## 5 Discussion
#### Limitations and Future Work  
Among the five prediction classes, further processing could be applied to the \"wheelchair\" class. As a matter of fact, it was easy to misclassify it with static or walking activities, due to acceleration pattern similarities. Our performance showed a low precision for the original test set and a high value for recall of the wheelchair class (0.41 \(\pm\) 0.23 precision, 0.92 \(\pm\) 0.12 recall for SHS participants, original test set). Frequently, slow walking activities were wrongly classified as wheelchair. A possible future improvement to \"wheelchair\" precision could be to apply post-processing steps to the predicted \"wheelchair\" class. For example, contextualizing the single \"wheelchair\" segment with the surrounding ones (i.e., within a certain number of consecutive \"walking\" segments, if \"wheelchair\" is detected, that prediction will likely be wrong, and therefore, post-processed as \"walking\"). Further steps could also consider prediction contextualization for all classes, either through post-processing or by adding specific layers to the deep-learning model (i.e., recurrent layer). Additionally, future studies should collect an higher amount of \"wheelchair\", \"stairs ascent\", and \"stairs descent\" data, given the imbalance of such data classes used in these studies. Our application used accelerometer sensors; however, multiple studies have combined together different sensor modalities belonging to Inertial Measurement Unit technology, involving measurements of accelerometer, gyroscope, and sometimes, magnetometer data. [PERSON] et al. [40] proposed a method that merges accelerometer and gyroscope data into an activity image. They used CNN power and obtained outstanding accuracy performances related to three different public datasets. Using these sensors could be helpful, respectively, for different types of activities. For example, stairs ascending and descending performances could be improved using gyroscope or barometer data, while more dynamic activities, such as walking, rely on acceleration data. In most circumstances, acceleration measurements primary lead the activity classification, while gyroscope data have a secondary support role [41]. In spite of the fact that more signals and sensors could be integrated, we focused our research on a single triaxial accelerometer-based solution. This approach has the  
Figure 9: Walking activities of 0.8 km/h (top) and 4.0 km/h (bottom) and the corresponding applied rotations of twenty and sixty degrees (\(g=9.81\) km/s\({}^{2}\)). advantage of being easily applicable to any device that contains an accelerometer, without the need to re-design its components. Additionally, it maintains low power consumption. The used model architecture, CNN, lies within the most common DNN-based approaches [11]. Despite many advantages and progresses made through DNN-based models, multiple challenges still apply to these techniques, such as their explainability and generalization capabilities compared with models built on extracted features from the respective knowledge domain [42]. Considering the SHS study, within the context of a patient-monitoring solution, using DL capabilities proved to be effective and promising [12]. Through this study, we examined how rotations could impact DL algorithms and how data augmentation address for this aspect. This challenge might be found also for other ML approaches that are orientation-dependent (e.g., orientation dependent features, three-axis acceleration). Future work should focus on comparing the augmented DL approach with other techniques, such as HMM, or feature-based models [43, 44]. The good performances of the augmented model obtained during cross-validation were confirmed by the holdout data results. This indicates that our model can well generalize using unseen data, i.e., participants.