The adaptation of GDL motion recognition system to sport and rehabilitation techniques analysis

Abstract

The main novelty of this paper is presenting the adaptation of Gesture Description Language (GDL) methodology to sport and rehabilitation data analysis and classification. In this paper we showed that Lua language can be successfully used for adaptation of the GDL classifier to those tasks. The newly applied scripting language allows easily extension and integration of classifier with other software technologies and applications. The obtained execution speed allows using the methodology in the real-time motion capture data processing where capturing frequency differs from 100 Hz to even 500 Hz depending on number of features or classes to be calculated and recognized. Due to this fact the proposed methodology can be used to the high-end motion capture system. We anticipate that using novel, efficient and effective method will highly help both sport trainers and physiotherapist in they practice. The proposed approach can be directly applied to motion capture data kinematics analysis (evaluation of motion without regard to the forces that cause that motion). The ability to apply pattern recognition methods for GDL description can be utilized in virtual reality environment and used for sport training or rehabilitation treatment.

Lower Limb Walking Gait Profiling Using Marker-less Motion Capture with GDL and R-GDL methods to Assist Physiotherapy Treatment

“…In order to produce motion template for lower limb walking gait, validation tests have been realized. The validation test done by reuse the same motion templates by cutting and segmented into 10 different motion clips based on one cycle walking within 3 meters [14]. In this ISSN: 2231-5381 doi : 10.14445/22315381/CATI2P207 Page 49 case, the healthy respondent can walk about five to six steps within 3 meters.…”

“…The adaptation of Gesture Description Language (GDL) approach for sport and rehabilitation data analysis and classification have been introduced by Hachaj & Ogiela [14]. GDL is real-time human gesture recognition that capable as a classifier while Reverse-GDL (R-GDL) is a generator of automatics scripts for gesture description using unsupervised learning approach [14;15;16].…”

Deep Learning Based Advanced Spatio-Temporal Extraction Model in Medical Sports Rehabilitation for Motion Analysis and Data Processing

“…It developed LBM training for each frame to obtain characteristics in storage structure and connect the hidden layer with the time information unit. The relationship assumed that through directional connections between the adjacent input frames, the entire motion series shared the same spatial transmission properties [13]. The scope for reuse of existing motion sequences [16] is substantially restricted by a large number of unlabeled or minimum design data [17,18].…”

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.…”

“…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]. Based on the proposed experimental schemes, the approaches could be categorized as the skeleton-joint-based approach [1,17,24,[28][29][30][31][32] and the silhouette-based approach [23,36]. Further combining the technique of virtual reality, these approaches can provide an alternative [10][11][12] for guiding correct motions in rehabilitation and can simultaneously motivate the psychological emotions of users.…”