## 5 Conclusions  
Almost all publicly available datasets from smartphones do not allow the selection of samples based on specific criteria related to the physical characteristics of subjects and the activities (and/or falls) they performed. Of the 11 datasets containing smartphone measurements, only MobiAct, RealWorld (HAR), and UMA Fall are the exception. These three datasets include more men than women. Considering only datasets that include falls (MobiAct and UMA Fall), the maximum age of the subjects is 47 years. Our goal was therefore to create a new dataset that would be complementary  
Figure 5: Confusion matrix of the AF-17 classification achieved with k-Nearest Neighbour (k-NN). \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & \multicolumn{6}{c}{_Leave-One-Subject-Out_} \\ \hline  & \multicolumn{4}{c}{**Raw Data**} & \multicolumn{4}{c}{**Magnitude**} \\ \hline Data & KNN & SVM & ANN & RF & KNN & SVM & ANN & RF \\ \hline AF-17 & 52.14 & 55.15 & 48.00 & **56.53** & 52.14 & 55.09 & 48.00 & **56.58** \\ AF-2 & 92.90 & **97.57** & 95.41 & 97.02 & 92.90 & **97.57** & 96.07 & 97.05 \\ A-9 & 63.79 & 63.32 & 63.63 & **73.17** & 63.79 & 63.36 & 63.63 & **72.67** \\ F-8 & 43.66 & **48.84** & 38.50 & 45.88 & 43.66 & **49.35** & 38.50 & 45.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Leave-one-subject-out. Mean Average Accuracy for each classification task using raw data and magnitude of the signal as feature vectors. In bold the best results for each classification task and feature vector employed. to the more complete ones and that also include falls. The result is UniMiB SHAR dataset that includes 9 ADLs and 8 falls performed by 30 humans, mostly female, with a huge range of ages, from 18 to 60 years. The classification results obtained on the proposed dataset showed that the raw data performs somewhat better than magnitude as feature vector in the case of subject-dependent evaluation, and, on the opposite, the magnitude performs quite better than raw data in the case of subject-independent evaluation. The classification of different types of activities is simpler than the classification of different types of falls. It is very easy to distinguish between falls and no falls for both raw data and magnitude. The subject-independent evaluation showed that recognition performance strongly depends on the subject data. As future work we plan to include other alternative artificial neural network architectures such as Recurrent Neural Networks (RNNs) and Deep Convolutional Neural Networks (CNNs). An RNN, especially its variant built on Long short-term memory (LSTM) units, is well suited to model temporal dynamics. Deep Convolutional Neural Networks (CNNs) are a class of learnable architectures used in many domains such as image recognition, image annotation, image retrieval etc. The combination of a CNN and RNN has demonstrated to be very effective for activity recognition [61]. UniMiB SHAR dataset will permit researchers to study several issues, such as: (i) robust features to deal with falls; (ii) robust features and classification schemes to deal with personalization issues. We are planning to carry out an evaluation of the state-of-the-art techniques for ADLs recognition on both UniMiB SHAR and all the publicly available datasets of accelerometer data from smartphone to have and objective comparison. Moreover, we have planned to make experimentation on personalization by using those datasets that include information about the characteristics of the subjects. We want to investigate whether the training set containing samples acquired by subjects with similar characteristics to the testing subject may result in a more effective classifier. Finally, we are planning to check if and how data from smartwatches and smartphones can jointly improve the performances of the classifiers. To this end, we are improving the data acquisition application used for UniMiB SHAR.