# Upper Body Posture Recognition Using Inertial Sensors and Recurrent Neural Networks
## 4 Discussion
In addition, we observed the importance of the \"order\" of postures in trials; more specifically, postures around (changed from or into) slump and upright postures caused relatively greater classification error, as shown in green rectangles in Figure 7. This type of error was caused by the order of labeling procedures. It is difficult to define the changing point from right bending to slump postures (around the 1000 th sample in Figure 7) because upright postures are similar to the combination of these two postures. Consequently, our classifier assigned upright postures to this changing period rather than slump postures. A more appropriate method of defining ground truth is using fuzzy logic, since most human body motions are dynamic or \"quasi-static\" rather than totally static, i.e., no change in signals. Changing postures occur frequently in daily activity, and it is impossible for changes from one posture to another to occur discontinuously, as our ground truth shows in Figure 7. In addition, as a result of anatomical differences between people and unconstrained trials, upright postures become more difficult to differentiate from all other postures, and misclassification error is higher than in other classes as illustrated by accuracy and F1 scores in Table 2.  
For the number of sensors, the average classification accuracy was reduced with a decreasing number of sensors and dimensionality of training features. Accuracy and F1 scores were significantly reduced by using fewer than three sensors, demonstrating that a minimum number of three sensors were required to cope with posture recognition, especially in dynamic conditions. Among devices using three sensors, the three-sensor series (B1-B3) positioned at the upper trunk displayed greater accuracy than the three-sensor series positioned at the lower trunk (B2-B4). This is probably because the B4 sensor was positioned at L5, which remained almost static between coplanar rotations, such as right and left bending [41]. Conversely, the upper arrangement of sensors B1-B3 exhibited a higher accuracy of 98.1%, which was comparable to the accuracy of the device using four sensors, indicating an optimal arrangement in the sensing system. This study has some limitations. Firstly, we evaluated only six subjects. With more subjects, it may be possible to match user-independent subjects with a subset of overall subjects based on weight, height, and various habits of activities. This would likely allow for improved user-independent classification performance. Secondly, the average accuracy when training and testing by using 10-fold cross validation was 99.0%. When testing with the excluded subject, average accuracy decreased to 81.2%. Similar variations were also reported in other papers related to human motion classification [42; 43]; similarly, our model seems to perform adequately when recognizing similar patterns but performs imperfectly when used on all segments of a population. Further studies will be conducted on more postures and more subjects in order to develop a neural network that can achieve comparable generalization performances.