NSFC-RSE Joint Project (81911530253), National Key R&#38;D Program of China (2018YFF0300905), and K. C. Wong Magna Fund in Ningbo University.

The Human Ethics Committee of Ningbo University approved this experiment. All written informed consent was obtained from all subjects after they were told about the goal, requirements, and experimental procedures of the UGT experiment.
1. Laboratory preparation for gait
<ol>
	<li>Kinematics: Motion capture system
	<ol>
		<li>When calibrating the system, turn off the incandescent lights and remove any possible reflective objects that can be mistaken for passive retro-reflective markers. Ensure that eight infrared cameras are properly aimed and have a clear and reasonable view.</li>
		<li>Plug the appropriate USB dongle into the PC&#8217;s parallel port. Switch on the motion-capture infrared cameras and analog-to-digital converter.</li>
		<li>Open the tracking software in the PC and allow time for the eight infrared cameras to initialize. Select &#8220;Local System&#8221; node of the &#8220;Resources&#8221; pane. Every camera node will show a green light if the hardware connection is true.</li>
		<li>Adjust the system parameters in the Camera view pane: set the Strobe Intensity to 0.95 - 1, Threshold to 0.2 - 0.4, Gain to times 1 (x1), Grayscale Mode to Auto, Minimum Circularity Ratio to 0.5, and Max Blob Height to 50.</li>
		<li>Put the T-frame consisting of 5 markers in the center of the motion capture area. Select all cameras using 2D mode and confirm that they can view the calibration wand (T-frame) without any interference and/or artefacts. Click the &#8220;System Preparation&#8221; item in the toolbar and select the 5 marker Wand &#38; T-Frame calibration object from the T-Frame drop-down list.</li>
		<li>In the &#8220;Tool&#8221; pane, select the &#8220;System Preparation&#8221; button, and click the &#8220;Start&#8221; button in the &#8220;Calibrate Cameras&#8221; section. Then physically wave the T-frame in the capture range. Stop the action when the blue lights on the infrared cameras stop flashing. Monitor the progress bar until the Calibration Process is completed at &#8220;100%&#8221;and returns to &#8220;0%&#8221;. 
		NOTE: Ensure the values of the Image Error are less than 0.3.</li>
		<li>Put the T-frame on the floor (the center of the motion capture area) and ensure the axes of T-frame are consistent with the heading direction.</li>
		<li>Select the &#8220;Start&#8221; button under the &#8220;Set Volume Origin&#8221; section in the Tool pane.</li>
	</ol>
	</li>
	<li>Plantar pressure: Pressure platform
	<ol>
		<li>Put the 2 m pressure platform in the center of the test area. Notice the eight infrared cameras displayed around the pressure platform.</li>
		<li>Divide the pressure platform into four average areas, A, B, C and D (each area is 50 cm * 50 cm) in a linear fashion and distinguish them with an alphabet label / sticker (Figure 1).</li>
		<li>Keep the PC and pressure platform connected via the proprietary data cable.</li>
		<li>Double-click the software icon on the desktop.</li>
		<li>Click the &#8220;Weight Calibration&#8221; on the Calibration Screen and input the body mass of a staff. Ask him or her to stand on the pressure platform, waiting until the system completes the calibration automatically before he/she can leave the pressure platform.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61558/61558fig01.jpg" /> 
Figure 1: Experimental protocol.&#160;If subjects received the termination signal as the heel touched area (A), the UGT was executed so that the subject stopped in area (B). Kinematic and plantar pressure data were collected synchronically. <a href="https://www.jove.com/files/ftp_upload/61558/61558fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Participant preparation
<ol>
	<li>Before the UGT test, interview all subjects and provide them with a simple explanation about the experimental goals and procedures. Obtain written informed consent from subjects who meet the key inclusion criteria.
	<ol>
		<li>Include participants who are physically active male adults, have the right leg as dominant, do not have any hearing disorder, do not have lower-limb disorders, and have not incurred injuries in the last six months. 
		NOTE: 15 male subjects (age: 24.1 &#177; 0.8 years; height: 175.7 &#177; 2.8 cm; body weight: 68.3 &#177; 3.3 kg; foot length: 252.7 &#177; 2.1 mm) who met the experimental conditions were included in this test.</li>
	</ol>
	</li>
	<li>Allow all subjects fill in a questionnaire survey. 
	NOTE: Questions include: Have you had a history of running or other physical activities? How often do you do physical activities in a week? Do you have any professional athletic training? Have you suffered any lower-limb disorders and injuries in the last six months?</li>
	<li>Ensure that all subjects wear identical t-shirts and tight-fitting pants.</li>
	<li>Measure subjects&#8217; standing height (mm) and body weight (kg), lower limb length (mm), knee width (mm) and ankle width (mm) of both left and right leg using Vernier caliper or small anthropometer. 
	NOTE: Measure the lower limb length from the superior iliac spine to the ankle medial condyle; the knee width from the lateral to the medial knee condyle; the ankle width from the lateral to the medial ankle condyle.</li>
	<li>Shave off the body hair as appropriate and remove excess sweat using alcohol wipes. Prepare skin areas of anatomical bony landmarks for marker placement on joints and segments. 
	NOTE: This study used 16 reflective markers18, including anterior-superior iliac spine (LASI/RASI), posterior-superior iliac spine (LPSI/RPSI), lateral mid-thigh (LTHI/RTHI), lateral knee (LKNE/RKNE), lateral mid-shank (LTIB/RTIB), lateral malleolus (LANK/RANK), second metatarsal head (LTOE/RTOE) and calcaneus (LHEE/RHEE) (Figure 2).</li>
	<li>Identify 16 anatomical landmarks. On the landmarks, attach passive retro-reflective markers with double-sided adhesive tapes.</li>
	<li>Give each subject 5 min to adapt to the test environment and warm up with light running and stretching.</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61558/61558fig02.jpg" /> 
Figure 2: The reflective markers attached to the lower limbs.&#160;(A) side, (B) front and (C) rear. <a href="https://www.jove.com/files/ftp_upload/61558/61558fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
3. Static calibration
<ol>
	<li>Kinematics: Motion capture system
	<ol>
		<li>In the tracking software, find the &#8220;New Database&#8221; in the toolbar to build a database. Click the &#8220;Data Management&#8221; to open the &#8220;Data Management&#8221; pane and click in order the &#8220;New Patient Classification&#8221;, &#8220;New Patient&#8221; and &#8220;New Session&#8221; button. Return to the &#8220;Resources&#8221; window, select &#8220;Create A New Subject&#8221; button to create a subject, and enter the values of height (mm), body weight (kg), leg length (mm), knee width (mm), and ankle width (mm) in the &#8220;Properties&#8221; pane.</li>
		<li>Click the &#8220;Go Live&#8221; and then click the &#8220;Split Horizontally&#8221; in the &#8220;View&#8221; pane. Then select the graph to view the Trajectory count. 
		NOTE: Check the &#8220;3D Perspective&#8221; pane to ensure that all 16 markers are visible.</li>
		<li>Ask subjects to stand still in the area A. Click &#8220;Start&#8221; in the subject capture section to capture the static model. About 200 frames of images were captured before clicking the &#8220;Stop&#8221; button.</li>
		<li>In the &#8220;Tools&#8221; pane, find the &#8220;Pipeline&#8221; button, and click on &#8220;Run the reconstruct pipeline&#8221; to build a new 3D image of all captured markers. Identify in the markers&#39; list, and manually apply the corresponding labels to the markers. Save and press &#8220;ESC&#8221; key to exit.</li>
		<li>Select &#8220;Subject Preparation&#8221; and &#8220;Subject Calibration&#8221; in the toolbar and choose the &#8220;Static plug-in gait&#8221; option in the drop-down menu.</li>
		<li>Select the &#8220;Left Foot&#8221; and &#8220;Right Foot&#8221; in the &#8220;Static Settings&#8221; pane and click the &#8220;Start&#8221;. Then save the static model.</li>
	</ol>
	</li>
	<li>Plantar pressure: Pressure platform
	<ol>
		<li>In the software, click &#8220;Database&#8221; to add a new patient. And enter the assigned subject number in the &#8220;Add Patient&#8221; pane. Then, click &#8220;Add&#8221;.</li>
		<li>Click &#8220;Dynamic&#8221; and enter body weight and shoe size. Then, click &#8220;OK&#8221;.</li>
	</ol>
	</li>
</ol>
4. Dynamic trials
<ol>
	<li>Ask the subject to be at the starting position.</li>
	<li>Software Operations 
	NOTE: The two kinds of software start (Motion capture system: click &#8220;Capture&#8221; button; Pressure platform: click &#8220;Capture&#8221; button) and end (Motion capture system: click &#8220;Stop&#8221; button; Pressure platform: click &#8220;Save Measurement&#8221; button), simultaneously.
	<ol>
		<li>Kinematics: Motion capture system
		<ol>
			<li>Select the &#8220;Go Live&#8221; button in the &#8220;Resources&#8221; pane and click &#8220;Capture&#8221; in the right toolbar. Find &#8220;Trial Type&#8221; and &#8220;Session&#8221; from top to bottom and edit &#8220;Trial&#8221; description.</li>
			<li>Ask subjects to perform UGT test as described in 4.3.</li>
			<li>After finishing the UGT test, click &#8220;Stop&#8221; to end the data collection trial. Repeat the above steps for 5 times.</li>
		</ol>
		</li>
		<li>Plantar pressure: Pressure platform
		<ol>
			<li>Select the &#8220;Measure&#8221; button before starting the UGT trials.</li>
			<li>After finishing the UGT test, click &#8220;Save Measurement&#8221; button to save data. Repeat the above steps for 5 times.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>UGT trials
	<ol>
		<li>Ask subjects to walk along a walkway at their NWS and instruct them to use the dominant leg and non-dominant leg to pass area A and B, respectively, and finally stop at area D on the pressure platform.</li>
		<li>Let the subject know when the termination signal is provided they need to quickly stop on area B.</li>
		<li>Randomly provide the termination signal as the heel touches area A, ensure that the UGT is executed and subjects stop quickly on area B (Figure 1). The staff sends the termination signal by randomly ringing a red bell, and the probability of ringing was controlled at about 20%. Capture at least five successive UGT trials. 
		NOTE: There is a 2-min rest interval between both trials.</li>
		<li>Calculate each subject&#8217;s walking speed using the pressure platform software. Then, calculate the FWS as 125% of the NWS.</li>
		<li>Repeat the above UGT test for the FWS. Capture at least 5 successive UGT trials using the FWS protocol.</li>
	</ol>
	</li>
</ol>
5. Post-processing
<ol>
	<li>Kinematics: Motion capture system
	<ol>
		<li>Find the &#8220;Data Management&#8221; button in the toolbar and double-click the trial name in the &#8220;Data Management&#8221; pane. Then select &#8220;Reconstruct&#8221; and &#8220;Label&#8221; to reconstruct the 3D dynamic model.</li>
		<li>On the &#8220;Time&#8221; bar, move the blue triangles to set the required range of time (for the stance phase during UGT).</li>
		<li>Click on the &#8220;Time&#8221; bar. Then click &#8220;Zoom to Region-of-Interest&#8221; in the &#8220;Context&#8221; menu.</li>
		<li>Click the &#8220;Label&#8221; button to identify and check the label points. Ensure the steps are the same as the static identification process. 
		NOTE: Fill in some incomplete identification markers and delete the unlabeled markers (if necessary).</li>
		<li>Choose the &#8220;Dynamic Plug-in Gait&#8221; in the &#8220;Subject Calibration&#8221; pane. Then click the &#8220;Start&#8221; button to run the data. Export dynamic trials in &#8220;.csv&#8221; format for following data analysis.</li>
		<li>Use a fourth-order low pass Butterworth filter with cut off frequency of 10 Hz and export the data of the joint angle.</li>
		<li>Calculate the range of motion (ROM) of three joints (hip, knee, and ankle) in sagittal plane. 
		NOTE: Define the differences between the maximum angles and minimum angles of the hip, knee, and ankle on the sagittal movement planes as the ROMs.</li>
		<li>Calculate means (M) and standard deviations (SD) of the ten trials (5 for NWS, and 5 for FWS) from each subject.</li>
	</ol>
	</li>
	<li>Plantar pressure: Pressure platform
	<ol>
		<li>Select the trial name from the &#8220;Measurements&#8221; menu of the corresponding subjects. Click the &#8220;Dynamic&#8221; button to open data.</li>
		<li>Click the &#8220;Manual&#8221; selection. Use the &#8220;Left Mouse&#8221; button to select the step of interest (the stance phase during GT). Click the &#8220;OK&#8221; button to save.</li>
		<li>Click the &#8220;Zone Division&#8221; and &#8220;Manual Zone Selection&#8221; to make adjusts. Then click the &#8220;Accept&#8221; button to save.</li>
		<li>Open &#8220;Pressures-Forces&#8221; screen and click the &#8220;Graph Composition&#8221; button to open the &#8220;Zone Graph Composition&#8221; window. Divide 10 anatomical regions, including Big Toe (BT), Other Toes (OT), First Metatarsal (M1), Second Metatarsals (M2), Third Metatarsal (M3), Fourth Metatarsal (M4), Fifth Metatarsal (M5), Mid-Foot (MF), Medial Heel (MH) and Lateral Heel (LH). Then click the &#8220;OK&#8221; button to save.</li>
		<li>Click &#8220;Parameter Table&#8221; to export plantar pressure data, including maximum pressure, maximum force and contact area.</li>
		<li>Calculate Means and SDs for 10 trials (5 for NWS, and 5 for FWS) from each subject.</li>
	</ol>
	</li>
</ol>
6. Statistical analysis
<ol>
	<li>Perform the Shapiro&#8211;Wilks tests to check normal distribution for all variables. Use Paired-sampled T-tests to compare lower limb kinematics and plantar pressure data during UGT at NWS and FWS. Set the significance level at p &#60; 0.05.</li>
</ol>

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No potential conflict of interest was reported by the authors.

Most previous studies that analyze gait biomechanics during UGT omit the importance of walking speed in their biomechanical assessment. Thus, this study investigated the lower-limb biomechanical changes that occur in UGT at NWS and FWS with the aim to reveal the speed-related effects.
Significant differences have been found on the ROM of the hip, knee, and ankle joints in the sagittal plane during UGT at NWS and FWS. Our findings showed greater ROMs of the 3 joints in the sagittal plane during...

Human locomotion is considered to be an extremely complex process that needs to be described by multidisciplinary methods1,2. The most representative aspect is the gait analysis by biomechanical approaches. Human gait aims to sustain progression from initiation to termination, and the dynamic balance should be maintained in position movement. Although gait termination (GT) has been extensively studied as a sub-task of gait, it has received less attention. Sparrow and Tirosh3 defined GT in their review as motor control period when both feet stop moving either forward or backward based on the displacement and time characteristics. Compared to steady-state gait, the process of executing GT demands higher control of postural stability and complex integration and cooperation of the neuromuscular system4. During GT, the body needs to rapidly increase the braking impulse and decrease propulsion impulse to form a new body balance5,6. Unplanned gait termination (UGT) is a stress response to an unknown stimulus6. When confronted by an unexpected stimulus that requires one to stop suddenly, initial dynamic balance will be disrupted. Because of the need for the continuous control of the body&#8217;s center of mass (COM) and feedback control, UGT poses a greater challenge to postural control and stablity3,7.
UGT has been reported to be an important factor leading to falls and injuries, especially in elderly people and patients with balance disorders3,8. Faster walking speeds may lead to an additional decline in motor control during UGT9. Ridge et al.10 investigated the peak joint angle and internal joint moment data of children during UGT at normal walking speed (NWS) and fast walking speed (FWS). The results showed larger knee flexion angles and extension moments at faster speeds compared with preferred speed. They indicated that strengthening the related muscles surrounding the lower extremity joints could be a useful intervention for injury prevention during UGT.
Although the effect of walking speed on the lower-limb biomechanical character during steady-state gait has been extensively studied11,12,13, the biomechanical mechanism of UGT under different walking speeds is limited. To our knowledge, only three studies have specifically evaluated healthy individuals&#8217; UGT performances with respect to velocity effects9,10,14. However, subjects in these studies were mainly the elderly14 and children10, the biomechanical mechanism of young adults during UGT is still unclear. Lower-limb kinematics and plantar pressure can provide a precise analysis of locomotion biomechanics, and these are also considered to be crucial components for clinical gait diagnoses15,16. For example, Serrao et al.17 used lower-limb kinematic data to detect the clinical differences between patients with cerebellar ataxia and healthy counterparts during sudden stopping. Besides, compared to planned gait termination (PGT), larger peak pressure and force in the lateral metatarsal during UGT could be observed7, which may be associated with higher injury risks.
Therefore, exploring the biomechanical mechanisms of UGT could provide insights for injury prevention and further clinical researches. This study presents a protocol to investigate any biomechanical alteration in young adults during UGT under different walking speeds. It is hypothesized that, with an increase in walking speed, participants would exhibit different lower-limb biomechanical characteristics during UGT.

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			<td >14 mm Diameter Passive Retro-reflective Marker</td>
			<td >Oxford Metrics Ltd., Oxford, UK</td>
			<td >n=16</td>
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			<td >Double Adhesive Tape</td>
			<td >Minnesota Mining and Manufacturing Corporation, Minnesota, USA</td>
			<td>For fixing markers to skin</td>
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			<td >Motion Tracking Cameras</td>
			<td >Oxford Metrics Ltd., Oxford, UK</td>
			<td>n= 8</td>
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			<td >T-Frame</td>
			<td >Oxford Metrics Ltd., Oxford, UK</td>
			<td>-</td>
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			<td >Valid Dongle</td>
			<td >Oxford Metrics Ltd., Oxford, UK</td>
			<td>Vicon Nexus 1.4.116</td>
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			<td >Vicon Datastation ADC&#160;</td>
			<td >Oxford Metrics Ltd., Oxford, UK</td>
			<td>-</td>
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			<td >Pressure platform</td>
			<td >RSscan International, Olen, Belgium</td>
			<td>-</td>
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lower-limb biomechanical characteristics associated with unplanned gait termination under different walking speeds

Gait termination caused by unexpected stimulus is a common occurrence in everyday life. This study presents a protocol to investigate the lower-limb biomechanical changes that occur during unplanned gait termination (UGT) under different walking speeds. Fifteen male participants were asked to perform UGT on a walkway at normal walking speed (NWS) and fast walking speed (FWS), respectively. A motion analysis system and plantar pressure platform were applied to collect lower-limb kinematic and plantar pressure data. Paired-sampled T-test was used to examine the differences in lower-limb kinematics and plantar pressure data between two walking speeds. The results showed larger range of motion in the hip, knee, and ankle joints in the sagittal plane as well as plantar pressure in forefoot and heel regions during UGT at FWS when compared with NWS. With the increase in walking speed, subjects exhibited different lower-limb biomechanical characteristics that show FWS associated with greater potential injury risks.

Mean &#38; SD values of NWS and FWS of 15 subjects were 1.33 &#177; 0.07m/s and 1.62 &#177; 0.11m/s, respectively.
Figure 3 shows the mean ROM of the hip, knee, and ankle joints in the sagittal plane during UGT at NWS and FWS. Compared with NWS, the ROM of three joints increased significantly at FWS (p&#60;0.05). In detail, the ROM of hip, knee and ankle joints increased from 22.26 &#177; 3.03, 29.72 &#177; 5.14 and 24.92 &#177; 4.17 to 25.98 &#177; 2.94, 31.61 &#...

Watch this Scientific Journal Video about Lower-Limb Biomechanical Characteristics Associated with Unplanned Gait Termination Under Different Walking Speeds at JoVE.com

Lower-Limb Biomechanical Characteristics Associated with Unplanned Gait Termination Under Different Walking Speeds

This study compared the biomechanical characteristics of the lower extremity during unplanned gait termination under different walking speeds. The lower-limb kinematic and kinetic data from fifteen subjects with normal and fast walking speeds were collected using a motion analysis system and plantar pressure platform.

Gait termination caused by unexpected stimulus is a common occurrence in everyday life. This study presents a protocol to investigate the lower-limb ...

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4.4K Views.  Ningbo University. This study compared the biomechanical characteristics of the lower extremity during unplanned gait termination under different walking speeds. The lower-limb kinematic and kinetic data from fifteen subjects with normal and fast walking speeds were collected using a motion analysis system and plantar pressure platform.

Video: Lower-Limb Biomechanical Characteristics Associated with Unplanned Gait Termination Under Different Walking Speeds

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults

Nordic walking (NW) has become a safe and simple form of exercise in recent years, and in studying this gait pattern, various data collection techniques have been employed, each with positives and negatives. The aim was to determine the effect of NW on older adult gait and posture and to determine optimal use of different data collection systems in both short and long duration analysis. Gait and posture during NW and normal walking were assessed in 17 healthy older adults (age: 69 &#177; 7.3). Participants performed two trials of 6 Minute Walk Tests (6MWT) (1 with poles (WP) and 1 without poles (NP)) and 6 trials of a 5m walk (3 WP and 3 NP). Motion was recorded using two systems, a 6-sensor accelerometry system and an 8-camera 3-dimensional motion capture system, in order to quantify spatial-temporal, kinematic, and kinetic parameters.
With both systems, participants demonstrated increased stride length and double support and decreased gait speed and cadence WP compared to NP (p &#60;0.05). Also, with motion capture, larger single support time was found WP (p &#60;0.05). With 3-D capture, smaller hip power generation and moments of force were found at heel contact and pre-swing as well as smaller knee power absorption at heel contact, pre-swing, and terminal swing WP compared to NP, when assessed over one cycle (p &#60;0.05). Also, WP yielded smaller moments of force at heel contact and terminal swing along with larger moments at mid-stance of a gait cycle (p &#60;0.05). No changes were found for posture.
NW seems appropriate for promoting a normal gait pattern in older adults. Three-dimensional motion capture should primarily be used during short duration gait analysis (i.e. single gait cycle), while accelerometry systems should be primarily employed in instances requiring longer duration analysis such as during the 6MWT.

The aim was to substantiate optimal use of data collection techniques for Nordic walking gait and posture analysis. Three-dimensional motion capture should be used during short duration analysis (i.e. single gait cycle), while accelerometry should be employed for longer duration analysis (i.e. repeated cycles) like a 6 Minute Walk Test.

Nordic walking (NW) has become a safe and simple form of exercise in recent years, and in studying this gait pattern, various data collection techniques ...

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running

A limited number of studies have explored lower-limb biomechanics during high-heeled jogging and running, and most studies have failed to clarify the wearing experience of subjects. This protocol describes the differences in lower-limb kinematics and ground reaction force (GRF) between experienced wearers (EW) and inexperienced wearers (IEW) during moderate high-heeled jogging and running. A three-dimensional (3D) motion analysis system with a configured force platform was used to synchronously capture lower-limb joint movements and GRF. 36 young females volunteered to participate in this study and were asked about high-heeled shoe-wearing experience, including frequency, duration, heel types, and heel heights. Eleven who had the experience of 3 to 6 cm heels for a minimum of three days per week (6 h per day) for at least two years and eleven who wore high heels less than twice per month participated. Subjects performed jogging and running at comfortable low and high speeds, respectively, with the right foot completely stepping onto a force platform when passing by along a 10 m walkway. EW and IEW adopted different biomechanical adaptations while jogging and running. IEW exhibited a generally larger range of joint movement, while EW showed a dramatically larger loading rate of GRF during running. Hence, further studies on the lower-limb biomechanics of high-heeled gait should strictly control the wearing experience of the subjects.

This study investigated lower-limb kinematics and ground reaction force (GRF) during moderate high-heeled jogging and running. Subjects were divided into groups of experienced wearers and inexperienced wearers. A three-dimensional motion analysis system with a configured force platform captured lower-limb joint movements and GRF.

A limited number of studies have explored lower-limb biomechanics during high-heeled jogging and running, and most studies have failed to clarify the ...

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Three-dimensional gait analysis (3DGA) is shown to be a useful clinical tool for the evaluation of gait abnormality due to movement disorders. However, the use of 3DGA in actual clinics remains uncommon. Possible reasons could include the time-consuming measurement process and difficulties in understanding measurement results, which are often presented using a large number of graphs. Here we present a clinician-friendly 3DGA method developed to facilitate the clinical use of 3DGA. This method consists of simplified preparation and measurement processes that can be performed in a short time period in clinical settings and intuitive results presentation to facilitate clinicians&#39; understanding of results. The quick, simplified measurement procedure is achieved by the use of minimum markers and measurement of patients on a treadmill. To facilitate clinician understanding, results are presented in figures based on the clinicians&#39; perspective. A Lissajous overview picture (LOP), which shows the trajectories of all markers from a holistic viewpoint, is used to facilitate intuitive understanding of gait patterns. Abnormal gait pattern indices, which are based on clinicians&#39; perspectives in gait evaluation and standardized using the data of healthy subjects, are used to evaluate the extent of typical abnormal gait patterns in stroke patients. A graph depicting the analysis of the toe clearance strategy, which depicts how patients rely on normal and compensatory strategies to achieve toe clearance, is also presented. These methods could facilitate implementation of 3DGA in clinical settings and further encourage development of measurement strategies from the clinician&#39;s point of view.

In this study, a clinician-friendly three-dimensional gait analysis method, which was designed to be performed in the rehabilitation clinic, is presented. The method consists of a simplified measurement method and intuitive figures to facilitate clinicians&#39; understanding of the results.

Three-dimensional gait analysis (3DGA) is shown to be a useful clinical tool for the evaluation of gait abnormality due to movement disorders. However, ...

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

A real-time locating system (RTLS) can be used to track the walking activity of institutionalized older adults in long-term care who are at risk for wandering behaviors. The benefits of a RTLS are objective and continuous measurements of activity. Self-report methods of activity, especially wandering, by health care staff are vulnerable to floor effects and recall bias, and continuous clinical or research observation over the long-term can be time-consuming and expensive. Health care staff also fail to recognize the onset and/or duration of wandering behaviors, which are associated with a variety of adverse health outcomes in this population but amenable to intervention. RTLS technologies can measure the walking activity of institutionalized residents with cognitive impairment over time with a high degree of accuracy. This is particularly useful for the study of wandering, defined as walking for at least 60 seconds with few (if any) breaks in activity. Wandering is associated with disease progression, hospitalizations, falls and death. Previous work suggests older adults with poor balance ability and high sustained walking activity may be particularly susceptible to poor health outcomes. RTLS&#39;s are used to assess cognitive impairment and factors associated with gait and balance; however, supplemental paper and pencil gait/balance tools may be used to further refine risk profiles. This project discusses the use of a RTLS to measure walking activity and also gait quality and balance ability measures on this population.

This paper discusses the use of a continuous and objective real-time locating system to measure walking activity associated with wandering behaviors, focusing on older adults with cognitive impairment. Walking activity is measured by walking distance, sustained walking distance, and sustained gait speed. Also assessed are gait quality and balance ability.

A real-time locating system (RTLS) can be used to track the walking activity of institutionalized older adults in long-term care who are at risk for ...

Biomechanical Analysis Methods to Assess Professional Badminton Players' Lunge Performance

Under the condition of simulating a badminton court in the laboratory, this study used the injury mechanism model to analyze the maximal right lunge movements of eight professional badminton players and eight amateur players. The purpose of this protocol is to study the differences in kinematics and joint moment of the right knee and ankle. A motion capture system and force plate were used to capture data of the joint movements of the lower extremity and the vertical ground reaction force (vGRF). Sixteen young men who did not have any sports injuries in the past 6 months took part in the study. The subjects performed a maximal right lunge from the start position with their right foot, stepping on and fully contacting with the force plate, hit the shuttlecock with an underhand stroke to the designated position in the backcourt, and then returned to the start/end position. All subjects wore the same badminton shoes to avoid a difference in impact from different badminton shoes. The amateur players showed a greater range of ankle movement and reverse joint moment on the frontal plane, and a larger internal joint rotation moment on the horizontal plane. The professional badminton players exhibited greater knee moment on the sagittal and frontal planes. Therefore, these factors should be considered in the development of the training program to reduce the risk of sports injuries in knee and ankle joints. This study simulates the real badminton court and calibrates the range of activities of each movement of the subjects so that the subjects complete the experimental action in a natural state with high quality. A limitation of this study is that it does not combine joint load and muscle activity. Another limitation is that the sample size is small and should be expanded in future studies. This research method can be applied to the lower limb biomechanical research of other footwork in the badminton project.

Here, we present a protocol to evaluate the differences in injury mechanisms between professional and amateur players when performing a badminton maximal right lunge movement by analyzing lower limb kinematics.

Under the condition of simulating a badminton court in the laboratory, this study used the injury mechanism model to analyze the maximal right lunge ...

3D Kinematic Gait Analysis for Preclinical Studies in Rodents

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

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

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

Low-Cost Gait Analysis for Behavioral Phenotyping of Mouse Models of Neuromuscular Disease

Measurement of animal locomotion is a common behavioral tool used to describe the phenotype of a given disease, injury, or drug model. The low-cost method of gait analysis demonstrated here is a simple but effective measure of gait abnormalities in murine models. Footprints are analyzed by painting a mouse&#8217;s feet with non-toxic washable paint and allowing the subject to walk through a tunnel on a sheet of paper. The design of the testing tunnel takes advantage of natural mouse behavior and their affinity for small dark places. The stride length, stride width, and toe spread of each mouse is easily measured using a ruler and a pencil. This is a well-established and reliable method, and it generates several metrics that are analogous to digital systems. This approach is sensitive enough to detect changes in stride early in phenotype presentation, and due to its non-invasive approach, it allows for testing of groups across life-span or phenotypic presentation.

Footprint analysis is a low-cost alternative to digitized gait analysis programs for researchers quantifying movement abnormalities in mice. Because of its speed, simplicity, and longitudinal potential, it is ideal for behavioral phenotyping of mouse models.

Measurement of animal locomotion is a common behavioral tool used to describe the phenotype of a given disease, injury, or drug model. The low-cost method ...

Examining Changes in HRV and Emotion Following Artmaking with Three Different Art Materials

This protocol enables the examination of psychological and physiological responses to different types of behavioral engagements. Specifically, in this study example, the emotional response and changes in heart rate variability are examined in response to artmaking with three different art materials that vary in their levels of fluidity. This protocol can be adapted to examine other types of behavior or engagement in artmaking with other materials. There are several benefits to using this protocol. Firstly, the order randomization of the materials improves the probability that the response measured is associated with its qualities and not the order of presentation. Secondly, the continuous measuring of electrocardiogram enables the examination of changes in heart rate variability after engagement with each art material and changes that might occur during the artmaking itself. The advantages of this protocol should be considered with their limitations. The music listening is before each art making session; thus, the return to baseline can only be measured in the first two conditions. The return to baseline provides information on how fast individuals recover after response to working with each of the materials. Furthermore, a more liquid material instead of gouache paint with a brush, such as finger paints, provides more difference between materials. Finally, this protocol can be adapted to specific research needs.

The goal of the protocol is to guide researchers in conducting experiments that are intended to measure changes in self-reported emotional response and heart rate variability following art making with different materials. The protocol can easily be adapted for use in a variety of behavioral conditions and activities.

This protocol enables the examination of psychological and physiological responses to different types of behavioral engagements. Specifically, in this ...

Measuring the Switch Cost of Smartphone Use While Walking

This paper presents a study protocol to measure the task-switching cost of using a smartphone while walking. This method involves having participants walk on a treadmill under two experimental conditions: a control condition (i.e., simply walking) and a multitasking condition (i.e., texting while walking). During these conditions, the participants must switch between the tasks related to the experimental condition and a direction determining task. This direction task is done with a point-light walker figure, seemingly walking towards the left or the right of the participant. Performance on the direction task represents the participant&#8217;s task-switching costs. There were two performance measures: 1) correct identification of the direction and 2) response time. EEG data are recorded in order to measure the alpha oscillations and cognitive engagement occurring during the task switch. This method is limited in its ecological validity: pedestrian environments have many stimuli occurring simultaneously and competing for attention. Nonetheless, this method is appropriate for pinpointing task-switching costs. The EEG data allow the study of the underlying mechanisms in the brain that are related to differing task-switching costs. This design allows the comparison between task switching when doing one task at a time, as compared to task switching when multitasking, prior to the stimulus presentation. This allows understanding and pinpointing both the behavioral and neurophysiological impact of these two different task-switching conditions. Furthermore, by correlating the task-switching costs with the brain activity, we can learn more about what causes these behavioral effects. This protocol is an appropriate base for studying the switching cost of different smartphone uses. Different tasks, questionnaires, and other measures can be added to it in order to understand the different factors involved in the task-switching cost of smartphone use while walking.

This study design measures the task-switching cost of using a smartphone while walking. Participants undergo two experimental conditions: a control condition (walking) and a multitasking condition (texting while walking). Participants switch between these tasks and a direction determining task. EEG data as well as behavioral measures are recorded.

This paper presents a study protocol to measure the task-switching cost of using a smartphone while walking. This method involves having participants walk ...

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

To cross a road successfully, individuals must coordinate their movements with moving vehicles. This paper describes use of a walking simulator in which people walk on a treadmill to intercept gaps between two moving vehicles in an immersive virtual environment. Virtual reality allows for a safe and ecologically varied investigation of gap crossing behavior. Manipulating the initial starting distance can further the understanding of a participant&#8217;s speed regulation while approaching a gap. The speed profile may be assessed for various gap crossing variables, such as initial distance, vehicle size, and gap size. Each walking simulation results in a position/time series that can inform how velocity is adjusted differently depending on the gap characteristics. This methodology can be used by researchers investigating pedestrian behavior and behavioral dynamics while employing human participants in a safe and realistic setting.

This protocol describes use of a walking simulator that serves as a safe and ecologically valid method to study pedestrian behavior in the presence of moving traffic.

To cross a road successfully, individuals must coordinate their movements with moving vehicles. This paper describes use of a walking simulator in which ...