## 1 Introduction
On the contrary, smartphones are good candidate devices for hosting fall detection systems: they are are widespread and daily used by a very large number of person, included elderly people. This, on the one hand, reduces costs, and on the other, eliminates the problem of having to learn how to use a new device. Moreover, studies demonstrated that samples from smartphones sensors (e.g., accelerometer and gyroscope) are accurate enough to be used in clinical domain, such as ADLs recognition [22]. This is also confirmed by the amount of publications that rely on the use of smartphones as acquisition devices for fall detection systems [18; 23] and ADLs recognition. For these reasons we concentrate our attention on smartphones as acquisition devices both for ADL recognition and fall detection. Thus, we searched the publicly available datasets acquired with smartphones in order to identify their strengths and weaknesses so as to outline an effective method for carrying out a new acquisition campaign. We searched the most common repository (IEEE, ACM Digital Library, Google, and Google Scholar) by using in our query the terms ADL dataset and Fall dataset in combination with the following words smartphone, acceleration, accelerometer, inertial, IMU (Inertial Measurement Unit), sensor, and wearable. We selected the first 100 results for each query. Removing duplicate entries, we obtained less then 200 different references. Then we manually examined the title, the abstract, and the introduction to eliminate references unrelated to ADL recognition and fall detection, and references that were based on ambient sensors such as camera, microphones, or RFID (Radio Frequency Identification) tags. We then read carefully the remaining references and discarded those that do not make publicly available the dataset used in the experimentation. Finally, we added the relevant references that we missed with our searches but were cited in the papers we selected. At the end of the process, we individuated 13 datasets with data from smartphones and 19 with data from wearable ad-hoc sensors. We then included only those datasets that have been recorded starting from 2012 (We considered the year in which the dataset has been made available. This year does not necessarily coincide with the year in which the related article has been published) mostly because the oldest dataset including samples from smartphones is dated 2012. This choice makes the datasets homogeneous with respect to the sensors technologies related to acquisition sensors which rapidly evolves year by year. At the end of the process, we individuated 13 datasets with data from smartphones and 13 with data from wearable ad-hoc sensors. In the following, we will detail some relevant characteristics of the 13 datasets from smartphones since our aim was to build a new dataset containing acceleration patters from smartphone able to complement the existing ones. As it will presented in Section 2, the datasets from ad-hoc wearable devices have be examined with the aim of identifying the most common ADLs and falls. Table 1 shows the publicly available datasets recorded by means of smartphones and their characteristics. Table 1 also includes the dataset we realized in the last row, in order to ease the comparison. The total number of datasets decreases to 11 because MobiAct and UCI HAPT are updated versions of MobiFall and UCI HAR respectively. Thus, in the following we will refer to 11 datasets overall, discarding MobiFall and UCI HAR. The 11 datasets have been recorded in the period 2012 to 2016 (column Year). Only 5 datasets out of 11 contain both falls (column Falls) and ADLs (column ADLs). The average number of subjects for dataset is 18 (column Nr. of subjects). The datasets that specify the gender of the subjects (which are MobiAct, RealWorld (HAR), Shoaib PA, Shoaib SA, tfall, and UMA Fall) contain in mean 6 women and 13 men (columns Gender-Female and Gender-Male respectively). DMPSBFD, UCI UIWADS, and WISDM do not specify the age of the subjects (column Age).