Human Body Motion and Gestures Recognition Based on Checkpoints

The computational implementation of human body gestures recognition has been a challenge for several years. Nowadays, thanks to the development of RGB-D cameras it is possible to acquire a set of data that represents a human position in time. Despite that, these cameras provide raw data, still being a problem to identify in real-time a specific pre-defined user movement without relying on offline training. However, in several cases the real-time requisite is critical, especially when it is necessary to detect and analyze a movement continuously, as in the tracking of physiotherapeutic movements or exercises. This paper presents a simple and fast technique to recognize human movements using the set of data provided by a RGB-D camera. Moreover, it describes a way to identify not only if the performed motion is valid, i.e. belongs to a set of pre-defined gestures, but also the identification of at which point the motion is (beginning, end or somewhere in the middle of it). The precision of the proposed technique can be set to suit the needs of the application and has a simple and fast way of gesture registration, thus, being easy to set new motions if necessary. The proposed technique has been validated through a set of tests focused on analyzing its robustness considering a series of variations during the interaction like fast and complex gestures.