## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**ADLs**} & \multirow{2}{*}{**Falls**} & \multirow{2}{*}{**Nr. of Subjects**} & \multicolumn{2}{c}{**Gender**} & \multicolumn{2}{c}{**Age**} & \multicolumn{1}{c}{**Height**} & \multicolumn{1}{c}{**Weight**} \\ \cline{5-8} \cline{7-9}  & & & & **Female** & & **Male** & **(Years)** & **(cm)** & **(Kg)** \\ \hline DMPSBF [24] & 2015 & yes & yes & 5 & - & - & - & - & - \\ \hline Gravity [23] & 2016 & yes & yes & 2 & - & - & 26-32 & 170-185 & 63-80 \\  & & & & & & \(29\pm 4.2\) & \(178\pm 10.6\) & \(71.5\pm 12\) \\ \hline MobiFall [11] & 2014 & yes & yes & 24 & 7 & 17 & 22-47 & 160-189 & 50-103 \\  & & & & & & \(27\pm 5\) & \(175\pm 7\) & \(764\pm 14.5\) \\ \hline MobiAct [25] & 2016 & yes & yes & 57 & 15 & 42 & 20-47 & 160-193 & 50-120 \\  & & & & & & & \(25\pm 4\) & \(175\pm 4\) & \(76.6\pm 14.4\) \\ \hline RealWorld (HAR) [26] & 2016 & yes & no & 16 & 7 & 8 & 16-62 & 163-183 & 48-95 \\  & & & & & & & \(32\pm 12\) & \(173\pm 7\) & \(74.1\pm 13.3\) \\ \hline Shoaib PA [27] & 2013 & yes & no & 4 & 0 & 4 & 25-30 & - & - \\ \hline Shoaib SA [28] & 2014 & yes & no & 10 & 0 & 10 & 25-30 & - & - \\ \hline tfall [29] & 2013 & yes & yes & 10 & 7 & 3 & 20-42 & 161–184 & 54-98 \\  & & & & & & \(31\pm 9\) & \(173\pm 1\) & \(69.2\pm 13.1\) \\ \hline UCI HAR [30] & 2012 & yes & no & 30 & - & - & 19-48 & - & - \\  & & & & & & - & & \\ \hline UCI HAPT [31] & 2015 & yes & no & 30 & - & - & 19-48 & - & - \\  & & & & & & - & & \\ \hline UCI UIWADS [32] & 2013 & yes & no & 22 & - & - & - & - \\  & & & & & & - & \\ \hline UMA Fall [33] & 2016 & yes & yes & 17 & 6 & 11 & 14–55 & 155–195 & 50–93 \\  & & & & & & \(27\pm 10\) & \(172\pm 9\) & \(69.9\pm 12.3\) \\ \hline WISDM [34] & 2012 & yes & no & 29 & - & - & - & - & - \\ \hline UniMiB SHAR & 2016 & yes & yes & 30 & 24 & 6 & 18-60 & 160–190 & 50-82 \\  & & & & & & \(27\pm 11\) & \(169\pm 7\) & \(644\pm 9.7\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The publicly available datasets containing samples from smartphones sensors. The dataset has been designed keeping in mind on one side the limitations of the actual publicly available datasets, and on the other the characteristics of MobiAct, RealWorld (HAR), and UMA Fall, so to create a new dataset that juxtaposes and complements MobiAct, RealWorld (HAR), and UMA Fall with respect to the data that is missing. Thus, such a dataset would have to contain a large number of subjects (more than the 18 on average), with a large number of women (to compensate MobiAct, RealWorld (HAR), and UMA Fall), with subjects over the age of 55 (to extend the range of UMA Fall (We do not consider RealWorld (HAR) since it contains ADLs only. Indeed, it is most difficult to recruit elderly subjects performing falls), with different physical characteristics (to maintain heterogeneity), performing a wide number of both ADLs and falls (to be suitable in several contexts). Moreover, the dataset would have to contain all the information required to select subjects or ADLs and falls according to different criteria, such as for example, all the female whose height is in the range 160-168 cm, all the men whose weight is in the range 80-110 Kg, all the walking activities of the subjects whose age is in the range 45-60 years. To fulfil those requirements, we built a dataset including 9 different types of ADLs and 8 different types of falls. The dataset contains a total of 11,771 samples describing both activities of daily living (7579) and falls (4192) performed by 30 subjects, mostly females (24), of ages ranging from 18 to 60 years. Each sample is a vector of 151 accelerometer values for each axis. Each accelerometer entry in the dataset maintains the information about the subject that generated it. Moreover, each accelerometer entry has been labeled by specifying the type of ADL (e.g., walking, sitting, or standing) or the type of fall (e.g., forward, syncope, or backward).