## 2 Dataset Description
### ADLs and Falls  
In order to select both the ADLs and the falls, we analyzed the datasets in Table 1 and the most recent publicly available datasets recorded with wearable ad-hoc devices. As discussed in Section 1, we considered the datasets acquired from 2012 to be compliant with the year of the older smartphone-based dataset. This set includes, sorted by year of creation from the oldest the most recent, the following datasets: DLR v2 [37], Ugulino [38], USC HAD [39], DaLiAc [10], EvAAL [40], MHEALTH [41], UCI ARSA [32], BaSA [42], UR Fall Detection [43], MMSys [9], SisFall [44], UMA Fall (UMA Fall contains samples from both smartphones and ad-hoc wearable devices.) [23], and REALDISP [45]. Regarding ADLs, Figure 1 shows the most common ones in the overall 24 datasets we analyzed (11 with samples from smartphones and sketched in Table 1 and 13 with samples from wearable ad-hoc devices listed above). The y axis represents the number of datasets that include the specified ADL. ADLs are grouped by category. The following categories have been identified by analizyng the datasets: Context-related, which includes activities that someway deal with the context (e.g., Stepping in a car), Motion-related, which includes activities that imply some kind of physical movement (e.g., Walking), Posture-related, which includes activities in which the person maintains the position for a certain amount of time (e.g., Standing), Sport-related, which includes any kind of activity that requires a physical effort (e.g., Jumping), and Others, which includes activities that are presented in one dataset only (e.g., Vacuuming in category Housekeeping-related). The Jogging and Running activities deserve a clarification. In all the datasets we analyzed, they are mutually exclusive, that is, datasets that contain Running, do not contain Jogging and vice versa. The datasets REALDISP and MHEAL are an exception because they include both the activities. These datasets, besides being realized by the same institution, are primarily oriented towards the recognition of physical activities (warm up, cool down and fitness exercises). Moreover, none of the datasets analyzed exactly specify what the Jogging and Running activities are related to. Thus, even though they may be considered very similar activities, we have decided to keep them separated in oder to do not loose their specificity. We classify Jogging as a sport-related activity (in the sense, for instance, of jogging in the park), and Running as a motion-related activity (in the sense, for instance, of running for the bus). For each category, the \(x\) axis shows all the ADLs we found and that are present in at least 2 datasets. Under the label Others fall all the ADLs for the corresponding category that have been included in one dataset only (e.g., Walking left-circle in category Motion-related). Table 2 shows the 9 ADLs we have selected among the most popular included in the analyzed publicly available datasets. UniMiB SHAR includes the top 5 most popular Motion-related activities (i.e., Walking, Going upstairs, Going downstairs, Sitting down, and Running). Moreover, we detailed the generic Standing up, by including the Standing up from sitting and Standing up from laying activities. Finally, we included also the Lying down from standing. In the Sport-related category, we did not included Jogging even if it is the most popular activity in its category because we included the Running activity in the Motion-related category. In Sport-related category, we chose the Jumping activity being the second most popular one. Our dataset does not include Postural-related activities. Indeed, we were interested in acquiring acceleration data from activities related to movements both because from them it is possible to estimate the overall physical activity performed by a person, and because people are more likely to fall during movements [46].