  Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are
being increasingly used to monitor human activities. Data acquired by the
hosted sensors are usually processed by machine-learning-based algorithms to
classify human activities. The success of those algorithms mostly depends on
the availability of training (labeled) data that, if made publicly available,
would allow researchers to make objective comparisons between techniques.
Nowadays, publicly available data sets are few, often contain samples from
subjects with too similar characteristics, and very often lack of specific
information so that is not possible to select subsets of samples according to
specific criteria. In this article, we present a new dataset of acceleration
samples acquired with an Android smartphone designed for human activity
recognition and fall detection. The dataset includes 11,771 samples of both
human activities and falls performed by 30 subjects of ages ranging from 18 to
60 years. Samples are divided in 17 fine grained classes grouped in two coarse
grained classes: one containing samples of 9 types of activities of daily
living (ADL) and the other containing samples of 8 types of falls. The dataset
has been stored to include all the information useful to select samples
according to different criteria, such as the type of ADL, the age, the gender,
and so on. Finally, the dataset has been benchmarked with four different
classifiers and with two different feature vectors. We evaluated four different
classification tasks: fall vs no fall, 9 activities, 8 falls, 17 activities and
falls. For each classification task we performed a subject-dependent and
independent evaluation. The major findings of the evaluation are the following:
i) it is more difficult to distinguish between types of falls than types of
activities; ii) subject-dependent evaluation outperforms the
subject-independent one
