Benchmarking human motion analysis using kinect one: An open source dataset

There is a clear advantage to developing automated systems to detect human motion in the field of computer vision for applications associated with healthcare. We have compiled a diverse dataset of clinically-relevant motions using the Microsoft Kinect One sensor and release the dataset to the community as an open source solution for benchmarking detection, quantification and recognition algorithms. The dataset, namely Kinect 3D Active (K3Da), includes motions collected from young and older men and women ranging in age from 18-81 years. Participants performed standardised tests, including the Short Physical Performance Battery, Timed-Up-And-Go, vertical jump and other balance assessments which were recorded using depth sensor technology and extracted to generate motion capture data, sampled at 30 frames-per-second. Preliminary evaluations using Support Vector Machines, Random Forests, Artificial Neural Networks and Boltzmann Machines show age-related differences in many of the movements. These results demonstrate the relevance of the dataset to support benchmarking of algorithms associated and/or intended for use in a healthcare setting.