fig4.jpg" /> 
Figure 4: Representative Example from a Person Recovering from Medical Illness in a Skilled Nursing Facility (SNF). The density plot shows 22 h of upper limb activity in daily life, plotted on a second-by-second basis. The x-axis (magnitude ratio) indicates the contribution of each limb to activity at the moment in time. The y-axis (bilateral magnitude) indicates the intensity of movement. The color represents frequency, with the large color bar scale on the right side of the figure, where brighter colors indicate greater frequencies. The small bars at -7 and 7 represent unilateral dominant and non-dominant activity, respectively. Compare the symmetry, peak height, and color to Figure 1. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55673/55673fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Finally, this methodology may not be just for adults. The protocol is suitable for children, with minor adaptations to encourage wearing (e.g. colorful straps, suggestions that the devices &#39;make you look like a superhero&#39;). Density plots from typically developing children show the same general shapes as adults, the tree-shape being narrower and the peak substantially higher. The children&#39;s shapes are consistent with their greater activity levels; an example of density plots from a typically developing child and a child with hemiparetic cerebral palsy can be seen on p. 25, Figure 5B and 5C in reference3. Further investigations are needed for the application to pediatric clinical practice. It is noted that the use ratio has a consistent moderate relationship to self-reporting of upper limb activity in adults with stroke,1 but, in children with cerebral palsy, the use ratio is not related to the parent report of upper limb activity.30 Whether the altered relationship between sensor-measured and reported values lies in the perceptions of the reporters or in some quantitative or qualitative difference of how children move is unknown. Future studies are sorely needed to determine normative values for typically developing children and investigate the interpretation of values in children with disabilities.

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

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

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

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12.3K Views.  Washington University School of Medicine. This protocol describes a method to quantify upper limb performance in daily life using wrist-worn accelerometers.

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

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). 
To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated.
The mobile versus immobile classification set had a sensitivity of 86.30% &#177; 7.2% and specificity of 98.96% &#177; 0.6%, while the second prediction set had a sensitivity of 88.35% &#177; 7.80% and specificity of 98.51% &#177; 0.62%. For the third classification set, sensitivity was 84.92% &#177; 6.38% and specificity was 98.17 &#177; 0.62. F1 scores for the first, second and third classification sets were 86.17 &#177; 6.3, 80.19 &#177; 6.36, and 78.42 &#177; 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that ...

Behavior

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 ...

Measuring the Motor Aspect of Cancer-Related Fatigue using a Handheld Dynamometer

Cancer-related fatigue (CRF) is commonly reported by patients both during and after receiving treatment for cancer. Current CRF diagnoses rely on self-report questionnaires which are subject to report and recall biases. Objective measurements using a handheld dynamometer, or handgrip device, have been shown in recent studies to correlate significantly with subjective self-reported fatigue scores. However, variations of both the handgrip fatigue test and fatigue index calculations exist in the literature. The lack of standardized methods limits the utilization of the handgrip fatigue test in the clinical and research settings. In this study, we provide detailed methods for administering the physical fatigue test and calculating the fatigue index. These methods should supplement existing self-reported fatigue questionnaires and help clinicians assess fatigue symptom severity in an objective and quantitative manner.

Simple and accessible methods were developed to measure the motor aspect of cancer-related fatigue objectively and quantitatively. We describe, in detail, ways to administer the physical fatigue test using a simple handgrip device as well as methods to calculate fatigue indices.

Cancer-related fatigue (CRF) is commonly reported by patients both during and after receiving treatment for cancer. Current CRF diagnoses rely on ...

Cancer Research

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Physical activity (PA) assessment needs tools that are inexpensive and easy to administer. Common questionnaires inquire time spent in light, moderate, and vigorous PA. However, inaccuracies may occur due to individually different understanding of PA intensity levels. Alternatively used direct measures (e.g., accelerometers) are susceptible to reactivity bias and may lack the ability to capture certain activities. Compared to accelerometer measurement, respondents report more time spent in higher-intensity PA. A video that visualizes PA intensity levels might help to overcome this problem. This report describes the design of a randomized controlled trial as a methodology to investigate the effect of a video on the difference between self-reported and directly measured PA. It is hypothesized that the video reduces the mean difference between the two measures. Individuals from the general population are recruited. Hip-worn accelerometers are used to collect directly measured PA data on seven consecutive days. Afterwards, participants are randomly allocated to the experimental and the control group. The experimental group receives a video demonstration on PA intensity levels and subsequent PA assessment via self-administered computer-assisted questionnaire. The control group receives PA assessment only. Thereafter, the data are processed to compare the difference between self-reported and accelerometer-based moderate-to-vigorous physical activity (MVPA) between the study groups using a two-sample t-test. This methodology is appropriate for investigating the effect of any existing or self-produced video on the difference between the two measurement methods. It can be used not only for persons from the general population, but for a variety of other populations and contexts as accurate measures are needed to evaluate PA levels.

This protocol describes a randomized controlled trial as a method to test the effect of a video demonstration on the intra-individual difference between self-reported and accelerometer-based moderate-to-vigorous physical activity.

Physical activity (PA) assessment needs tools that are inexpensive and easy to administer. Common questionnaires inquire time spent in light, moderate, ...

A Standardized Method for Measurement of Elbow Kinesthesia

Proprioception is an important component of controlled movement. The threshold to detection of passive movement (TDPM) is a commonly used method for quantifying the proprioceptive submodality of kinesthesia in research settings. The TDPM paradigm has been found to be valid and reliable; however, the equipment and methods used for TDPM vary between studies. In particular, the research laboratory apparatuses for producing passive movement of an extremity are often custom designed by individual laboratories or inaccessible due to high cost. There is a need for a standardized, valid, and reliable method for measuring TDPM using readily available equipment. The purpose of this protocol is to provide a standardized method for measurement of TDPM at the elbow that is economical, easy to administer, and that produces quantitative results for measurement purposes in research-based settings. This method was tested on 20 healthy adults without neurological impairment, and eight adults with chronic stroke. The results obtained suggest this method is a reliable way to quantify elbow TDPM in healthy adults, and provides initial support for validity. Researchers seeking a balance between equipment affordability and measurement precision are most likely to find this protocol of benefit.

Here, we present a standardized method for measurement of elbow passive kinesthesia using the threshold to detection of passive movement (TDPM) that is appropriate for a research setting.

Proprioception is an important component of controlled movement. The threshold to detection of passive movement (TDPM) is a commonly used method for ...

Physical Activity Measurement in Children Accepting Table Tennis Training

An increasing body of evidence now shows that the majority of children in China experience lower levels of physical activity (PA) than the recommended guideline. Table tennis is a compound and technically difficult game that is popular in China; undertaking table tennis training in clubs can help children to elevate their levels of PA. Given that children cannot complete self-evaluated questionnaires themselves and caregiver-based observations are not suitable for children, we hypothesized that an actigraphy-based method can be an objective method to measure PA. In the present study, we describe a procedure that can be used to evaluate PA levels using an actigraphic device and software. Furthermore, since hip-worn devices are known to reduce compliance, we attempted to assess the agreement between hip-worn and wrist-worn device data. Collectively, our results indicate that these devices are suitable for measuring PA and leisure time physical activity (LTPA) levels. Together with subjective questionnaires, both hip-worn and wrist-worn devices are highly suitable for evaluating PA in Chinese children undergoing table tennis training in clubs.

This study proposes an accelerometer-based method to objectively measure physical activity (PA) and leisure time physical activity (LTPA) in Chinese children accepting table tennis training in clubs.

An increasing body of evidence now shows that the majority of children in China experience lower levels of physical activity (PA) than the recommended ...

Integrating VR and Biosensors for Quantitative Motion-Muscle Activity Assessment

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

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

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

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

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

Engineering

Home-Based Monitor for Gait and Activity Analysis

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

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

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

Bioengineering

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 ...