## 5 Discussion  
The context of HAR is broad and with multiple fields of application, making it hard, sometimes, to compare studies due to the diversity in the selected activities, environment conditions, target population, and chosen metrics [34]. Our study was focused on simulated activities that may characterize a hospitalized patient wearing a device with freedom of movement (i.e., pendants or inside a pocket), localized in the upper part of the patient's body. Thanks to data augmentation, the HAR model was able to learn additional configurations not provided by the initial dataset. As Figure 8 shows, the false negative percentage red area covered by the augmented model was significantly smaller compared to the green area belonging to the baseline model. Additionally, the red area kept the error profile low and stable while the applied rotations increased. To choose the augmented rotation ranges to be applied to the training data, performances obtained from two different sets, shown in Figure 6, were evaluated: despite the seven rotations between 0 and 180 degrees being sparse, they allowed the model to better learn device configurations characterized by higher applied rotations. In light of these considerations, a rotation range from 0 to 180 degrees was chosen for training data augmentation. To the best of our knowledge, few research studies addressed data augmentation of acceleration signals; therefore, expanding this research field and its potential applications could be of relevant interest in this knowledge domain. The study by [PERSON] et al. [17] addressed data augmentation according to a specific physical constraint; in particular, it allows sensor movement only on a certain trajectory dictated by the arm's degrees of freedom. In contrast, our applied augmentation does not follow physical constraints. In fact, it includes rotations that could easily happen when using sensors pending from the body in patient monitoring devices (i.e., the PortraitTM Mobile, by GE Healthcare [35], or the IntelliVue MX40 by Philips [36]), such as for example: the up-side down flipping of the device (i.e., 180 degrees on the frontal axis), inclined device due to body shape (i.e., small rotations along the frontal axis), and inclined device due to asymmetric position of the pending rigid support (small rotations along sagittal axis). Collecting data spanning from many orientation configurations is highly time- and computationally expensive; from this perspective, data augmentation could represent an optimal approach to deal with this aspect and to increase overall performance. In Table 4, the main augmentation-related studies found in the literature are reported, along with the considered sensors and their positions, the applied augmentations, and the identified activities within the proposed framework. Figure 8: False negative percentage profiles of treadmill activities (i.e., walking class) for rotations applied to each axis separately for baseline and augmented models. The top line corresponds to the maximum profile for that rotation; the bottom line corresponds to the minimum profile for that rotation; the black dotted lines correspond to the profile median value. In accordance with the literature, our initial results confirmed that device displacement might cause significant performance loss when using sensor orientation-dependent models. The error rate steeply rises even with small rotations (i.e., 5 degrees applied to the frontal axis \(\approx\) 10%; 10 degrees applied to the frontal axis \(\approx\) 20% for 0.8 km/h from Figure 5). False negative percentages of static activities did not increase when rotations were applied (i.e., 5 and 10 degrees applied to the frontal axis \(\approx\) 5% for \"Sitting edge of the bed\" from Figure 5). As a result of the stable acceleration pattern, the model was able to recognize and classify this behavior as static activity. On the other hand, treadmill-related activity results showed an error rise as the applied rotation increased over the frontal and sagittal axis (X- and Z-axis).