Preprint Article Version 2 This version not peer reviewed

UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

Version 1 : Received: 5 June 2017 / Approved: 6 June 2017 / Online: 6 June 2017 (07:59:54 CEST)
Version 2 : Received: 14 July 2017 / Approved: 18 July 2017 / Online: 18 July 2017 (13:16:10 CEST)

How to cite: Micucci, D.; Mobilio, M.; Napoletano, P. UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones. Preprints 2017, 2017060033 (doi: 10.20944/preprints201706.0033.v2). Micucci, D.; Mobilio, M.; Napoletano, P. UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones. Preprints 2017, 2017060033 (doi: 10.20944/preprints201706.0033.v2).

Abstract

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 smartphone accelerometer dataset designed for activity recognition. The dataset includes 11,771 activities performed by 30 subjects of ages ranging from 18 to 60 years. Activities are divided in 17 fine grained classes grouped in two coarse grained classes: 9 types of activities of daily living (ADL) and 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 two different classifiers and with different configurations. The best results are achieved with k-NN classifying ADLs only, considering personalization, and with both windows of 51 and 151 samples.

Subject Areas

smartphone accelerometers; dataset; human activity recognition; fall detection

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