## 2 Related Work  
This section discusses some of the available mobile applications for data acquisition and labeling. The search was performed on Google Scholar and Google Play Store, on July 15, 2018, considering applications available from 2015 to the end of 2017 with the following keywords: \"sensor recording\", \"sensor log\", \"activity recognition\", and \"sensor labeling activity\". In the case of Google Scholar, we also added to these the keyword \"Android\". We looked for applications that are able to collect and label signals from sensors embedded in a smartphone (and from a possibly paired wearable device), that provide the opportunity to select the sensors to be used during recording, and that may support the user by showing the sequence of activities he/she has to execute to be compliant with the design of a dataset collecting campaign. Thus, in order to be considered, the applications had at least the following characteristics, which made them usable and complete: (i) compliance with the human-computer interaction (HCI) guidelines [35] in order to guarantee both the applications's correct use and its correct use by the subjects; (ii) provision of the possibility to record signals from a wearable device in order to collect more signals or signals from different positions; (iii) a selectable pool of sensors to be used for recording in order to choose those most suitable for the current acquisition campaign; (iv) in the case of acquisition campaigns with a sequence of predefined activities that the subjects have to perform, help provided to the user proposing time after time the activity that he/she must carry out, in order to relieve the user from having to remember the protocol (i.e., the sequences of activities) of the acquisition campaign; and finally; (v) labeling and acquisition activities executed in different devices to obtain signals and assigned labels as synchronous as possible with the exact activity carried out by the subject. Executing the two activities on different devices allows the user not to have to move the device that acquires when he/she specifies the beginning and the end of each activity from its position relative to the device used for labeling. We clearly did not consider applications that do not store the acquired signals, because these were useless for our scope. We considered only applications that make the acquired data available for offline processing related to the experimentation of activity recognition techniques. Moreover, we did not exclude from our research applications that record signals only, that is, applications that do not provide a built-in labeling feature. At the end of the research, we found only seven applications that met the requirements. Table 1 summarizes their characteristics. We include the UniMiB AAL suite in the table in order to compare it with the applications we found. Table 1 includes the following information:  
* _App_: The name of the application and its reference (i.e., the URL (Uniform Resource Locator) used to download the application or a reference to the article presenting the application). * _Google Play Store_: The availability of the application on Google Play Store. * _Source code_: The availability of the source code of the application. * _Multi-devices (Wear OS)_: Support for recording sensors mounted on external devices such as smartwatches equipped with Wear OS. * _Multi-sensors_: The possibility of recording data acquired from a set of sensors that are not only the inertial sensors. * _Sensor selection_: The possibility to choose which sensors to use to record data. * _Labeling_: The possibility to apply labels to the recorded data. * Acq. & Lab. )_: The possibility to run the acquisition and the labeling activities on different devices. * _List of activities_: The possibility of supporting the user with the list of activities he/she has to perform (i.e., the protocol defined by the acquisition campaign if defined).