Human actions recognition from motion capture recordings using signal resampling and pattern recognition methods

Abstract

In this paper we will experimentally prove that after recalculating the motion capture (MoCap) data to position-invariant representation it can be directly used by classifier to successfully recognize various actions types. The assumption on classifier is that it is capable to deal with objects that are described by hundreds of numeric values. The second novelty of this paper is application of neural network trained with the parallel stochastic gradient descent, Random Forests and Support Vector Machine with Gaussian radial basis kernel to perform classification task on gym exercises and karate techniques MoCap datasets. We have tested our approach on two datasets using k-fold cross-validation method. Depending of the dataset we have obtained averaged recognition rate from 100 to 97 %. Our results presented in this work give very important hints for developing similar actions recognition systems because proposed features selection and classification setup seems to guarantee high efficiency and effectiveness.

The effects of ArUco marker velocity and size on motion capture detection and accuracy in the context of human body kinematics analysis

“…In sports, it can support the development of proper technique (Kopniak, 2012), for example, testing athletes using rowing machines with motion acquisition systems, EMG systems, and heart rate monitors to develop the interdisciplinary procedures for the testing of rowers (Skublewska-Paszkowska, Montusiewicz, Łukasik, Pszczoła-Pasierbiewicz, Baran, Smołka, Pueo, 2016). In psychology, it supports gesture analysis (Kopniak, 2012) or emotion analysis to allow for easy interpretation (Hachaj, Ogiela, Koptyra, 2018). For example, it can be used to detect behaviour based on motion recordings and detected behavioural patterns (Baran, 2018).…”

Real-Time Musical Conducting Gesture Recognition Based on a Dynamic Time Warping Classifier Using a Single-Depth Camera

“…In section 2, we present In computer vision systems, to track the position of hands and palms, a certain amount of preprocessing is required and the accuracy of the performance is not guaranteed. Therefore, in this study, we chose the Kinect to achieve hand tracking [17][18][19][20]. The Kinect generates skeleton images of the human body as graphs with edges and vertices through its depth sensors.…”

Pattern Recognition of Human Postures Using the Data Density Functional Method

“…Under the hardware schemes, human activities and motional patterns were detected and recorded by employing inertial sensors [1,2,[15][16][17] and/or image sensors [1,23,24,27]. The acquired analog signals were then commonly analyzed using machine learning methods [15,17,22,23,[27][28][29][30][31][32]. Thus, the data acquirements of human mobility and activities can have less interventions in the duration of data collection.…”

“…Thus, the data acquirements of human mobility and activities can have less interventions in the duration of data collection. Informative feature extractions mainly rely on the support vector machine [27,33], K-means clustering algorithm [33,34], or linear discriminant analysis [35], whereas the hidden Markov model was commonly used for human activity recognition [22,31,33,34]. To further increase the accuracy of posture recognition in both industry and academia, the image and inertial sensor fusion is a popular technique, performed by commercial equipment, the Microsoft Kinect [1,10,11,23].…”