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Related Areas: Ambient Intelligence

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, there are only a few publicly available data sets, which often contain samples from subjects with too similar characteristics, and very often lack specific information so that is not possible to select subsets of samples according to specific criteria.

UniMiB SHAR, is 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 performed, 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 5-fold cross-validation (i.e., including samples from all the subjects in both the training and the test dataset) and a leave-one-subject-out cross-validation (i.e., the test data include the samples of a subject only, and the training data, the samples of all the other subjects).

Dataset & Code

The dataset and the code to replicate our results is freely downlodable from here.

The Android application used to acquire the dataset is available for download here.


To get in touch with us about UniMiB SHAR, please send an email to:


If you use the dataset and/or code, please cite this paper (downloadable from here):

    AUTHOR = {Micucci, Daniela and Mobilio, Marco and Napoletano, Paolo},
    TITLE = {UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones},
    JOURNAL = {Applied Sciences},
    VOLUME = {7},
    YEAR = {2017},
    NUMBER = {10},
    ARTICLE NUMBER = {1101},
    URL = {},
    ISSN = {2076-3417},
    DOI = {10.3390/app7101101}

People: Paolo Napoletano Marco Mobilio Daniela Micucci

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