# Multiple Sensors Based Hand Motion Recognition Using Adaptive Directed Acyclic Graph
## 4 Experiment and Validation
### Different Sensor Based Recognition  
In our research, we assess the recognition rates of ten hand motions captured from the combined sensors, as shown in Figure 7. The proposed method gives a high average recognition rate of 94.57%, indicating the capability of the proposed hand motion recognition algorithm. The recognition rates are different for each motion. Specifically, we find that the ADAG method presented a perfect performance when identifying motion 1 (grasp and lift a book using five fingers with the thumb abduction) and motion 3 (grasp and lift a can full of rice using five fingers with the thumb abduction). For motion 2 (grasp and lift a can full of rice using thumb, index finger, and middle finger only), motion 4 (grasp and lift a big ball using five fingers), motion 5 (grasp and lift a disc container using thumb and index finger only), motion 6 (uncap and cap a pen using thumb, index finger, and middle finger), motion 7 (open and close a pen box using five fingers) and motion 8 (pick up a pencil using five fingers, flip it and place it on the table), all of the accuracies are up to 90%. In this case, the algorithm also reveals an excellent performance.  
There are some reasons for good results. Firstly, more feature information is extracted to better capture hand movements for recognition. Secondly, five-quick-grasp and four-quick-grasp models identified using peak-detection algorithm, are utilized in the experiments to protect the integrity of raw data. Thirdly, the optimal kernels and parameters obtained will be conducive to gaining a better recognition rate, as well as the standard deviation of the accuracy.  
Figure 6: Hand motions including grasps and in-hand manipulations.  
Figure 8 shows the comparative experimental results and their variances of eight subjects using different sensor types. For different subjects performing all motions, the EMG based has the lowest average recognition rate of 85.55%, the data glove based average recognition rate is 88.30%, while the fingerTPS based average recognition rate is 89.20%. Compared with the results by using a single sensor, multi-sensor based has the highest accuracy. However, the recognition time increased because the extracted keypoints increased from hand motions. On the other hand, the identification of motion 9 (hold and lift a dumbbell) and 10 (grasp and lift a cup using thumb, index finger, and middle finger) also performs well, but it indicates the relatively lower discrimination compared with other motions. The main reason may be caused by the noise when collecting the EMG and finger angle information during two-hand cooperation and complex in-hand manipulation.  
The recognition rates of different motions, as well as different sensor types, have been analysed through the above experiments. Different subjects exhibit significant individual variation in the same hand motion, such as the diversities of the applied acceleration, force, etc. Hence, it is necessary to  
Figure 8: Comparative experimental results.  
Figure 7: Confusion matrix for the ten motions using ADAG, where the total accuracy is 94.57%.  
analyse the effects on the motion acquisition and recognition. Figure 9 describes the identification results of the same motion based on different subjects from the combined sensors. For different subjects performing all motions, we can clearly see that the difference of recognition rate is very apparent. The majority of them can get the average recognition rate of up to 90%, mainly caused by less training sample size and correct manipulation following the model. However, hand motion recognition from subject 7 has the worse recognition rates due to the misclassification of motions 5 and 6 with 22% and 46% error rates, respectively. The main reason for this result is the non-standard manipulation of motions 5 and 6. Hence, the influence of different subjects on the final experimental results can not be ignored.