## 1 Introduction  
In recent years, researchers have been increasingly interested in systems for human activity monitoring based on machine learning techniques. In particular, the scientific community is paying particular attention to the definition and experimentation of techniques that use inertial signals captured by wearable devices to automatically recognize activities of daily living (ADLs) [1] and/or promptly detect falls [2; 3]. These techniques have proven to be effective in many application domains, such as physical activity recognition and the estimation of energy expenditure [4; 5], the monitoring of [PERSON]'s disease development [6], and the early detection of dementia disease [7]. Very recently, these techniques have been embedded in smartphone applications and rely on data acquired by the hosted sensors [8]. For instance, the applications by [PERSON] et al. [9], [PERSON] et al. [10], [PERSON] et al. [11], and [PERSON] et al. [12] promptly detect falls; the application by [PERSON] et al. [13] classifies ADLs; and the application by [PERSON] [14] both detects falls and classifies ADLs. The success of these techniques depends on the effectiveness of the machine learning based algorithm employed, which results in being penalized by at least two factors: (i) the lack of an adequate number of public datasets to evaluate the effectiveness; and (ii) the lack of tools to be used to record labeled datasets and thus increase the number of available datasets. Despite the strong interest in publicly available datasets containing labeled inertial data, their number is still limited. [PERSON] et al. (2015) and [PERSON] et al. (2016) analyze the state of the arts and provide a nearly complete list of those currently available, which includes, for example, UniMiB SHAR (2016), MobiAct (2017), and UMAFall (2018). For this reason, many researchers experiment with their techniques using ad hoc built datasets that are rarely made publicly available (2019; 2021; 2020). This practice makes it difficult to compare in an objective way the several newly proposed techniques and implementations because of a lack of a common source of data (2016; 2021; 2022; 2023). Almost none of the applications used to collect data are made publicly available (2019). To the authors' knowledge, the only exception is that used in the Gravity project, which, however, records falls only (2018). Thus, we searched for applications that also record inertial data in well-established digital marketplaces, such as Android's Google Play Store (2016), Apple's App Store (2017), and Microsoft's Windows Phone App Store (2018). We decided to focus on this market because we were interested in smartphones and smartwatches, and thus we neglected all the applications, such as that presented in (2019), that take into consideration wearable devices not equipped with a mobile operating system. In the marketplaces we explored, we found some applications for registration, but all of them presented some limitations, for example, the kind of sensors used as the data source (e.g., G-sensor Logger (2019)), the lack of support for labeling (e.g., Sensor Data Logger (2019)), and the possibility of recording only one type of activity at each registration (e.g., Sensor Log (2021)). In this article, we present UniMiB AAL (Acquisition and Labeling), an Android suite that provides support for the acquisition of signals from sensors and their labeling. The suite is available for download from the following address [[http://www.sal.disco.unimib.it/technologies/unimib-aal/](http://www.sal.disco.unimib.it/technologies/unimib-aal/)]([http://www.sal.disco.unimib.it/technologies/unimib-aal/](http://www.sal.disco.unimib.it/technologies/unimib-aal/)) (2021). The suite can manage the sensors hosted by both an Android smartphone and, if present, a paired Wear OS smartwatch.