Name: quantified assessment of infant's gross motor abilities using a multisensor wearable
Uploaded: 2024-05-17T13:30:00
Duration: 9 min 24 s
Description: Watch this Scientific Journal Video about Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable at JoVE.com

This work was supported by the Finnish Academy (314602, 335788, 335872, 332017, 343498), Finnish Pediatric Foundation (Lastentautien tutkimuss&#228;&#228;ti&#246;), Aivos&#228;&#228;ti&#246;, Sigrid Juselius Foundation, and HUS Children&#39;s Hospital/HUS diagnostic center research funds.&#8195;

The studies carried out with this system were reviewed by the Ethics committee of the New Children&#39;s Hospital, Helsinki University Hospital, and the hospital gave permission to carry out the research projects described in the original studies cited in this work. An informed consent was obtained to film the child in the video.
1. Preparing the suit for a recording session
<ol>
	<li>Select the correct suit size. Currently, the sizes run up from size 68 cm, and there are five distinct size options (68 cm, 74 cm, 80 cm, 86 cm, and 92 cm). Ensure that the suit fits snugly yet comfortably and the child can move freely without interference.</li>
	<li>Start the recording, i.e., data collection using the data logger application, Maijulogger, as described in Supplemental file 1.
	<ol>
		<li>On the first page, enter the &#34;Subject identification number&#34;. The number of movement sensors used in the assessment is set to 4 as a default, as shown on the first page.</li>
		<li>Proceed to the next stage by pressing the forward arrow.</li>
	</ol>
	</li>
	<li>Equip four sensors with batteries (version CR2025).</li>
	<li>Pair each sensor with the mobile application by selecting and pressing a limb location on the application and bringing the sensor close to the mobile device.
	<ol>
		<li>Confirm that the correct sensor number is shown on the application.</li>
		<li>Ensure sufficient battery charge level (recommended&#160;&#62; 80%) by checking the indicators below the sensor number. 
		NOTE: Replace the sensor batteries, if needed, by lifting the backside cover.</li>
	</ol>
	</li>
	<li>Fix each sensor with the snap-on mounts in the correct sleeve pocket.
	<ol>
		<li>Ensure that the sensors are correctly oriented.</li>
		<li>Always check the latest user guide for details. Incorrect orientation will lead to unusable data.</li>
	</ol>
	</li>
	<li>Proceed to check the correct sensor pairing by pressing the forward arrow in the application.
	<ol>
		<li>Shake the sensors one by one and check the application to see that the correct indicator is wiggling.</li>
	</ol>
	</li>
	<li>When the checks are complete, proceed to the Start page by pressing the forward arrow. 
	NOTE: If needed, the duration of the recording (hh:mm) can be set manually by pressing the Settings button at the start of each recording.
	<ol>
		<li>Press the Record button to start the recording. Wait until the sensors are ready before dressing the suit on the infant (this may take a few min).</li>
	</ol>
	</li>
	<li>For unsupervised home recordings, set the screen lock to the mobile device to avoid its parental operation and pack the suit for delivery to the recipient.
	<ol>
		<li>Use a courier or a similar service to deliver the suit to the recipient immediately after it has been prepared.</li>
	</ol>
	</li>
</ol>
2. Preparing and dressing the infant for a recording session
<ol>
	<li>Ensure that the infant is nursed (i.e., fed and diapers changed) and feels secure and comfortable for a natural and spontaneous playtime.
	<ol>
		<li>Remove the suit later for a diaper change, if needed.</li>
	</ol>
	</li>
	<li>Dress the suit on the infant as done with regular overalls.
	<ol>
		<li>Check that the sensor pockets are facing outwards (i.e., away from the body midline instead of rotating towards the front or back of the infant).</li>
	</ol>
	</li>
	<li>Check that the suit fits snugly on the limbs at each sensor location.
	<ol>
		<li>Adjust the straps near the pockets to tighten the sensor on the limb, if needed. 
		NOTE: It is also possible to add clothes underneath the suit or bandages to fasten the sensors in case the limbs are too thin for sufficient fastening with the straps.</li>
	</ol>
	</li>
</ol>
3. Recording session
<ol>
	<li>Keep the mobile device near the infant to ensure a reliable data transmission over the Bluetooth connection (i.e., in the same room or within 10 m if recording in open space) when recording with direct data streaming.
	<ol>
		<li>If applicable, keep the mobile device in a bag, such as a tablet sleeve, for easier and safer handling. The Bluetooth&#160;protocol will attempt reconnecting if the BLE-connection is lost. 
		NOTE: It is not advisable to pause and then continue the recording as it may disrupt synchronization between sensors or the temporal integrity of the recorded data, as well as you may lose the BLE connection. On such occasions, the only option is to switch off the application and start the recording anew.</li>
	</ol>
	</li>
	<li>Arrange the surroundings to facilitate play with toys and other objects and encourage the infant to move freely. For instance, clear the space for moving around or lay a mat on the ground so the infant may be encouraged to move when seeing age-appropriate toys. The aim is to record the natural movement of the infant.
	<ol>
		<li>Ensure that the infant feels comfortable and secure enough to engage in spontaneous play without experiencing anxiety due to new people or unfamiliar places.</li>
	</ol>
	</li>
	<li>Record at least 1 h of free play or as long as desired by the study questions. Multiple play/movement epochs can be combined later during the analysis.</li>
	<li>If required by the specific research protocol, take further notes to facilitate detailed inspection of individual results. Use real-time synchronized annotations in the data logger application as free text, audio recordings, or video recordings.</li>
	<li>For unsupervised home recordings, set the recording to stop automatically (i.e., set the duration (hh:mm) of the recording manually by pressing the Settings button).</li>
	<li>In supervised settings, end the recording by pressing the Stop button on the application.</li>
	<li>Take off the suit and pack it for return to the laboratory.</li>
	<li>Wash the suit after each recording after taking the sensors off the suit pockets. If available, use detergents, including moisture transportation aimed at synthetic materials11.
	<ol>
		<li>Inspect the suit visually after washing for mechanical defects before storing it for the use on the next child.</li>
	</ol>
	</li>
</ol>
4. Cloud-based analysis: upload of raw data and download of results
<ol>
	<li>Click on the recorded file in the database view of the application, and then click Export to Babacloud, which opens the home page of the computational cloud (https://babacloud.fi/) using the web browser on the mobile device (Supplemental file 2).</li>
	<li>Enter username and password into the login page. If needed, request new credentials from the email address provided on the Babacloud home page.</li>
	<li>Choose the recorded file for an upload. Then, choose an identification number for the subject and add the required information as indicated by the Babacloud workflow. If relevant for the case, also enter the subject&#39;s age or other information (e.g., playtime) to &#34;Add another raw file label&#34;. Finally, save the upload session by pressing the Save button. 
	NOTE: As a default, the data logger application will generate a zipped file from the movement data to enable an easier and faster data transfer over the air via a wi-fi or mobile network. The Babacloud interface will require credentials that can be obtained as instructed on their web page. The recorded raw data can also be transferred directly from the phone to another device for customized analysis. Ensure that unnecessary data from the memory of the mobile device is deleted to avoid confusion between recording sessions. The recording sessions are automatically placed in their own folders, which are named with their respective time stamps.</li>
	<li>Download the zipped package of analysis outputs from the cloud server using a mobile device or a computer web browser. Click the correct Subject ID, then choose the link to the desired analysis, which automatically opens the file download. 
	NOTE: In the zipped results package, the two following items can be found: A detailed result matrix, which gives all second-by-second classifications for posture, movement, infant carrying, and free playtime (Figure 2A, Supplemental file 3). Graphical reports that give an overview of the study session (Supplemental file 4).</li>
</ol>

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M., Gasson, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+role+of+early+fine+and+gross+motor+development+on+later+motor+and+cognitive+ability.">The role of early fine and gross motor development on later motor and cognitive ability.</a> Human Movement Science. 27 (5), 668-681 (2007).</li><li>Adolph, K. E., Franchak, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+development+of+motor+behavior.">The development of motor behavior.</a> WIREs Cognitive Science. 8, e1430 (2017).</li><li>Adolph, K. E., Hoch, J. E., Cole, W. 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M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hammersmith+infant+neurological+examination+for+infants+born+preterm:+predicting+outcomes+other+than+cerebral+palsy.">Hammersmith infant neurological examination for infants born preterm: predicting outcomes other than cerebral palsy.</a> Developmental Medicine &amp; Child Neurology. 63, 939-946 (2021).</li><li>De Kegel, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+reference+values+must+be+established+for+the+Alberta+Infant+Motor+Scales+for+accurate+identification+of+infants+at+risk+for+motor+developmental+delay+in+Flanders.">New reference values must be established for the Alberta Infant Motor Scales for accurate identification of infants at risk for motor developmental delay in Flanders.</a> Child: Care, Health and Development. 39 (2), 260-267 (2013).</li><li>Airaksinen, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intelligent+wearable+allows+out-of-the-lab+tracking+of+developing+motor+abilities+in+infants.">Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants.</a> Communications Medicine. 2, 69 (2022).</li><li>Airaksinen, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+posture+and+movement+tracking+of+infants+with+wearable+movement+sensors.">Automatic posture and movement tracking of infants with wearable movement sensors.</a> Scientific Reports. 10, 169 (2020).</li><li>Airaksinen, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Charting+infants'+motor+development+at+home+using+a+wearable+system:+validation+and+comparison+to+physical+growth+charts.">Charting infants' motor development at home using a wearable system: validation and comparison to physical growth charts.</a> eBioMedicine. 92, 104591 (2023).</li><li>Hewitt, L., Kerr, E., Stanley, R. 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D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tummy+time+and+infant+health+outcomes:+a+systematic+review.">Tummy time and infant health outcomes: a systematic review.</a> Pediatrics. 145, e20192168 (2020).</li><li>Pascal, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Motor+outcome+after+perinatal+stroke+and+early+prediction+of+unilateral+spastic+cerebral+palsy.">Motor outcome after perinatal stroke and early prediction of unilateral spastic cerebral palsy.</a> European Journal of Paediatric Neurology. 29, 54-61 (2020).</li><li>Tabard-Foug&#232;re, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Are+clinical+impairments+related+to+kinematic+gait+variability+in+children+and+young+adults+with+cerebral+palsy.">Are clinical impairments related to kinematic gait variability in children and young adults with cerebral palsy.</a> Frontiers in Human Neuroscience. 16, 816088 (2022).</li><li>Fish, L. A., Jones, E. J. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+survey+on+the+attitudes+of+parents+with+young+children+on+in-home+monitoring+technologies+and+study+designs+for+infant+research.">A survey on the attitudes of parents with young children on in-home monitoring technologies and study designs for infant research.</a> PLOS One. 16, e0245793 (2021).</li><li>Porciuncula, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Wearable+movement+sensors+for+rehabilitation:+a+focused+review+of+technological+and+clinical+advances.">Wearable movement sensors for rehabilitation: a focused review of technological and clinical advances.</a> Physical Medicine &amp; Rehabilitation. 10 (9), S220-S232 (2018).</li></ol>

E.I. is the founder of the company Planno Oy that consults in textile engineering. Other authors have no relevant interests to disclosure.

A quantified assessment and developmental tracking of infants&#39; motor performance with a wearable solution, such as MAIJU, is technically simple to learn and perform, and it can be readily implemented into health care or clinical research practice10,11,12. Compared to the other existing motor assessment methods, this kind of at-home recording of infants&#39; spontaneous motor activity improves the ecological validity of the assessment. Furthermore, it provides a quantified, transparent, and fully automated analysis of infants&#39; motor performance. Most importantly, the metrics used in the analysis are intuitive and explainable, which enables their easy comparison with other clinical and research assessments, such as environmental factors, cognitive development, or psychosocial assessments. A holistic assessment of motor development provides an accuracy that compares well with the conventional physical growth measures12.
Critical steps in the protocol include careful preparation of the wearable suit. When preparing for a recording, choosing the correct size for the suit is crucial, as the sensor attachments in the sleeves and legs are required to sit tightly to obtain a reliable recording of body movements. Also, for a successful recording, it is essential to place the sensors in the pockets with a correct orientation, as indicated in the protocol. The sensor mounts will not allow sensors to rotate during the recording. However, the incorrectly oriented sensor records data that is difficult, if not impossible, to fix afterward. The infant should be&#160;encouraged to move freely and independently during the recording. The recording length&#160;may vary according to the given study questions. The multiple spontaneous movement epochs are combined to accumulate enough spontaneous movement for each recording session.
The flexible and practical operation of the MAIJU wearable solution allows its use in variable contexts in both supervised and unsupervised settings, such as research labs or homes. Recent results from our clinical trials show that fully unsupervised recordings conducted at home may provide comparable results with recordings that are done under full or partial supervision12. Still, a child&#39;s spontaneous motor behavior is potentially affected by several factors, such as the surroundings (e.g., playing outside vs. indoors, the layout of the space, furniture, and toys), the child&#39;s level of alertness, and the parents&#39; involvement during the home recording. When the recordings are performed in unsupervised settings at home, it is important to encourage the child to play spontaneously, i.e., to play or move independently, without someone else carrying or holding the child if not necessary, and to keep the recording mobile phone at a Bluetooth range (in the same room)10. The majority of our current troubleshooting situations during the recordings are caused by loss of Bluetooth connection. Near-future advances in sensor technology will improve Bluetooth connectivity, and the upcoming introduction of a larger sensor memory will allow offline recording by storing movement data directly in the sensor memory.
Out-of-hospital recordings with a wearable solution of this kind are readily scalable and they may improve&#160;safety of infants, e.g., by enabling remote monitoring during circumstances such as a pandemic. Our present&#160;classifier algorithms&#160;were trained to specifically recognize the given motor abilities, postures, and movements shown in the motility description scheme (Figure 2A). These phenomena were identified as characteristic of infant movement during the first two years of life. Other types of movements or postures seen in older children, such as running or jumping, will require modified movement description schemes and respective algorithms to be trained to identify them. Posture-context dependent analysis is a potentially fruitful approach where an infant&#39;s motor activity is analyzed separately in different postures to&#160;support studying, e.g., developmental correlates of infant behavior5,6,7,8,9,13. Alternatively, a context-dependent movement analysis could also support assessing asymmetry in motor function when predicting the development of unilateral cerebral palsy10,12,14,15. Further, assessment of motor abilities with the MAIJU system may be combined with other study modalities, e.g., eye tracking, imaging, or video recording, to provide multimodal data, spanning it to different types and contexts. Multimodal data may be useful, e.g., in evaluating the effects of social interaction or the efficacy of therapeutic intervention.
For the success of novel wearable technologies in out-of-hospital monitoring environments with infants, certain limitations, challenges, and ethical concerns need to be addressed. Our analysis pipelines were trained and validated using typically developing infants in Finland10,11,12. The raw analysis outputs with pure postures and movements should be universal. However, their developmental trajectories may require adjustments for diverse cultures and geographical locations. According to parent feedback regarding wearable devices, they are viewed favorably due to infant-friendliness16. However, parents may raise concerns regarding privacy, data access, and family practicalities (e.g., multiple caregivers, visitors, and varying schedules). Dependency on the battery life of the sensors and the recording phone can be considered a limitation of the method. In our experience, the battery model (CR2025)&#160;typically lasts for the full day (12-24 hours) when using continuous data streaming. Notably, it depends on both the battery brand and the strength of the Bluetooth connection needed for wireless data transmission, which is continuously changing to maximize data transmission in the recording environment. For instance, a long distance between the infant and the phone or a wall between them would adjust the Bluetooth connection to significantly higher battery consumption. Notably, the batteries of most mobile devices are also drained within about the same time if using continuous Bluetooth streaming. In practice, the presently used continuous data streaming over Bluetooth connection implies that both the sensors and mobile devices need a daily recharge/battery replacement. The near future introduction of sensors with larger memory capacity will allow data storage in the sensor memory, supporting over a week&#160;of continuous recording. That will remove the need for power-consuming Bluetooth streaming, as well as carrying the phone within a Bluetooth range that may be perceived as restrictive in recording situations and is susceptible to human error.
Overall, tracking of early neurodevelopment needs&#160;methods that are&#160;sensitive to&#160;natural neurobehavioural variability. Gross motor development is an intricate process consisting of variations in&#160;the order and timing, both on individual and cultural levels4. Detection of&#160;atypical motor development is&#160;effective in&#160;recognizing infants at risk for an extensive range of neurodevelopmental disorders. Traditional test batteries with standardized neurodevelopmental assessments are performed in controlled environments, such as hospitals, and are at least partially subjective7,8,9. Current advances in sensor technology and signal analysis have enabled recordings of infants&#39; spontaneous motor ability over extended periods in out-of-hospital settings and quantitation of the motor behavior at an accuracy comparable with human observers10,11,12. Novel wearable technology offers automated and scalable methods for monitoring movement and the efficacy of therapeutic intervention in infants in an ecologically valid and objective way. Furthermore, the novel neurodevelopmental index Baba Infant Motor Score (BIMS) enables the estimation of infants&#39; maturity of motor ability by individual tracking of neurodevelopment10,12. It can be employed in a range of future applications, such as the development of infant motor growth charts12. By training the automated classifiers for other specific motilities (e.g., for older children or adults) with different kinds of movement description schemes and algorithms, the wearable movement sensors have the potential for clinical applications, such as movement disorders or follow-up on the effects of therapeutic interventions regardless of the developmental stage of the individual17. Currently, however, this should be viewed as an investigational methodology that should not be used to inform clinical diagnosis or treatment targets.

Early gross motor development is essential for higher-level neurocognitive performance that emerges later by supporting infants&#39; exploration of the environment. Therefore, clinicians and researchers alike have a high interest in assessing early gross motor development1,2,3. To provide support for evidence-based medicine or scientific studies, it is essential that the gross motor assessments are quantitative, reliable, objective, and ecologically valid. However, there is a scarcity of such methods available for either clinical or basic science research.
A typical early gross motor development progresses through a predictable sequence of newly acquired skills. They are commonly observed in infants as reaching discrete motor milestones4, where standing and walking are often considered significant landmarks en route to more complex behavioral repertoire5. In addition to direct observation or parental surveys about motor milestones, several widely used standardized batteries&#160;have been developed6,7,8,9 for performing infants&#39; assessments in the laboratory or hospital environment. However, these assessments suffer from multiple caveats: they need substantial expertise from trained professionals, they are partly subjective and categorical, and they assess infants&#39; performance in an environment (hospital or laboratory) that is unnatural from an infant&#39;s perspective.
Recording infants&#39; spontaneous motor activity over longer times in the native environment, such as their home, enables more relevant measures of motor abilities. In one such feasible method, the assessment is performed for the full sequence of the infant&#39;s motor ability development from lying supine to walking fluently with a wearable system, such as the MAIJU wearable (Motor Assessment of the Infants with a JUmpsuit)10,11,12. The MAIJU wearable system (Figure 1) involves a whole-body textile garment equipped with movement sensors to allow unsupervised out-of-hospital/laboratory assessments and recordings that are analyzed with an automated pipeline, providing a second-by-second assessment of posture and movement patterns. These algorithmic detections can be used for each posture and movement type separately, or they can be combined for a holistic assessment of the maturational level of the infant&#39;s motor abilities. A recently published, unit-free expression of such metric of motor maturity is BIMS (Baba Infant Motor Score)10,12.
This article will describe the assessment of infants&#39; gross motor performance using a multisensor wearable suit; the rationale, practical performance, analysis pipeline, and potential future perspectives for using the metrics that can be obtained from the automated analysis pipeline available for recordings with a multisensor wearable10,11,12. The method is suitable for a detailed quantitation of spontaneous gross motor activities in all infants that exhibit motor abilities between supine lying and fluent walking.
The multisensor wearable system consists of three components: 1) the full body overall garment equipped with four movement sensors, 2) a mobile device using a custom-built iOS application, and 3) a cloud-based analysis pipeline (Babacloud the credentials for which can be obtained from the authors)11. The waterproof inertial measurement unit (IMU) sensors stream synchronized data (3-axis accelerometer and gyroscope) at 13-52 Hz sampling frequency to a mobile phone using a low-energy Bluetooth connection. The data is initially stored in the memory of (the sensor or) the mobile device, followed by an&#160;offline analysis in the cloud server after the recording is stopped.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>iOS device (version 16.5 or higher)</td>
			<td>Apple</td>
			<td>n/a</td>
			<td></td>
		</tr>
		<tr>
			<td>MAIJU jumpsuit</td>
			<td>Planno Ltd</td>
			<td>n/a</td>
			<td>customized for purpose</td>
		</tr>
		<tr>
			<td>Maijulogger (mobile application) and sensor firmware</td>
			<td>BABA Center (www.babacenter.fi), Kaasa solutions GmbH</td>
			<td>n/a</td>
			<td>constructed by Kaasa Solutions, distributed by Baba Center</td>
		</tr>
		<tr>
			<td>Movesense movement sensor</td>
			<td>Movesense (www.movesense.com)</td>
			<td>n/a</td>
			<td></td>
		</tr>
	</tbody>
</table>

quantified assessment of infant's gross motor abilities using a multisensor wearable

Developing objective and quantitative methods of early gross motor assessment is essential to better understand neurodevelopment and to support early therapeutic interventions. Here, we&#160;present&#160;a method to quantify gross motor performance using a multisensor wearable, MAIJU (Motility Assessment of Infants with a JUmpsuit), which offers an automated, scalable,&#160;quantitative, and objective assessment using&#160;a fully automated cloud-based pipeline. This wearable suit is equipped with four movement sensors that record synchronized data to a mobile phone utilizing a low-energy Bluetooth connection. An offline analysis in the cloud server generates fully analyzed results within minutes for each recording. These results include a graphical report of the recording session and a detailed result matrix that gives second-by-second classifications for posture, movement, infant carrying, and free playtime. Our recent results show the virtue of such&#160;quantified motor assessment&#160;providing&#160;a potentially&#160;effective method for distinguishing variations in the infant&#39;s gross motor development.

The presented method quantifies infants&#39; gross motor performance by classifying the types of postures and movements for every second of the recording session. Therefore, the results package from the automated analysis pipeline includes a full classification matrix (Supplemental file 3) and a graphical summary (Supplemental file 4) from the entire recording session. Depending on the exact study question, these results can be inspected at different levels.
Results inspection was used for the development and validation of this method. 
In the following, we present four levels of results inspection used for the development and validation of this methodology. Supplemental file 5 presents representative examples of the key validation experiments that were previously published in full detail10,11,12.
First, the automatic algorithms trained for movement and posture detections were validated against second-by-second-level human observations of infants&#39; movement behavior. We used several parallelly trained experts who blindly reviewed the synchronized video recordings with the wearable recordings. All different posture and movement categories were compared separately to the individual human annotations, and we found a very high agreement between the algorithm and human for postures (average kappa 0.93); a substantial agreement was found for movement categories (sub-category specific kappa mostly at the range of 60-80%). See Supplemental file 5A for an example confusion matrix10. Also, interrater agreement levels were assessed to confirm that the algorithms perform at about human equivalent level10,11.
Second, we assessed how well the classifier-based quantification of movement and posture categories would match the corresponding quantification from the second-level human annotations. Example scatter plots shown in Supplemental file&#160;5B10 demonstrate that several key categories have a nearly perfect match (correlation coefficient &#62;0.96) between algorithmic and human visual quantitation. This directly supports the idea that the age-specific distributions of motility quantitation (Figure 3A,B) are reliable12.
Third, the idea of a holistic assessment of motor maturity was validated by training a developmental age prediction from the combination movement quantities (see above) that correlated very closely with the actual age in the typically developing infants (r=0.89; Supplemental file 5C). Subsequently, the age prediction was scaled to 0-100 as a unitless measure BIMS (Baba Infant Motor Score10), and its utility for building motor growth charts (Supplemental file 5D) was validated using a typically developing infant cohort highly showing age-dependent and predictable growth trajectories. We also validated its relative accuracy by showing that the accuracy in motor growth charts compares well with the well-known physical growth charts12.
Fourth, the potential for abnormality detection with the given metrics was validated in a proof of principle experiment where individual motor measures were shown to clearly differentiate between infants with poor and good motor performance, respectively (Supplemental file 5E)10.
Potential further study questions at different levels of analyses 
Figure 3 shows examples of further uses for the information provided by the wearable suit and its automated analysis pipeline. First, the early development of posture and movement skills can be plotted as a function of age and compared to the age-dependent distributions (Figure 3A, &#34;growth charts&#34;12), or the development can be tracked over time for each individual (Figure 3B). Second, when a study question requires a more holistic gross motor assessment, one can use a combination of an individual&#39;s posture and movement proportions (as shown in Supplemental file&#160;5D, computed from the time series in Supplemental file 5C) to generate an index like BIMS (Baba Infant Motor Score). Such measures support direct use in motor growth charts (Supplemental file 5C,D) and computation of statistical derivations like z score (Figure 3A). Using the full-time series of algorithm&#39;s detections (Figure 3C, and Supplemental file 3) allows studies on the detailed temporal structure of infants&#39; motility with questions like &#34;How many posture transitions does the infant perform in a unit time?&#34; or &#34;What is the distribution of standing epochs during spontaneous playtime?&#34;.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65949/65949fig01.jpg" /> 
Figure 1: Overview of the multisensor wearable system and a typical study flow from the recording to the analysis. Figure 1 adapted from Airaksinen et al.12published under CC_BY license. The infant photograph is published with parental consent. <a href="https://www.jove.com/files/ftp_upload/65949/65949fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65949/65949fig02.jpg" /> 
Figure 2: Posture and movement categorization and an example visualization of&#160;raw data and analysis output (A) Posture and movement categorization scheme used by the algorithmic classifiers within the fully automated analysis pipeline for a multisensor wearable. This figure is reprinted from Airaksinen et al.10 (B) Example of a 10 min of raw accelerometer sensor data from each of the four arms as it comes out from the MAIJU recordings. The horizontal bars below depict automated classifier outputs for the posture (upper bar) and movement (lower bar) detections for the same 10 min epoch. <a href="https://www.jove.com/files/ftp_upload/65949/65949fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65949/65949fig03v2.jpg" /> 
Figure 3: Example results derived from the raw classifier outputs. (A) An example of a data comparison between infant age and Baba Infant Motor Score (BIMS). The S-shaped green&#160;curve depicts the developmental trajectory of BIMS in a larger population. The dot depicts an example individual measured at 14&#160;months, with a BIMS ~74, corresponding to a little below the average age-typical level (the green&#160;line). (B) An example cohort with individual trajectories of motor development using the Baba Infant Motor Score over (BIMS). Each line represents an infant recorded at several age points (dots in the line). The lines are colored for the average deviance relative to the age-typical mean (blue S-shaped curve in the background; see also panel A. (C) Representative output matrix from the automatic classifier as it comes from the Babacloud pipeline. The first column depicts the elapsed time from the recording start (in seconds) for each analysis window in the classification (window duration 2.3s, with 50% overlap). The second and third columns show the classifier detection for posture and movement, respectively. The third and fourth columns are auxiliary classifiers depicting epochs when the infant was carried by someone else, and when the infant was engaging in autonomous play, respectively. (D) An illustration of a summary report. Panels A and B are adapted from Airaksinen et al.10. <a href="https://www.jove.com/files/ftp_upload/65949/65949fig03largev2.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental File 1: A quick guide to recording with MAIJU wearable. <a href="https://www.jove.com/files/ftp_upload/65949/supplemental_file1 .zip" target="_blank">Please click here to download this File.</a>
Supplemental File 2: A quick guide to transferring data from Maijulogger to the analysis server in Babacloud. <a href="https://www.jove.com/files/ftp_upload/65949/supplemental_file_2.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 3: An example of a detailed classification matrix that gives all second-by-second classifications for posture, movement, infant carrying, and free playtime. <a href="https://www.jove.com/files/ftp_upload/65949/supplemental_file_3.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 4: A full PDF file example of a graphical summary report. This is taken from the algorithm file that includes, (A) basic background information about the recording session (subject ID and age, the recording date, duration, and the total amount of epochs used for the final analyses). (B) A graphical display of the full recording indicating distribution of postures over the full recording and epochs that are excluded from the quantitative assessment. C) Typical distributions displayed with violin plots for all six postures (left-hand side) and 12 movement types (right-hand side). Similarly, drawings on the right side depict the type of movements indicated by each posture, also showing the sequence of incrementally developing motor performance (the dots indicating the results from an individual recording and colorful violin plots indicating age-dependent distributions of posture/movement of a relevant dataset). Notably, the raw values indicated by the dots show actual amounts of the given motor performance, and they can be used directly in other contexts. <a href="https://www.jove.com/files/ftp_upload/65949/supplemental_file_4.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 5: Validation experiments at different levels of analysis. (A) Confusion matrices showing agreement between human annotations (target class) and the algorithm&#39;s detections (predicted class) for both the posture and the movement categories. (B) Comparison of motor quantitations over full recording sessions between human annotations and detections derived from the automated classifier. (C) Correlation between developmental age prediction from the wearable data (left side Y axis) and its re-scaling to generate the BIMS score (right side Y axis). The actual age of the infant at the time of recording is shown on the X-axis. (D) Correlation of the age prediction and the actual age when using a fitted function. The values depict fit to the model when using group average at the depicted time windows (blue), all raw values (black), or when accounting for repeated measures of each individual (green). (E) Comparison of individual motor measures between groups of well and poorly performing infants suggests that several automatically detected motor metrics may differentiate these infant groups. Panel A,B,C is adapted from Airaksinen et al.10. Panel D is adapted from Airaksinen et al.12. Panel E is adapted from &#34;Airaksinen et al.11. <a href="https://www.jove.com/files/ftp_upload/65949/supplemental_file_5.zip" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable at JoVE.com

Quantified Assessment of Infant&#039;s Gross Motor Abilities Using a Multisensor Wearable

This paper outlines the assessment of infants' gross motor performance with a multisensor wearable and its fully automated deep learning-based analysis pipeline. The method quantifies the posture and movement patterns of infants from lying supine until they master walking independently.

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Developing objective and quantitative methods of early gross motor assessment is essential to better understand neurodevelopment and to support early ...

quantified-assessment-infant-s-gross-motor-abilities-using

Research

JoVE Journal

Neuroscience

Kinematics and Ground Reaction Force Determination: A Demonstration Quantifying Locomotor Abilities of Young Adult, Middle-aged, and Geriatric Rats

Behavior, in its broadest definition, can be defined as the motor manifestation of physiologic processes. As such, all behaviors manifest through the motor system. In the fields of neuroscience and orthopedics, locomotion is a commonly evaluated behavior for a variety of disease models. For example, locomotor recovery after traumatic injury to the nervous system is one of the most commonly evaluated behaviors 1-3. Though locomotion can be evaluated using a variety of endpoint measurements (e.g. time taken to complete a locomotor task, etc), semiquantitative kinematic measures (e.g. ordinal rating scales (e.g. Basso Beattie and Bresnahan locomotor (BBB) rating scale, etc)) and surrogate measures of behaviour (e.g. muscle force, nerve conduction velocity, etc), only kinetics (force measurements) and kinematics (measurements of body segments in space) provide a detailed description of the strategy by which an animal is able to locomote 1. Though not new, kinematic and kinetic measurements of locomoting rodents is now more readily accessible due to the availability of commercially available equipment designed for this purpose. Importantly, however, experimenters need to be very familiar with theory of biomechanical analyses and understand the benefits and limitations of these forms of analyses prior to embarking on what will become a relatively labor-intensive study. The present paper aims to describe a method for collecting kinematic and ground reaction force data using commercially available equipment. Details of equipment and apparatus set-up, pre-training of animals, inclusion and exclusion criteria of acceptable runs, and methods for collecting the data are described. We illustrate the utility of this behavioral analysis technique by describing the kinematics and kinetics of strain-matched young adult, middle-aged, and geriatric rats.

Locomotion is often examined as a behavioural outcome in various models of disease in fields such as neuroscience and orthopedics. This video paper intends to describe a method for collecting ground reaction forces and kinematics from rats during unrestrained locomotion.

Behavior, in its broadest definition, can be defined as the motor manifestation of physiologic processes.  As such, all behaviors manifest through the ...

Assessment of Cerebral Lateralization in Children using Functional Transcranial Doppler Ultrasound (fTCD)

There are many unanswered questions about cerebral lateralization. In particular, it remains unclear which aspects of language and nonverbal ability are lateralized, whether there are any disadvantages associated with atypical patterns of cerebral lateralization, and whether cerebral lateralization develops with age. In the past, researchers interested in these questions tended to use handedness as a proxy measure for cerebral lateralization, but this is unsatisfactory because handedness is only a weak and indirect indicator of laterality of cognitive functions1. Other methods, such as fMRI, are expensive for large-scale studies, and not always feasible with children2.
Here we will describe the use of functional transcranial Doppler ultrasound (fTCD) as a cost-effective, non-invasive and reliable method for assessing cerebral lateralization. The procedure involves measuring blood flow in the middle cerebral artery via an ultrasound probe placed just in front of the ear. Our work builds on work by Rune Aaslid, who co-introduced TCD in 1982, and Stefan Knecht, Michael Deppe and their colleagues at the University of M&uuml;nster, who pioneered the use of simultaneous measurements of left- and right middle cerebral artery blood flow, and devised a method of correcting for heart beat activity. This made it possible to see a clear increase in left-sided blood flow during language generation, with lateralization agreeing well with that obtained using other methods3.
The middle cerebral artery has a very wide vascular territory (see Figure 1) and the method does not provide useful information about localization within a hemisphere. Our experience suggests it is particularly sensitive to tasks that involve explicit or implicit speech production. The 'gold standard' task is a word generation task (e.g. think of as many words as you can that begin with the letter 'B') 4, but this is not suitable for young children and others with limited literacy skills. Compared with other brain imaging methods, fTCD is relatively unaffected by movement artefacts from speaking, and so we are able to get a reliable result from tasks that involve describing pictures aloud5,6. Accordingly, we have developed a child-friendly task that involves looking at video-clips that tell a story, and then describing what was seen.

Assessment of Cerebral Lateralization in Children using Functional Transcranial Doppler Ultrasound fTCD

Functional transcranial Doppler sonography (fTCD) is a simple and non-invasive ultrasound technique which can be used to assess the lateralization of cognitive functions, especially language, and is suitable for use with children.

There are many unanswered questions about cerebral lateralization.  In particular, it remains unclear which aspects of language and nonverbal ability are ...

Assessment of Motor Balance and Coordination in Mice using the Balance Beam

Brain injury, genetic manipulations, and pharmacological treatments can result in alterations of motor skills in mice. Fine motor coordination and balance can be assessed by the beam walking assay. The goal of this test is for the mouse to stay upright and walk across an elevated narrow beam to a safe platform. This test takes place over 3 consecutive days: 2 days of training and 1 day of testing. Performance on the beam is quantified by measuring the time it takes for the mouse to traverse the beam and the number of paw slips that occur in the process. Here we report the protocol used in our laboratory, and representative results from a cohort of C57BL/6 mice. This task is particularly useful for detecting subtle deficits in motor skills and balance that may not be detected by other motor tests, such as the Rotarod.

Deficits in fine motor coordination can be assessed with the balance beam test. Performance on the beam is quantified by the speed at which the beam is traversed and the number of times the mouse slips on the beam.

Brain injury, genetic manipulations, and pharmacological treatments can result in alterations of motor skills in mice.  Fine motor coordination and ...

Extracellularly Identifying Motor Neurons for a Muscle Motor Pool in Aplysia californica

In animals with large identified neurons (e.g. mollusks), analysis of motor pools is done using intracellular techniques1,2,3,4. Recently, we developed a technique to extracellularly stimulate and record individual neurons in Aplysia californica5. We now describe a protocol for using this technique to uniquely identify and characterize motor neurons within a motor pool.
This extracellular technique has advantages. First, extracellular electrodes can stimulate and record neurons through the sheath5, so it does not need to be removed. Thus, neurons will be healthier in extracellular experiments than in intracellular ones. Second, if ganglia are rotated by appropriate pinning of the sheath, extracellular electrodes can access neurons on both sides of the ganglion, which makes it easier and more efficient to identify multiple neurons in the same preparation. Third, extracellular electrodes do not need to penetrate cells, and thus can be easily moved back and forth among neurons, causing less damage to them. This is especially useful when one tries to record multiple neurons during repeating motor patterns that may only persist for minutes. Fourth, extracellular electrodes are more flexible than intracellular ones during muscle movements. Intracellular electrodes may pull out and damage neurons during muscle contractions. In contrast, since extracellular electrodes are gently pressed onto the sheath above neurons, they usually stay above the same neuron during muscle contractions, and thus can be used in more intact preparations.
To uniquely identify motor neurons for a motor pool (in particular, the I1/I3 muscle in Aplysia) using extracellular electrodes, one can use features that do not require intracellular measurements as criteria: soma size and location, axonal projection, and muscle innervation4,6,7. For the particular motor pool used to illustrate the technique, we recorded from buccal nerves 2 and 3 to measure axonal projections, and measured the contraction forces of the I1/I3 muscle to determine the pattern of muscle innervation for the individual motor neurons.
We demonstrate the complete process of first identifying motor neurons using muscle innervation, then characterizing their timing during motor patterns, creating a simplified diagnostic method for rapid identification. The simplified and more rapid diagnostic method is superior for more intact preparations, e.g. in the suspended buccal mass preparation8 or in vivo9. This process can also be applied in other motor pools10,11,12 in Aplysia or in other animal systems2,3,13,14.

Extracellularly Identifying Motor Neurons for a Muscle Motor Pool in Aplysia californica

In animals with large identified neurons (e.g. mollusks), analysis of motor pools is done using intracellular techniques1,2,3,4. Recently, we developed a technique to extracellularly stimulate and record individual neurons in Aplysia californica5. We now describe a protocol for using this technique to uniquely identify and characterize motor neurons within a motor pool.

In animals with large  identified neurons (e.g. mollusks), analysis of motor pools is done using  intracellular techniques1,2,3,4. Recently,  we developed ...

Functional and Morphological Assessment of Diaphragm Innervation by Phrenic Motor Neurons

This protocol specifically focuses on tools for assessing phrenic motor neuron (PhMN) innervation of the diaphragm at both the electrophysiological and morphological levels. Compound muscle action potential (CMAP) recording following phrenic nerve stimulation can be used to quantitatively assess functional diaphragm innervation by PhMNs of the cervical spinal cord in vivo in anesthetized rats and mice. Because CMAPs represent simultaneous recording of all myofibers of the whole hemi-diaphragm, it is useful to also examine the phenotypes of individual motor axons and myofibers at the diaphragm NMJ in order to track disease- and therapy-relevant morphological changes such as partial and complete denervation, regenerative sprouting and reinnervation. This can be accomplished via whole-mount immunohistochemistry (IHC) of the diaphragm, followed by detailed morphological assessment of individual NMJs throughout the muscle. Combining CMAPs and NMJ analysis provides a powerful approach for quantitatively studying diaphragmatic innervation in rodent models of CNS and PNS disease.

Compound muscle action potential recording quantitatively assesses functional diaphragm innervation by phrenic motor neurons. Whole-mount diaphragm immunohistochemistry assesses morphological innervation at individual neuromuscular junctions. The goal of this protocol is to demonstrate how these two powerful methodologies can be used in various rodent models of spinal cord disease.

This protocol specifically focuses on tools for assessing phrenic motor neuron (PhMN) innervation of the diaphragm at both the electrophysiological and ...

Stimulation Location Determination using a 3D Digitizer with High-Definition Transcranial Direct Current Stimulation

The abundance of neuroimaging data and rapid development of machine learning has made it possible to investigate brain activation patterns. However, causal evidence of brain area activation leading to a behavior is often left missing. Transcranial direct current stimulation (tDCS), which can temporarily alter brain cortical excitability and activity, is a noninvasive neurophysiological tool used to study causal relationships in the human brain. High-definition transcranial direct current stimulation (HD-tDCS) is a noninvasive brain stimulation (NIBS) technique that produces a more focal current compared to conventional tDCS. Traditionally, the stimulation location has been roughly determined through the 10-20 EEG system, because determining precise stimulation points can be difficult. This protocol uses a 3D digitizer with HD-tDCS to increase accuracy in determination of stimulation points. The method is demonstrated using a 3D digitizer for more accurate localization of stimulation points in the right temporo-parietal junction (rTPJ).

Presented here is a protocol to achieve higher accuracy in determination of stimulation location combining a 3D digitizer with high-definition transcranial direct current stimulation.

The abundance of neuroimaging data and rapid development of machine learning has made it possible to investigate brain activation patterns. However, ...

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

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

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

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

Three-Dimensional Motor Nerve Organoid Generation

A fascicle of axons is one of the major structural motifs observed in the nervous system. Disruption of axon fascicles could cause developmental and neurodegenerative diseases. Although numerous studies of axons have been conducted, our understanding of formation and dysfunction of axon fascicles is still limited due to the lack of robust three-dimensional in vitro models. Here, we describe a step-by-step protocol for the rapid generation of a motor nerve organoid (MNO) from human induced pluripotent stem (iPS) cells in a microfluidic-based tissue culture chip. First, fabrication of chips used for the method is described. From human iPS cells, a motor neuron spheroid (MNS) is formed. Next, the differentiated MNS is transferred into the chip. Thereafter, axons spontaneously grow out of the spheroid and assemble into a fascicle within a microchannel equipped in the chip, which generates an MNO tissue carrying a bundle of axons extended from the spheroid. For the downstream analysis, MNOs can be taken out of the chip to be fixed for morphological analyses or dissected for biochemical analyses, as well as calcium imaging and multi-electrode array recordings. MNOs generated with this protocol can facilitate drug testing and screening and can contribute to understanding of mechanisms underlying development and diseases of axon fascicles.

This protocol provides a comprehensive procedure to fabricate human iPS cell-derived motor nerve organoid through spontaneous assembly of a robust bundle of axons extended from a spheroid in a tissue culture chip.

A fascicle of axons is one of the major structural motifs observed in the nervous system. Disruption of axon fascicles could cause developmental and ...

Multifactorial Assessment of Motor Behavior in Rats after Unilateral Sciatic Nerve Crush Injury

The induction of a peripheral nerve injury is a widely used method in neuroscience for the assessment of repair and pain mechanisms among others. In addition, in the research field of movement disorders, sciatic crush injury has been employed to trigger a dystonia-like phenotype in genetically predisposed DYT-TOR1A rodent models of dystonia. To achieve consistent, reproducible and comparable results after a sciatic nerve crush injury, a standardized method for inducing the nerve crush is essential, in addition to a standardized phenotypical characterization. Attention must be paid not only to the specific assortment of behavioral tests, but also to the technical requirements, the correct execution and consecutive data analysis. This protocol describes in detail how to perform a sciatic nerve crush injury and provides a behavioral test battery for the assessment of motor deficits in rats that includes the open field test, the CatWalk XT gait analysis, the beam walking task, and the ladder rung walking task.

We provide a protocol for the assessment of motor behavior via a behavioral test battery in rats after sciatic nerve crush injury.

The induction of a peripheral nerve injury is a widely used method in neuroscience for the assessment of repair and pain mechanisms among others. In ...

Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique

This paper examines the application of electroencephalogram-based methods to assess the effects of audio-tactile substitution training in young, profoundly deaf (PD) participants, with the aim of analyzing the neural mechanisms associated with vibrotactile complex sound discrimination. Electrical brain activity reflects dynamic neural changes, and the temporal precision of event-related potentials (ERPs) has proven to be key in studying time-locked processes while performing behavioral tasks that involve attention and working memory. 
The current protocol was designed to study electrophysiological activity in PD subjects while they performed a continuous performance task (CPT) using complex-sound stimuli, consisting of five different animal sounds delivered through a portable stimulator system worn on the right index finger. As a repeated-measures design, electroencephalogram (EEG) recordings in standard conditions were performed before and after a brief training program (five 1 h sessions over 15 days), followed by offline artifact correction and epoch averaging, to obtain individual and grand-mean waveforms. Behavioral results show a significant improvement in discrimination and a more robust P3-like centroparietal positive waveform for the target stimuli after training. In this protocol, ERPs contribute to the further understanding of learning-related neural changes in PD subjects associated with audio-tactile discrimination of complex sounds.

This protocol is designed to explore underlying learning-related electrophysiological changes in subjects with profound deafness after a short training period in audio-tactile sensory substitution by applying the event-related potential technique.