Name: three-dimensional finger motion tracking during needling: a solution for the kinematic analysis of acupuncture manipulation
Uploaded: 2021-10-28T14:00:00.000+00:00
Duration: 8 min 27 s
Description: Watch this Scientific Journal Video about Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation at JoVE.com

This work was supported by the National Natural Science Foundation of China (Grant Number. 82174506).

This study was approved by the ethics committee of Yueyang Hospital, affiliated with Shanghai University of Traditional Chinese Medicine (reference no. 2021-062), and each participant signed an informed consent form.
1. Experiment preparations
<ol>
	<li>Camera settings:
	<ol>
		<li>Place three tripods in front of the operation table, and connect them with three cameras.</li>
		<li>Set the shooting parameters of the cameras as follows: resolution 1280 x720 pixels, format MP4, full manual mode (M), aperture F1.2, shutter 1/1000s, ISO 6400, automatic white balance, optical zoom 0mm. 
		NOTE: The angle between every two cameras is required to be set at 60&#176;-120&#176; (Figure 1A).</li>
	</ol>
	</li>
	<li>Tracking marker placement:
	<ol>
		<li>Attach seven reflective balls with a diameter of 6.5 mm on the holding-needle hand of each participant for video recording as detailed in steps 1.2.2-1.2.4 and shown in Figure 2A.</li>
		<li>Wrist: Attach one ball on the midpoint of the ulna and radial styloid defined as tracking point &#34;wrist joint &#34;(WJ)</li>
		<li>Thumb: Attach one ball each on the center of thumb nail defined as tracking point &#34;thumb tip&#34; (TT), the IP joint defined as tracking point &#34;thumb end joint&#34; (TEJ), and the metacarpophalangeal (MCP) joint defined as tracking point &#34;thumb base joint&#34; (TBJ), respectively.</li>
		<li>Forefinger: Attach one ball each on the center of the forefinger nail defined as tracking point &#34;forefinger tip&#34; (FT), the proximal interphalangeal (PIP) joint defined as tracking point &#34;forefinger middle joint&#34; (FMJ), and the MCP joint defined as tracking point &#34;forefinger base joint&#34; (FBJ), respectively.</li>
	</ol>
	</li>
</ol>
2. Video shooting and editing
<ol>
	<li>Place a small 15 cm x 15 cm x 15 cm 3D calibration frame with 8 points on the operating table for 3D calibration (Figure 1B,C).</li>
	<li>Remove the frame from the table after taking a video of the calibration frame for at least 8 s.</li>
	<li>Instruct the participants to perform AM on the acupuncture point LI11 (Quchi) of the volunteer, including lifting-thrusting and twirling skills, to control the needle to move up and down and rotate with thumb and forefinger, respectively. Take the videos of the above skills for at least 10 cycles. 
	NOTE: The inclusion and exclusion criteria of the participants to perform AM and volunteers to provide acupuncture points for needling are listed. Participant inclusion: (1) acupuncture teacher or student finished the &#34;lifting-thrusting skill&#34; and &#34;twirling skill&#34; chapter in the course textbook entitled &#39;Acupuncture and Moxibustion Techniques and Manipulations19; (2) participant should have hands-on needling experience with the human body for more than 5 times. Participant exclusion: (1) non-acupuncture teachers or students; (2) acupuncture students without any hands-on needling experience with the human body. Volunteer inclusion: (1) age between 16-60 years old; (2) no obvious skin damage, rupture, suppuration or obvious exudation around LI11 on the right arm. Volunteer exclusion: (1) individuals with a history of smoking, alcohol or drug abuse; (2) individuals with blood system diseases or obvious bleeding tendency; (3) individuals with chronic mental illness or mental disorders; (4) pregnant women; (5) individuals with a history of fainting needles.</li>
	<li>Export all the videos from the cameras to the designated disk of the computer. Rename the 3D calibration videos in cameras 1, 2, 3 as &#34;ca-1.mp4&#34;, &#34;ca-2.mp4&#34; and &#34;ca-3.mp4&#34;.</li>
	<li>Synchronize all manipulation videos in the video editing software (e.g., Adobe premiere pro) and export them named as &#34;lifting-thrusting-1.avi&#34;, &#34;lifting-thrusting-2.avi&#34;, &#34;lifting-thrusting-3.avi&#34;, &#34;twirling-1.avi&#34;, &#34;twirling-2.avi&#34; and &#34;twirling-3.avi&#34;, respectively. 
	&#8203;NOTE: Refer to Supplementary File 1 for the video synchronization instructions of the video editing software used in this study.</li>
</ol>
3. Project configuration of Simi Reality Motion System (motion capture and analysis software)
<ol>
	<li>Open the motion capture and analysis software and choose Create a New Project. Set the project name in Project Label and click Create and Save to save the project in the designated disk.</li>
	<li>Choose Specification &#62; Points &#62; Right/Left Hand and drag the above tracking points from the Predefined Points box into the Used Points box, then click on the Close button to continue. 
	NOTE: All the following steps take the tracking points of the right hand as an example.</li>
	<li>Choose Specification &#62; Connections and click on New Connection
	<ol>
		<li>Input connection name &#34;forefinger III right&#34;. Select &#34;forefinger middle joint right&#34; as the Starting Point and Line to the point &#34;forefinger tip right&#34; in the same window</li>
		<li>Click on the Apply and Close buttons to finish the establishment of the connection.</li>
	</ol>
	</li>
	<li>Add and rename the camera groups
	<ol>
		<li>Right-click on Cameras &#62; Add Camera Group to add new camera groups.</li>
		<li>Right-click on Cameras &#62; Rename to rename the camera groups as &#34;lifting-thrusting camera group&#34; and &#34;twirling camera group&#34;, respectively.</li>
	</ol>
	</li>
	<li>Right-click on the Lifting-Thrusting Camera Group &#62; Add Camera
	<ol>
		<li>Click on the Select File button in the Tracking box.</li>
		<li>Click on Open Existing File and select the operation video &#34;lifting-thrusting-1.avi&#34; in the next window, then click on Apply to finish video import.</li>
		<li>Similar to the above actions, click on Select File in the 3D Calibration box, and import the corresponding calibration video &#34;ca-1.mp4&#34;.</li>
	</ol>
	</li>
	<li>According to step 3.5, continue to import the operation videos &#34;lifting-thrusting-2.avi&#34; and &#34;lifting-thrusting-3.avi&#34;, and their corresponding calibration videos &#34;ca-2.mp4&#34; and &#34;ca-3.mp4&#34; in the Lifting-Thrusting Camera Group, respectively. 
	NOTE: There should be 3 cameras in the Lifting-Thrusting Camera Group in the project window after sections 3.4 and 3.5.</li>
	<li>According to steps 3.4, 3.5, and 3.6, import the twirling skill and calibration videos into the Twirling Camera Group.</li>
</ol>
4. Video analysis
<ol>
	<li>3D calibration for each camera
	<ol>
		<li>Expand Lifting-Thrusting Camera Group and right-click on Lifting-Thrusting-1 &#62; Properties.</li>
		<li>Click on the 3D Calibration button in the 3D Calibration box; input the description and add 8 points by clicking on Add Point button for 8 times</li>
		<li>Click on Apply after setting the name and corresponding X, Y, Z value for each point according to the calibration parameters (Table 1).</li>
		<li>After configuring all points, move the mouse to click each endpoint of the calibration video to finish the 3D calibration.</li>
		<li>Follow steps 4.1.1-4.1.4 to complete the 3D calibration of the other cameras in the same group and the cameras in the Twirling Camera Group.</li>
	</ol>
	</li>
	<li>3D finger motion tracking
	<ol>
		<li>Right-click on Lifting-Thrusting Camera Group &#62; 3D Tracking, select all the cameras, and click on the OK button to open the 3D tracking window.</li>
		<li>Set the Track Using Pattern Matching (all points) for all the cameras and manually click on all the tracking points in the first frame.</li>
		<li>Click on the Search Automatically button to start automatic 3D tracking frame by frame.</li>
		<li>Follow steps 4.2.1-4.2.3 to complete the motion tracking of the Twirling Camera Group. 
		NOTE: If a tracking point is lost during the automatic 3D tracking, select the lost point line, right-click on Discard Point From Here, then re-click the point and the Search Automatically button. Select Yes if the message &#34;No start frame for tracking has been set for 3 selected camera(s). it can be set individually in camera properties. Do you want to set start frame to frame 0 for all cameras without start frame and continue now?&#34; pops up.</li>
	</ol>
	</li>
	<li>Data export
	<ol>
		<li>Right-click on Lifting-Thrusting Camera Group &#62; New 3D Calculation, select all the cameras, and check Update Data Continuously and Store Data Explicitly in File in Create 3D Data window. Click the OK button to continue.</li>
		<li>Right-click on the folder Lifting-Thrusting-3D Coordinates Data &#62; Export, check Column Headings, Tracking Names, Start Time and Frequency, Time Information in First Column, X, Y, Z, v(X), v(Y), v(Z) in the Export window</li>
		<li>Click the Export button to export the data file (*.txt) with the customized name. Export the data file of the Twirling Camera Group in the same way.</li>
	</ol>
	</li>
</ol>
5. Data analysis
NOTE: An original PHP script is used to browse and analyze the data files exported by the motion capture and analysis software. All the source code has been shared in a GitHub repository20.
<ol>
	<li>After the data files exported from the motion capture and analysis software are uploaded to a specific server folder running this script, open the script and input the Username and Password to log in.</li>
	<li>Click on Add New Participant, select the Participant Type and Gender, and input the Participant Name, Age, and Practice Time in the pop-up page; click on Submit to finish adding a new participant.</li>
	<li>Click on Add New Record corresponding to the newly added participant in the list page, then input the Folder Name containing the uploaded data files of the motion capture and analysis software and select the Operation Date; click on Submit to continue.</li>
	<li>Click on Analysis corresponding to the newly added operation record, then select Skill and click on Submit. The script will identify and display all the valid crests and troughs for manual review. 
	NOTE: A certain crest or trough can be reselected manually in the corresponding drop-down list if the script incorrectly identifies it. Based on these crests and troughs, the average values of amplitudes and velocities along three axes of each tracking point and the operating time of lifting, thrusting, twirling left, and twirling right actions can be calculated and displayed by the script. The calculation method of these parameters is shown in Figure 3.</li>
</ol>

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The authors have nothing to disclose.

This study established the measurement method of the kinematic parameters of AM in vivo and obtained the data of motion amplitude, velocity, and operation time of the six important tracking points on the thumb and forefinger along three axes. Meanwhile, based on the 3D calibration frame, a 3D stick view and corresponding animation of the thumb and forefinger during needling were generated. The thumb and forefinger movement of AM can be fully displayed with the synchronous playback of kinematic parameter curve and stick animation, which can help researchers to explore the movement characteristics and compare the similarities and differences of different AM skills.
Throughout the entire experimental process, some critical steps that affect the results of the analysis can be summarized-first, the experimental environment configuration. The recommended temperature of the experimental environment is constant 22-25 &#176;C, and relative humidity is about 60% without obvious airflow in the room. Meanwhile, there is no strong noise and electromagnetic source interference in the surrounding environment. Second, the placements of the camera and tripod. In the process of motion tracking, all tracking points should be recorded by all cameras to obtain high-precision data. Therefore, a reasonable camera position is key to reducing experimental errors. Furthermore, the tripods should be adjusted to a proper height (higher than the table and ensure that the experimental devices on the table and the hand of the participant can be clearly recorded). Third, calibration and automatic motion tracking. All analysis data is calculated based on the position of each tracking point in the 3D calibration system in each frame of the motion video; therefore, successful calibration and automatic tracking of each point are prerequisites for performing calculations. Finally, identification of crests and troughs. The technical indicators of AM can be calculated by the positioning of the crests and troughs in each cycle. In this protocol, the steps of automatic identification and manual review are designed to ensure the accuracy of the experimental data.
In order to apply 3D motion tracking technology to the kinematic analysis of AM, two modifications were made to this technology commonly used in the large joints of human limbs. First, the customization of a small 3D calibration frame for fingers. A 15&#215;15&#215;15cm 3D calibration frame was customized for improving the measurement accuracy of finger movements. Through 3D laser scanning, the calibration accuracy of the frame is 0.01mm. Second, the establishment of technical indicators of AM and related calculation methods. According to the motion characteristics of AM and the raw data exported by the motion tracking system, three technical indicators, &#34;amplitude, velocity, and time&#34; along three axes were established for each finger tracking point. These parameters can be calculated by PHP script based on the inflection point recognition of the coordinate-time curve. The possible crests and troughs can be identified according to the logical expression (1) and (2), respectively.
<img alt="Equation 1" src="/files/ftp_upload/62750/62750eq01.jpg" />&#160; (1) 
<img alt="Equation 2" src="/files/ftp_upload/62750/62750eq02.jpg" />&#160; (2)
Where dc, dt and dt2 are the differentiations of coordinate value, time and time squared, d2c is the quadratic differentiation of coordinate. According to the test results of experimental sample data, two types of thresholds were set for verifying the validities of these crests and troughs. The time threshold is 80% of the average operating cycle, the crest and trough thresholds are 75% and 25% of the maximum operating amplitude. After traversing all the crests and troughs, the crest whose interval time from the previous crest is greater than the time threshold and coordinate value is greater than the crest threshold is identified as the valid crest. The trough whose interval time from the previous crest is greater than the time threshold and coordinate value is less than the trough threshold is identified as the valid trough. Although, in most cases, the crests and troughs can be identified automatically, there are still a few cases that need to be adjusted manually. Therefore, as the main limitation of this solution, the recognition algorithm needs to be improved in future work. The preliminary analysis of the experimental data showed that the movement amplitude and velocity of MCP joints were the smallest, and the related parameters of IP or PIP joint and fingertips were larger and largest, respectively. Moreover, the needle body was driven by the vertical or tangential movement of the fingertips to move up and down or rotate on a fixed axis. In summary, AM is a kind of rhythmic movement performed by fingertips driven by MCP joints of the thumb and forefinger. Moreover, no matter which AM skill was used, a certain range of movement occurred along three axes at all tracking points, which suggests that during the operation of the lifting-thrusting skill, although the fingertips mainly move in the vertical direction, it is still accompanied by a tangential coupled movement, and the tangential-based twirling skill is also accompanied by a vertical coupled movement. These results indicate that the AM is not a simple single-axis movement.
Similar to other studies that use this technology to analyze finger motion, the motion tracking technology in this protocol also provides three-axis kinematics data of finger joints with high accuracy22. However, a secondary analysis on raw data according to the skill characteristics of AM was performed, and corresponding technical indicators were established in this protocol for further comparative analysis. Furthermore, compared with the portable, easy-to-use and low-cost hand motion tracking devices such as Leap Motion, standard marker-based motion tracking analysis has the advantages of higher accuracy and wider application range23,24. Compared with the traditional AM analysis device ATP-II, the amplitude-time curve along the main motion axis derived from motion tracking analysis and the voltage-time curve derived by ATP-II have significant conformity in the same AM skill. Moreover, the operating cycles calculated by the two measurement methods were also relatively consistent. These results showed that this experimental method can not only reflect similar skill characteristics to that of ATP-II but also provide more kinematics parameters along three axes of multiple tracking points, which cannot be measured by previous experimental technology.
This experimental method provides an efficient way for analyzing complicated movements of fingers involved in AM. It has great potential for future applications. First, the study of the dose-effect relationship of acupuncture. 3D finger motion tracking technology provides a solution for determining the stimulation amount of manual acupuncture and can be used to carry out studies such as the correlation analysis between needling velocity, amplitude and therapeutic effect, so as to provide more scientific data support for the clinical application of acupuncture. Second, the quantitative evaluation and feedback for the teaching and learning of AM. The results from data analysis combined with the teacher&#39;s verbal feedback can help learners adjust their finger actions and reduce the cognitive load24,25. Previous studies have used the data provided by 3D motion tracking technology to improve the effect of motor skills learning, such as repetitive overarm throwing26 and musical performance27,28. Some reports also showed that medical skills such as colonoscopy29, laparoscopic30, arthroscope31 and other endoscope32,33 could also be enhanced with this technology. And another study suggested that the video-based self-reflection and discussion with learners engaging at a higher cognitive level than the standard descriptive feedback34. Third, the measurement and preservation of famous acupuncturists&#39; AM. Because all the AM is collected, recorded, and analyzed based on motion videos stored in the database, these videos and relevant data of AM can be browsed by researchers at any time for further learning and inheritance.
The establishment of this experimental method opens up a new way for the quantitative research of AM. In the future, more camera positions, higher-definition lenses, and Higher precision calibration frames can be applied to further improve data accuracy and dig out more meaningful technical indicators to provide more data reference for the clinical application, education, and promotion of acupuncture.

As a kind of the clinical skills of traditional Chinese medicine (TCM) and physical stimulation, acupuncture manipulation (AM) is often regarded as an important factor that affects the therapeutic effect of acupuncture1,2. Many studies have confirmed that different AM or different stimulation parameters (needling velocity, amplitude, frequency, etc.) of the same AM resulted in different therapeutic effects3,4,5,6,7. Therefore, the measurement of relevant kinematic parameters of AM and correlation analysis with the therapeutic effect can provide useful data support and reference for the clinical treatment with acupuncture8,9.
The measurement of kinematic parameters of the AM began in the 1980s10. In the early days, the electrical signal conversion technology based on variable resistance was mainly used to convert the displacement signal of the needle body into a voltage or current signal for displaying and recording the amplitude and frequency data of AM11. Moreover, the famous ATP-II Chinese medicine acupuncture technique tester II (ATP-II) with this technology currently has been used by many traditional Chinese medicine universities of China12. After that, with the continuous development and innovation of sensor technology, different types of sensors were used to collect kinematic parameters of AM. For example, the three axes electromagnetic motion sensor was attached to the needle handle to acquire needling amplitude and velocity13; the bioelectric signal sensor was placed on the dorsal horn of the animal&#39;s spinal cord to record needling frequency14, etc. Although the quantitative research of AM based on the above two types of technologies has completed the acquisition of relevant kinematic parameters during needling, its main disadvantages are the inability to perform the real-time non-invasive measurement and the change of operating feel caused by the modification of the needle body.
In recent years, motion tracking technology was gradually applied to the quantitative research of AM15,16. Because it is based on the frame-by-frame analysis of needling video, the measurement of acupuncture parameters can be acquired during in vivo operation without modifying the needle body. This technology has been used to measure the kinematic parameters such as amplitude, velocity, acceleration, and frequency of four tracking points of thumb and forefinger during needling in a two-dimensional (2D) plane and established the corresponding finger stick figure15. Some studies also measured the angle change range of interphalangeal (IP) joint of thumb and forefinger with similar technology9,17,18. However, the current studies on AM analysis are still mainly limited to the 2D motion plane, and the number of tracking points is relatively small. So far, there is no complete three-dimensional (3D) kinematics measurement and analysis method for AM, and no related data was published.
To solve the above problems, this study will use 3D motion tracking technology to measure the kinematic parameters of the seven tracking points of hand during needling. This protocol aims to provide a complete technical solution for the kinematic analysis on AM, as well as the further study on the dose-effect correlation of acupuncture.

<table><tbody><tr><td>3D calibration frame</td><td>Any brand</td><td></td><td>15 x 15 x 15 cm</td></tr><tr><td>Acupuncture needles</td><td>Suzhou Medical Appliance Factory</td><td></td><td>0.35 x 40 mm</td></tr><tr><td>Double-sided tape</td><td>Any brand</td><td></td><td>Round, 1 cm-diameter</td></tr><tr><td>Reflective balls</td><td>Simi Reality Motion Systems GmbH</td><td></td><td>6.5 mm-diameter</td></tr><tr><td>SD card</td><td>Western Digital Corporation</td><td></td><td>SDXC UHS-I</td></tr><tr><td>SD card reader</td><td>UGREEN Group Limited</td><td></td><td>USB 3.0</td></tr><tr><td>Simi Motion</td><td>Simi Reality Motion Systems GmbH</td><td></td><td>Ver.8.5.15</td></tr><tr><td>Swab</td><td>Any brand</td><td></td><td>The volume fraction of ethanol is 70%-80%</td></tr><tr><td>Three cameras</td><td>Victor Company of Japan, Limited</td><td></td><td>JVC GC-PX100BAC</td></tr><tr><td>Three tripods</td><td>Any brand</td><td></td><td></td></tr></tbody></table>

three-dimensional finger motion tracking during needling: a solution for the kinematic analysis of acupuncture manipulation

Three-dimensional (3D) motion tracking has been used in many fields, such as the researches of sport and medical skills. This experiment aimed to use 3D motion tracking technology to measure the kinematic parameters of the joints of fingers during acupuncture manipulation (AM) and establish three technical indicators "amplitude, velocity and time". This method can reflect the operation characteristics of AM and provide quantitative parameters along three axes of multiple finger joints. The current evidence shows that the method has great potential for future applications such as the study of the dose-effect relationship of acupuncture, teaching, and learning of AM, and the measurement and preservation of famous acupuncturists' AM.

After establishing this experimental method, the lifting-thrusting and twirling skills of basic AM of nineteen acupuncture teachers from the School of Acupuncture-Moxibustion and Tuina of Shanghai University of TCM were measured using 3D motion tracking. According to the definition of a joint coordinate system (JCS) for the shoulder, elbow, wrist, and hand proposed by the Standardization and Terminology Committee (STC) of the International Society of Biomechanics21, seven finger tracking points have been selected. The stick view generated by the motion capture and analysis software based on the anatomical positions of these points is shown in Figure 2B. The typical coordinate-time curves along three axes of each point are shown in Figure 4, and two videos of lifting-thrusting and twirling skills with stick view (Video 1 and Video 2).
As shown in Figure 4C,E, because of the minimal movement amplitude along the main motion axes during different skills (the Z-axis of lifting-thrusting skill and the Y-axis of twirling skill) of the wrist joint (WJ) can be fixed, and the movement seems to occur from the thumb and index finger. Therefore, the data of the other six points were exported by the motion capture and analysis software for further kinematic analysis of AM. After data analysis, the average values of amplitude and velocity along three axes and the operating time of the action &#34;lifting&#34;, &#34;thrusting&#34;, &#34;twirling left&#34; and &#34;twirling right&#34; of each tracking point on fingers were calculated and shown in Table 2, Table 3 and Table 4.
In addition, the finger motion of participants was also tracked when they performed AM on ATP-II. The data derived from ATP-II was compared with the data exported by the motion capture and analysis software. The results show that the shape of the coordinate-time curve of TT along the Z-axis was similar to the voltage-time curve generated by ATP-II during the lifting-thrusting skill. Meanwhile, during the twirling skill, the shape of the amplitude-time curve along the Y-axis of TT was also similar to the voltage-time curve of ATP-II. Furthermore, after calculation, the average operating cycles of these two types of curves were basically the same (Figure 5).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62750/62750fig01.jpg" /> 
Figure 1: Camera positions and the placement of 3D calibration frame. (A) The positions of three cameras. (B) Front view of 3D calibration frame. (C) Top view of 3D calibration frame. <a href="https://www.jove.com/files/ftp_upload/62750/62750fig01large.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/62750/62750fig02.jpg" /> 
Figure 2: The positions of tracking markers and their stick view. (A) The positions of tracking markers on hand. (B) The stick view generated by the motion capture and analysis software based on the anatomical positions of these points. <a href="https://www.jove.com/files/ftp_upload/62750/62750fig02large.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/62750/62750fig03.jpg" /> 
Figure 3: Schematic diagram of calculation method of kinematic parameters. The average amplitude and velocity can be calculated based on curve crest and trough positioning. <a href="https://www.jove.com/files/ftp_upload/62750/62750fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/62750/62750fig04.jpg" /> 
Figure 4: Typical coordinate-time curves during lifting-thrusting and twirling skills. (A,B,C) The typical coordinate-time curves along the X-, Y-, Z-axis of each tracking point during the lifting-thrusting skill, respectively. (D,E,F) The curves with the same settings of lifting-thrusting skill during twirling skill. <a href="https://www.jove.com/files/ftp_upload/62750/62750fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/62750/62750fig05.jpg" /> 
Figure 5: Comparison of the curves generated by ATP-II and motion capture and analysis software. (A) Finger motions of participants were tracked when they performed AM on ATP-II. (B) The voltage-time curve of ATP-II during the lifting-Thrusting skill. (C) The coordinate-time curve along the Z-axis of TT during the lifting-thrusting skill. (D) The voltage-time curve of ATP-II during twirling skill. (E) The coordinate-time curve along the Y-axis of TT during twirling skill. <a href="https://www.jove.com/files/ftp_upload/62750/62750fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Table 1: Coordinate parameters of the calibration points. The coordinate values of three axes of eight calibration points. <a href="https://www.jove.com/files/ftp_upload/62750/Table 1.xlsx" target="_blank">Please click here to download this Table.</a>
Table 2: Kinematics data of each tracking point during the lifting-thrusting skill. The average values of amplitude and velocity along three axes of each tracking point on figures during the lifting-thrusting skill. <a href="https://www.jove.com/files/ftp_upload/62750/Table 2.xlsx" target="_blank">Please click here to download this Table.</a>
Table 3: Kinematics data of each tracking point during twirling skill. The average values of amplitude and velocity along three axes of each tracking point on figures during twirling skill. <a href="https://www.jove.com/files/ftp_upload/62750/Table 3.xlsx" target="_blank">Please click here to download this Table.</a>
Table 4: Operating time during lifting-thrusting and twirling skills The average values of operating time in the processes of lifting, thrusting, twirling left, and twirling right actions <a href="https://www.jove.com/files/ftp_upload/62750/Table 4.xlsx" target="_blank">Please click here to download this Table.</a>
Video 1: Lifting-thrusting skill. (Top left) The stick view of the hand. (Top right, Bottom left, Bottom right) The typical coordinate-time dynamic curve along the X-, Y-, Z-axis of each tracking point during the lifting-thrusting skill <a href="https://www.jove.com/files/ftp_upload/62750/Lifting-thrusting skill.mp4" target="_blank">Please click here to download this Video.</a>
Video 2: Twirling skill: The stick view of the hand and typical coordinate-time dynamic curves with the same settings as Video 1 during the twirling skill. <a href="https://www.jove.com/files/ftp_upload/62750/Twirling skill.mp4" target="_blank">Please click here to download this Video.</a>
Supplementary File 1: Video synchronization instructions. Screenshots and steps of video synchronization instructions of the video editing software used in this study. <a href="https://www.jove.com/files/ftp_upload/62750/Instruction of video synchronization in Adobe premiere pro.pdf" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation at JoVE.com

Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation

This experimental method describes a solution for the kinematic analysis of acupuncture manipulation with three-dimensional finger motion tracking technology.

Three-dimensional (3D) motion tracking has been used in many fields, such as the researches of sport and medical skills. This experiment aimed to use 3D ...

three-dimensional-finger-motion-tracking-during-needling-solution-for

Nanyang Technological University

Research

JoVE Journal

Medicine

2.7K Views.  Shanghai University of Traditional Chinese Medicine. This experimental method describes a solution for the kinematic analysis of acupuncture manipulation with three-dimensional finger motion tracking technology.

Video: Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation

Visualizing Motion Patterns in Acupuncture Manipulation

Acupuncture manipulation varies widely among practitioners in clinical settings, and it is difficult to teach novice students how to perform acupuncture manipulation techniques skillfully. The Acupuncture Manipulation Education System (AMES) is an open source software system designed to enhance acupuncture manipulation skills using visual feedback. Using a phantom acupoint and motion sensor, our method for acupuncture manipulation training provides visual feedback regarding the actual movement of the student's acupuncture manipulation in addition to the optimal or intended movement, regardless of whether the manipulation skill is lifting, thrusting, or rotating. Our results show that students could enhance their manipulation skills by training using this method. This video shows the process of manufacturing phantom acupoints and discusses several issues that may require the attention of individuals interested in creating phantom acupoints or operating this system.

Here, we present a protocol for using the Acupuncture Manipulation Education System (AMES) in the training of acupuncture manipulation skills using phantom acupoints.

Acupuncture manipulation varies widely among practitioners in clinical settings, and it is difficult to teach novice students how to perform acupuncture ...

Behavior

Three-Dimensional Ultrasonic Needle Tip Tracking with a Fiber-Optic Ultrasound Receiver

Ultrasound is frequently used for guiding minimally invasive procedures, but visualizing medical devices is often challenging with this imaging modality. When visualization is lost, the medical device can cause trauma to critical tissue structures. Here, a method to track the needle tip during ultrasound image-guided procedures is presented. This method involves the use of a fiber-optic ultrasound receiver that is affixed within the cannula of a medical needle to communicate ultrasonically with the external ultrasound probe. This custom probe comprises a central transducer element array and side element arrays. In addition to conventional two-dimensional (2D) B-mode ultrasound imaging provided by the central array, three-dimensional (3D) needle tip tracking is provided by the side arrays. For B-mode ultrasound imaging, a standard transmit-receive sequence with electronic beamforming is performed. For ultrasonic tracking, Golay-coded ultrasound transmissions from the 4 side arrays are received by the hydrophone sensor, and subsequently the received signals are decoded to identify the needle tip&#39;s spatial location with respect to the ultrasound imaging probe. As a preliminary validation of this method, insertions of the needle/hydrophone pair were performed in clinically realistic contexts. This novel ultrasound imaging/tracking method is compatible with current clinical workflow, and it provides reliable device tracking during in-plane and out-of-plane needle insertions.

Accurate and efficient visualization of invasive medical devices is extremely important in many ultrasound-guided minimally invasive procedures. Here, a method for localizing the spatial position of a needle tip relative to the ultrasound imaging probe is presented.

Ultrasound is frequently used for guiding minimally invasive procedures, but visualizing medical devices is often challenging with this imaging modality. ...

Engineering

Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact

The measurement of dynamic scapular kinematics is complex due to the sliding nature of the scapula beneath the skin surface. The aim of the study was to clearly describe the acromion marker cluster (AMC) method of determining scapular kinematics when using a passive marker motion capture system, with consideration for the sources of error which could affect the validity and reliability of measurements. The AMC method involves placing a cluster of markers over the posterior acromion, and through calibration of anatomical landmarks with respect to the marker cluster it is possible to obtain valid measurements of scapular kinematics. The reliability of the method was examined between two days in a group of 15 healthy individuals (aged 19-38 years, eight males) as they performed arm elevation, to 120&#176;, and lowering in the frontal, scapular and sagittal planes. Results showed that between-day reliability was good for upward scapular rotation (Coefficient of Multiple Correlation; CMC = 0.92) and posterior tilt (CMC = 0.70) but fair for internal rotation (CMC = 0.53) during the arm elevation phase. The waveform error was lower for upward rotation (2.7&#176; to 4.4&#176;) and posterior tilt (1.3&#176; to 2.8&#176;), compared to internal rotation (5.4&#176; to 7.3&#176;). The reliability during the lowering phase was comparable to results observed during the elevation phase. If the protocol outlined in this study is adhered to, the AMC provides a reliable measurement of upward rotation and posterior tilt during the elevation and lowering phases of arm movement.

This report presents details of how to adopt the acromion marker cluster method of obtaining scapular kinematics when using a passive marker motion capture device. As has been described in the literature, this method provides a robust, non-invasive, three-dimensional, dynamic and valid measurement of scapular kinematics, minimizing skin movement artifact.

The measurement of dynamic scapular kinematics is complex due to the sliding nature of the scapula beneath the skin surface. The aim of the study was to ...

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-dimensional computed tomography (4DCT) provides a series of volume data and visualizes joint motions. However, numerical analysis of 4DCT data remains difficult because segmentation in all volumetric frames is time-consuming. We aimed to analyze joint kinematics using a sequential 3D-3D registration technique to provide the kinematics of the moving bone with respect to the fixed bone semiautomatically using 4DCT DICOM data and existing software. Surface data of the source bones are reconstructed from 3DCT. The trimmed surface data are respectively matched with surface data from the first frame in 4DCT. These trimmed surfaces are sequentially matched until the last frame. These processes provide positional information for target bones in all frames of the 4DCT. Once the coordinate systems of the target bones are decided, translation and rotation angles between any two bones can be calculated. This 4DCT analysis offers advantages in kinematic analyses of complex structures such as carpal or tarsal bones. However, fast or large-scale motions cannot be traced because of motion artifacts.

We analyzed joint kinematics from four-dimensional computed tomography data. The sequential 3D-3D registration method semiautomatically provides the kinematics of the moving bone with respect to the subject bone from four-dimensional computed tomography data.

Four-dimensional computed tomography (4DCT) provides a series of volume data and visualizes joint motions. However, numerical analysis of 4DCT data ...

Bioengineering

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

Kinematic analysis is a powerful method for objective assessment of upper extremity movements in a three-dimensional (3D) space. Three-dimensional motion capture with an optoelectronic camera system is considered as golden standard for kinematic movement analysis and is increasingly used as outcome measure to evaluate the movement performance and quality after an injury or disease involving upper extremity movements. This article describes a standardized protocol for kinematic analysis of drinking task applied in individuals with upper extremity impairments after stroke. The drinking task incorporates reaching, grasping and lifting a cup from a table to take a drink, placing the cup back, and moving the hand back to the edge of the table. The sitting position is standardized to the individual&#39;s body size and the task is performed in a comfortable self-paced speed and compensatory movements are not constrained. The intention is to keep the task natural and close to a real-life situation to improve the ecological validity of the protocol. A 5-camera motion capture system is used to gather 3D coordinate positions from 9 retroreflective markers positioned on anatomical landmarks of the arm, trunk, and face. A simple single marker placement is used to ensure the feasibility of the protocol in clinical settings. Custom-made Matlab software provides automated and fast analyses of movement data. Temporal kinematics of movement time, velocity, peak velocity, time of peak velocity, and smoothness (number of movement units) along with spatial angular kinematics of shoulder and elbow joint as well as trunk movements are calculated. The drinking task is a valid assessment for individuals with moderate and mild upper extremity impairment. The construct, discriminative and concurrent validity along with responsiveness (sensitivity to change) of the kinematic variables obtained from the drinking task have been established.

This protocol describes an objective method to evaluate the movement performance and sensorimotor function of the upper extremity applied to individuals with stroke and healthy controls. A standardized test procedure, kinematic analysis and outcome variables for three-dimensional motion capture of drinking task are provided.

Kinematic analysis is a powerful method for objective assessment of upper extremity movements in a three-dimensional (3D) space. Three-dimensional motion ...

Neuroscience

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

The shoulder is one of the human body's most complex joint systems, with motion occurring through the coordinated actions of four individual joints, multiple ligaments, and approximately 20 muscles. Unfortunately, shoulder pathologies (e.g., rotator cuff tears, joint dislocations, arthritis) are common, resulting in substantial pain, disability, and decreased quality of life. The specific etiology for many of these pathologic conditions is not fully understood, but it is generally accepted that shoulder pathology is often associated with altered joint motion. Unfortunately, measuring shoulder motion with the necessary level of accuracy to investigate motion-based hypotheses is not trivial. However, radiographic-based motion measurement techniques have provided the advancement necessary to investigate motion-based hypotheses and provide a mechanistic understanding of shoulder function. Thus, the purpose of this article is to describe the approaches for measuring shoulder motion using a custom biplanar videoradiography system. The specific objectives of this article are to describe the protocols to acquire biplanar videoradiographic images of the shoulder complex, acquire CT scans, develop 3D bone models, locate anatomical landmarks, track the position and orientation of the humerus, scapula, and torso from the biplanar radiographic images, and calculate the kinematic outcome measures. In addition, the article will describe special considerations unique to the shoulder when measuring joint kinematics using this approach.

Biplane videoradiography can quantify shoulder kinematics with a high degree of accuracy. The protocol described herein was specifically designed to track the scapula, humerus, and the ribs during planar humeral elevation, and outlines the procedures for data collection, processing, and analysis. Unique considerations for data collection are also described.

The shoulder is one of the human body's most complex joint systems, with motion occurring through the coordinated actions of four individual joints, ...

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

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

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

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

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

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

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

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

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Finger gestures are a critical element in human communication, and as such, finger gesture recognition is widely studied as a human-computer interface for state-of-the-art prosthetics and optimized rehabilitation. Surface electromyography (sEMG), in conjunction with deep learning methods, is considered a promising method in this domain. However, current methods often rely on cumbersome recording setups and the identification of static hand positions, limiting their effectiveness in real-world applications. The protocol we report here presents an advanced approach combining a wearable surface EMG and finger&#160;tracking system to capture comprehensive data during dynamic hand movements. The method records muscle activity from soft printed electrode arrays (16 electrodes) placed on the forearm as subjects perform gestures in different hand positions and during movement. Visual instructions prompt subjects to perform specific gestures while EMG and finger positions are recorded. The integration of synchronized EMG recordings and finger tracking data enables comprehensive analysis of muscle activity patterns and corresponding gestures. The reported approach demonstrates the potential of combining EMG and visual tracking technologies as an important resource for developing intuitive and responsive gesture recognition systems with applications in prosthetics, rehabilitation, and interactive technologies. This protocol aims to guide researchers and practitioners, fostering further innovation and application of gesture recognition in dynamic and real-world scenarios.

The paper presents a comprehensive protocol for simultaneously recording hand-electromyography (EMG) and visual finger tracking during natural finger gesturing. The visual data is designed to serve as the ground truth for the development of accurate EMG-based computational models for finger gesture recognition.

Finger gestures are a critical element in human communication, and as such, finger gesture recognition is widely studied as a human-computer interface for ...

Targeted Tuina Stimulation Protocol in Lumbar Region of IDD Model Rabbits

Operational and Intervention Effects of Targeted Tuina in Lumbar Intervertebral Disc Degeneration Model Rabbits

Tui Na or massage therapy alleviates symptoms related to intervertebral disc degeneration (IDD). However, precise, repeatable, standardized instructions for Tuina manipulation are lacking. This study establishes IDD model rabbits induced by fibrous ring puncture, creates targeted Tuina stimulation protocols at the acupuncture points in the lumbar region, and describes in detail the operation methods and requirements of kneading, pointing, and flicking. New Zealand male white rabbits (n = 15) were selected and randomly divided into a blank group, a model group, and a Tuina group. The rabbits in the model group and the Tuina group were molded by fibrous ring puncture; the rabbits in the model group were only immobilized on the operating table without treatment. In contrast, the Tuina group used the "8N/10N, 30 cycles/min" prescription for kneading, pointing, and flicking to perform the intervention, using tactile sensory aids to monitor and regulate the intensity of the Tuina operation. Imaging diagnosis and pathological tests were used to assess the effect of Tuina in rabbits, and the results showed improved imaging features and significantly lowered pathology scores of lumbar disc degeneration in the Tuina group compared to the model group (P &lt; 0.01). Targeted Tuina in the lumbar region may be beneficial in the alleviation of lumbar disc degeneration, but further verification is needed. By regularly performing Tuina and recording the mechanical information involved enables reproducible manipulation prescriptions and helps to observe the basic features of the underlying mechanism of Tuina for IDD.

The finger tactile measurement system quantifies tactile pressure on the finger using a highly sensitive electrosurgical pressure sensor, with software displaying and accurately recording the pressure data and video in real time. Two analysis modules unify the data processing for thumb tracking and conditioning during Tuina.

Tui Na or massage therapy alleviates symptoms related to intervertebral disc degeneration (IDD). However, precise, repeatable, standardized instructions ...