Acupuncture manipulation is well-known as an important basis for acupuncture treatment. The quantitative analysis of acupuncture manipulation can provide data support for its clinical application. The establishment of this movement method is based on three-dimensional motion tracking technology.
It opens up a new way for the quantitative research of acupuncture manipulation. This experimental method provides a solution for the stimulation amount of determination of manual acupuncture and the quantitative evaluation for the teaching and the learning of acupuncture manipulation. To begin, take seven reflective balls with a diameter of 6.5 millimeters for attachment on the participant's holding needle hand.
Attach one ball on the midpoint of the ulna and radial styloid defined as tracking point wrist joint and one ball at the metacarpophalangeal joint defined as tracking point thumb base joint, one ball at the interphalangeal joint defined as tracking point thumb end joint, attach one ball on the center of thumbnail defined as tracking point thumb tip. Next, attach one ball at the metacarpophalangeal joint defined as tracking point forefinger base joint. Attach one ball at the proximal interphalangeal joint defined as tracking point forefinger middle joint, and one ball on the center of the fore fingernail defined as tracking point forefinger tip.
For 3D calibration, place a small 3D calibration frame with eight points on the operating table. Remove move the frame from the table after taking a video of the calibration frame for at least eight seconds. Instruct the participant to perform acupuncture manipulation on the acupuncture joint LI11 of the volunteer, including lifting-thrusting and twirling skills to control the needle to move up and down and rotate with thumb and forefinger.
Next, export the videos from the cameras to the designated disk of the computer and rename the 3D calibration videos in camera one as ca-1.mp4. And similarly, rename the videos of cameras two and three. Synchronize all manipulation videos in the video editing software and export them named as lifting-thrusting-1.
avi, lifting-thrusting-2. avi, lifting-thrusting-3. avi, twirling-1.
avid, twirling-2. avi, and twirling-3. avi respectively.
Open the motion capture an analysis software and choose create a new project. Set the project name and project label and click create and save to save the project in the designated disk. Choose the specification followed by points, then select right hand and drag the tracking points from the predefined points box into the used points box.
Click on the close button to continue. Next, select specification followed by connections, then click on the new connection and input connection name as forefinger III right. Select the forefinger middle joint right as the starting point and forefinger tip right as the line two point in the same window.
Click on the apply and close buttons to finish the establishment of the connection. For adding the new camera groups, right-click on the cameras and select add camera group. Then again, right-click on the cameras, select rename to rename the camera groups as lifting-thrusting camera group and twirling camera group respectively.
Right-click on the lifting-thrusting camera group, select add camera and in the tracking box, click on the select file button. Then click on the open existing file, select the operation video lifting-thrusting-1. avi in the next window and click apply to finish the video import.
Likewise, import the corresponding calibration video ca-1. mp4 by clicking on the select file in the 3D calibration box and continue importing other operational and corresponding calibration videos. Next, import the twirling skill and calibration videos into the twirling camera group similar to the video import in the lifting-thrusting camera group.
For 3D calibration for each camera, expand the lifting-thrusting camera group, right-click on the lifting-thrusting 1 and select properties. In the 3D calibration box, click on the 3D calibration button, then input the description and add eight points by clicking the add point button eight times. Set the name and corresponding XYZ value for each point according to the calibration parameters and click apply.
After configuring all points, click each endpoint of the calibration video to finish the 3D calibration and similarly complete the 3D calibration of the other cameras in the same group and the cameras in the twirling camera group. For 3D finger motion tracking, right-click on the lifting-thrusting camera group, select 3D tracking, select all the cameras, and click OK to open the 3D tracking window. Set the track using pattern matching all points for all the cameras and manually click on all the tracking points in the first frame.
Click on the search automatically button to start automatic 3D tracking frame by frame. And similarly, complete the motion tracking of the twirling camera group. To export the data, right-click on the lifting-thrusting camera group, select new 3D calculation, select all the cameras, check update data continuously and store data explicitly in file and create 3D data window, then click OK to continue.
Next, right-click on the folder lifting-thrusting camera group 3D coordinates, select export to open the export window, and check column headings, tracking names, start time and frequency, time information in the first column X, Y, Z, VX, VY, VZ parameters. Click the export button to export the data file with the customized name and export the twirling camera group data file in the same way. In the present study during lifting-thrusting and twirling skills, the typical coordinate time curves along three axes of each point were recorded.
The preliminary analysis of the experimental data showed that the movement, amplitude, and velocity parameters of the metacarpophalangeal joints were the smallest, larger for the interphalangeal joints, and largest for the proximal interphalangeal joints. Because of the minimal movement, the amplitude along the main motion axis during different skills of the wrist joint could be fixed and the movement seemed to occur from the thumb and index finger. Upon comparison of the data derived from ATP2 and the data exported by the motion capture and analysis software, it was found that the shape of the coordinate time curve of TT along the z-axis was similar to the voltage time curve generated by ATP2 during the lifting-thrusting skill.
Meanwhile, during twirling skill, the shape of the amplitude time curve along the y-axis of TT was also similar to the voltage time curve of ATP2 and the average operating cycles of these two types of curves were the same. During motion tracking, all the tracking points should be identified correctly to obtain high-precision data. Pattern matching algorithm is recommended for automatic 3D tracking to located points rapidly.
