## 1 Introduction
However, most prior research either deals with a restricted range of emotions or has marginally poor classification accuracies and hence cannot be employed in practical applications [27; 42; 43]. The work at hand presents a CNN-RNN-based deep learning model, which can predict emotions from human gait using low-level kinematic signals. To train the model, we used an inertial-based emotions dataset collected in one of our previous studies [19]. An autobiographical memories paradigm was employed for emotional stimuli to collect the emotional data. A chest-mounted smartphone with on-board inertial sensors (tri-axial accelerometer and tri-axial gyroscope) was used to capture gait data from 40 healthy volunteers for six fundamental human emotions, including anger, happy, disgust, sad, fear, and surprise. Subjects were instructed to recollect a prior significant incident and walk straight twice for 10-meters back and forth for each emotion. This resulted in a 40-m walk for each mood. During the design of the deep model, a key factor was reducing the model's complexity to speed up the learning process. To this end, we proposed the computation of the magnitude of 3D accelerations (\(\widehat{mag_{a}}\)) from the raw signals in the pre-processing step. This reduces the input space from 3D to 1D. Similarly, we compute the magnitude of 3D angular velocities (\(\widehat{mag_{\omega}}\)) from the raw signals, which reduces the input space from 3D to 1D. The proposed model was separately trained and validated with \(\widehat{mag_{a}}\) and \(\widehat{mag_{\omega}}\). For the sake of comparison, the proposed model was validated with a 3D input space (3D accelerations and 3D angular velocities) as well as a 6D input space (6D accelerations and angular velocities). During experimentation, it was observed that the reduction of input size to 1D (\(\widehat{mag_{a}}\) or \(\widehat{mag_{\omega}}\)) significantly reduces the computation time as compared to 3D or 6D input sizes. Furthermore, the proposed methodology also outperforms existing state-of-the-art approaches. The following are the significant contributions of this work:  
* We present a CNN-RNN deep architecture (inspired by InceptionResNet CNN and BiGRU models) where we proposed a novel approach of dense connections employing 1x1 convolutions from raw input to the BiGRU model (see Section 4);* We proposed a reduction in the input space by utilising 1D magnitudes of 3D accelerations and 3D angular velocities (\(\overrightarrow{mag_{a}}\), \(\overrightarrow{mag_{\omega}}\)), which result in lower processing requirements as well as better results (Sections 4 and 5);
* The proposed deep model was trained and evaluated with different input sizes (1D-6D) for the sake of comparison and the best results are reported (Section 5);
* The proposed model was compared with the existing approaches and it was observed that the proposed model outperforms the state-of-the-art approaches (Section 6.1). The rest of the article is organised as follows: Section 2 discusses related literature, Section 3 describes the dataset used in this study; Section 4 explains the methodology including pre-processing, the proposed model, the training procedure, and the model's complexity; Section 5 presents the results computed with different input sizes, and Section 6 concludes the article.