The cost function consisted of the sum of normalized mean square error and normalized maximum error with movement range of seven finger movements.
Due to the limitations of the vision-based finger movement measurement system, the wearable glove system was proposed [7,8,9]. In the wearable glove system, sensors were installed on the support worn on the hand to identify the rotation of the joints, making the accuracy of the system higher than that of the vision-based system. With a bend sensor system, natural finger movement was limited by the glove sensor.
On the other hand, the soft sensor based finger motion measurement system did not restrict the natural motion of the fingers. On the other hand, the systems shown in Figure 1.4 (a) and (c) did not restrict the natural movement of the fingers. Therefore, it is necessary that the system does not interfere with the movement of the fingers and can measure the movements of the thumb and the abduction and adduction of the other four fingers.
The number of sensors is minimized to measure high-DOF finger motion, including thumb movements and abduction and adduction of the other four fingers in a limited space. In Chapter 4, a soft sensor system was fabricated as a single sensor sheet, and real-time finger motion measurement was performed with the proposed soft sensor system.
Manufacturing of sensors and performance verification
A kinematic structure of the hand
First, the kinematic definition of the index and middle finger MCP joint is shown in Figure 2.3. And the y-axis (Yre) of the reference frame is the unit vector which is the origin to MCP joint of the middle finger. The x-axis of the reference frame (Xre) is the result of cross product Yre and Zre.
The cross product of Vin1 and Vin2 is the z-axis of the index frame (sin). The x-axis of the index frame (xin) is the cross product of yin and sin. And the center of the CMC joint is the origin of the CMC frame (OCMC).
The z-axis of the CMC frame (zCMC) is the cross product of vcmc1 and vcmc2. The thumb angle CMC R is the angle of rotation of yCMC relative to the ZreXre plane.
Strain measurement and analysis in the dorsum of the hand
The complex postures of index AA and middle AA were scanned with five different postures. And the complex pose of index FE and middle FE were scanned with five different poses. The candidate location is set as point-to-point with a length of 10 mm to 20 mm in a neutral position.
The first strain group is the 10 mm (10 mm) group which includes candidate locations with a length of 10 mm or more and less than 15 mm. The second group is the 15 mm group (15 mm) which includes the candidate location with a length of 15 mm more and less than 20 mm. The last group is the 20 mm group (20 mm) which includes the candidate location with a length of 20 mm.
The performance of the soft sensor was verified at a high percentage stretch, such as 80% stretch or more than 100% stretch due to the advantage of stretchability. But most of the tension in the back of the hand is less than 10%, and the maximum tension is about 45%.
Verification of sensor performance
Before the verification of the performance of the soft sensor, the relationship between the load of the sensor and the meter must be checked. Strain of gauge strain means the strain of the eGaIn part, which is a red box in Figure 2.9. The entire process was captured on video by the microscope to calculate the strain on the gauge and sensor.
The gauge and sensor deformation was calculated based on the image length ratio captured from the microscope video. Then we marked the part of the sensor and the part of the deformation with a line. Therefore, the sensor strain and gauge strain were the same under 20% strain.
The resolution of a strain sensor is the smallest strain detected by the strain sensor, so the normalized resistance of the sensor does not overlap in resolution. In other words, the strain value that is a normalized resistance probability density function with non-overlapping 95% confidence intervals is the resolution of the soft sensor. The probability density function of each step in all displacements was calculated and plotted against % strain in an error bar as shown in Figure 2.12.
All sensors can detect the same change, a displacement of 0.03 mm, but the resolution of the strain sensor is the smallest strain detected by the sensor. The measuring factor is the slope of the normalized resistance with respect to elongation and related to sensitivity. Least squares linear regression to the linear equation of the curve was performed to correlate the normalized resistance with the strain of the sensor.
The normalized resistance and the linear model of the sensor were plotted against time. Sensitivity is the gauge factor (GF), which is the slope of the linear model (black dotted line in Figure 2.16). The hysteresis with respect to the total variation of the sensor is below 10%, then it can be used to measure finger movement .
Optimization of the sensor location in dorsum of the hand
- Method overview
- Sensor location optimization
- Experimental verification
- Decoupling algorithm
The system with an error of less than 5 degrees of the CMC AA and CMC R is sufficient to measure the finger movement in the clinical industry  and thus the accuracy criteria of the movements with similar ROM of the CMC AA and CMC R, namely index and center AA is less than 5 degrees maximum error and less than 2.5 degrees RMSE. On the other hand, the ROM of MCP FE motion is about two times larger than the ROM of AA motion, so the criterion of the FE motion is below 10 degrees of maximum error and below 5 degrees of RMSE. It is difficult to maintain exactly the same position of the sensor while measuring finger movement.
If the sensors overlap, it is difficult to attach the sensor to the back of the hand. Therefore, considering the minimum sensor size, 10 mm is the sensor overlap criterion. The method is a standard approach to approximate the solution of systems by minimizing the sum of squared errors.
The maximum error is the maximum absolute value of the gap in the real value, the finger joint angle and the estimated value. One term which is the MSE term of the cost function is related to the target motion estimation, so the REME represents how the sensor angle estimation will perform under static and dynamic loading. But the results said that the estimated results of 5 sensors had the lowest error.
The overlap of the sensor occurred in the position with the lowest price in 6 sensors, thus the 5 sensors had the lowest price. The starting point was located 5 mm at the top of the wrist and 5 mm next to the straight line connecting the point between the middle finger and the ring finger with the wrist. The result of the optimization was verified by comparing the angle measured by a camera-based motion capture system and the angle estimated by the sensor signal.
The calibration movements were complicated and not accurate movements because the calibration was done with motion capture markers. The plot of the estimated angle not met was divided by finger movement, and maximum error and RMSE were calculated in each section. If the move is a combination move consisting of the more than two moves, the error was plotted with respect to each move that makes up the combination move.
The results of the verification including the decoupling algorithm were the comparison of the measured angle with the motion capture system and the estimated angle from the sensor signal. The reason that the gap between the maximum error and RMSE of FE angle estimation was larger than the gap between the maximum error and RMSE of AA angle estimation.
Development of the sensor system for finger 3D motion measurement
Fabrication of sensor system
And the lead wires in sensor 3, 4 and 5 are good to pass the zone related to the index and middle finger movements. The zone related to the finger movement means that the load zone is affected by the finger movement. Thus, the positions of the top 5 stems in each movement were plotted on the markers as shown in Figure 4.1.
After fabricating the sensor sheet, the area near the sensor was trimmed to reduce the dependence on the sensor.
Accuracy of the sensor system
Real-time measurement of finger motion
Conclusion and open issues
34;Planimetry study of the percentage of body surface represented by the hand and palm: sizing of irregular burns is performed more accurately with the palm." Motion controller in healthy adults. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA, 26-30 May 2015; p.
A Novel All-in-One Manufacturing Process for a Soft Sensor System and Its Application to a Soft Sensing Glove.