# Upper Body Posture Recognition Using Inertial Sensors and Recurrent Neural Networks
## 4 Discussion  
This study demonstrated a process of data labeling and investigated unconstrained movements to improve HAR research. Sitting postures investigated in this study are sustained for a relatively long time in daily life. Hence, this protocol not only simplifies time-consuming procedures but is also appropriate for sitting postures detection. In Table 2, the highest and lowest accuracies were 99.3 and 98.3% for lean and upright postures, respectively. These results show the importance of postural features in this application. Upright postures, as described in the data labeling section, served as the \"labeling midpoint\" for many posture-changing phases, and our model showed the poorest accuracy on this class. In contrast, holding lean or bending postures results in large tilt/bending angles in sagittal and coronal planes, respectively, and these changes allow IMUs to collect detectable changes when compared to other postures. Accelerometers have been widely used in human motion analysis [16; 17; 19]. In this study, we used acceleration data from IMUs to conduct data labeling. Prior to processing classification, 28 intermittent phases were extracted by using a static-dynamic alternating sequence with approximately 5 s each. During posture transition, the signal was divided into two parts and assigned to the previous posture and the next posture, respectively, in order to enhance the model's general application. However, this form of labeling for such transition phases is imprecise and causes misclassifications for obscure definitions of posture, e.g., upright postures during changes from right bending to lean postures, as the third green rectangle shows in Figure 8. For real-world sitting posture correction devices, upright postures are more important to classify than compared to other postures. Misidentification of upright postures (such as when transitioning from one posture to another) may be an issue with our labeling method and can be fixed by adding more explanations about upright postures before trials. In this study, we used an LSTM-RNN to conduct sequence-to-sequence classification. High classifier accuracy indicates the effectiveness of this IMUs-RNN scheme for sitting posture detection. In addition, the model exhibited a higher classification accuracy for flexion/extension (lean postures) than for axial rotation (twisting postures) due to the larger motion range of sensors during flexion/extension. This is related to the sensors' axis; twisting supplies rotation to all sensors along our axis, especially the lowest sensor. However, signals for other axes did not change significantly when compared to other postures, and this small difference caused relatively poorer performance in these classes. However, this model showed slight overfitting. As shown in Figure 7, when subjects were in a static state, the model recognized postures more accurately when compared to dynamic transition states as mentioned by [PERSON] et al. [40]. Some techniques of feature extraction were suggested for solving this problem; for example, an averaging filter can condense signals in dynamic periods with parts of signals in static periods to improve the  
Figure 8: F1 scores of the neural network using different sensor combinations. accuracy of a shorter period. Further investigations of feature selection or preprocessing are not discussed in this article. In addition, even though seven postures were performed by one single subject, differences of velocity, travel path, and moved distance between trials added randomness to the training dataset. Due to the fact that movements were unconstrained and, therefore, natural and despite a sample of five research subjects, we believe that the resulting data represent a significant portion of the range of normal spinal postures, which is, of course, limited to a certain extent.