UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones

Understand

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.