A naturalistic 3D acceleration-based activity dataset & benchmark evaluations

In this paper, a naturalistic 3D acceleration-based activity dataset, the SCUT-NAA dataset, is created to assist researchers in the field of acceleration-based activity recognition and to provide a standard dataset for comparing and evaluating the performance of different algorithms. The SCUT-NAA dataset is the first publicly available 3D acceleration-based activity dataset and contains 1278 samples from 44 subjects (34 males and 10 females) collected in naturalistic settings with only one tri-axial accelerometer located alternatively on the waist belt, in the trousers pocket, and in the shirt pocket. Each subject was asked to perform ten activities. Benchmark evaluations of the dataset are provided based on FFT coefficients, DCT coefficients, time-domain features, and AR coefficients for the different accelerometer locations.