Posture-based and Action-based Graphs for Boxing Skill Visualization

Automatic evaluation of sports skills has been an active research area. However, most of the existing research focuses on low-level features such as movement speed and strength. In this work, we propose a framework for automatic motion analysis and visualization, which allows us to evaluate high-level skills such as the richness of actions, the flexibility of transitions and the unpredictability of action patterns. The core of our framework is the construction and visualization of the posture-based graph that focuses on the standard postures for launching and ending actions, as well as the action-based graph that focuses on the preference of actions and their transition probability. We further propose two numerical indices, the Connectivity Index and the Action Strategy Index, to assess skill level according to the graph. We demonstrate our framework with motions captured from different boxers. Experimental results demonstrate that our system can effectively visualize the strengths and weaknesses of the boxers

SkillVis: A Visualization Tool for Boxing Skill Assessment

Motion analysis and visualization are crucial in sports science for sports training and performance evaluation. While primitive computational methods have been proposed for simple analysis such as postures and movements, few can evaluate the high-level quality of sports players such as their skill levels and strategies. We propose a visualization tool to help visualizing boxers' motions and assess their skill levels. Our system automatically builds a graph-based representation from motion capture data and reduces the dimension of the graph onto a 3D space so that it can be easily visualized and understood. In particular, our system allows easy understanding of the boxer's boxing behaviours, preferred actions, potential strength and weakness. We demonstrate the effectiveness of our system on different boxers' motions. Our system not only serves as a tool for visualization, it also provides intuitive motion analysis that can be further used beyond sports science

Human Motion Analysis and Synthesis in Computer Graphics

This thesis focuses on solving a challenging problem in the field of computer graphics, namely to model and understand 3D human motion efficiently and meaningfully. This is vital to achieve the analysis (health & sports science), synthesis (character animation) and control (video game) of human movements. Though numerous studies have focused on improving the results of motion analysis, motion synthesis and motion control, only a few of these studies solved the problems from the fundamental part owing to the lack of information encoded in motion data. In my works, the motion of human was divided into the three types, namely single human motion, multi-people interactions and crowd movement. Subsequently, I solved the problems from motion analysis to motion control in different types of motion. In the single human motion, two types of motion graphs on the motion sequence were proposed using Markov Process. The human motion is represented as the directed graphs, which suggests the number of action patterns and transitions among them. By analyzing the graphs topologies, the richness, transitions flexibility and unpredictability among different action patterns inside the human motion sequence can be easily verified. The framework here is capable of visualizing and analyzing the human motion on the high level of action preference, intention and diversity. For the two people interaction motion, the use of 3D volumetric meshes on the interacting people was proposed to model their movement and spatial relationship among them. The semantic meanings of the motions were defined by such relationship. A customized Earth Movers Distance was proposed to assess the topological and geometric difference between two groups of meshes. The above assessment captured the semantic similarities among different two-people interactions, which is consistent with what humans perceive. With this interaction motion representation, the multi-people interactions in semantic level can be retrieved and analyzed, and such complex movements can be easily adapted and synthesized with low computational costs. In the crowd movement, a data-driven gesture-based crowd control system was proposed, in which the control scheme was learned from example gestures provided by different users. The users gestures and corresponding crowd motions, representable to the crowd motions properties and irrelevant to style variations of gestures and crowd motions, were modelled into a compact low dimensional space. With this representation, the proposed framework can take an arbitrary users input gesture and generate appropriate crowd motion in real time. This thesis shows the advantages of higher-level human motion modelling in different scenarios and solves different challenging tasks of computer graphics. The unified framework summarizes the knowledge to analyze, synthesize and control the movement of human

An interactive motion analysis framework for diagnosing and rectifying potential injuries caused through resistance training

With the rapid increase in individuals participating in resistance training activities, the number of injuries pertaining to these activities has also grown just as aggressively. Diagnosing the causes of injuries and discomfort requires a large amount of resources from highly experienced physiotherapists. In this paper, we propose a new framework to analyse and visualize movement patterns during performance of four major compound lifts. The analysis generated will be used to efficiently determine whether the exercises are being performed correctly, ensuring anatomy remains within its functional range of motion, in order to prevent strain or discomfort that may lead to injury

Assessment of Physical Fitness and Training Effect in Individual Sports

Physical fitness is the basis for the success of players in sports, and its monitoring makes it possible to assess the effectiveness of training and identify possible errors. During training, thanks to the use of control results, these activities are modified, which better prepares players for competition. This Special Issue, entitled "Assessment of Physical Fitness and the Effect of Training in Individual Sports" presents the results of coaching control and the results of monitoring progression in training, as well as an assessment of the physical fitness of athletes practicing individual sports

Multi-sensor human action recognition with particular application to tennis event-based indexing

The ability to automatically classify human actions and activities using vi- sual sensors or by analysing body worn sensor data has been an active re- search area for many years. Only recently with advancements in both fields and the ubiquitous nature of low cost sensors in our everyday lives has auto- matic human action recognition become a reality. While traditional sports coaching systems rely on manual indexing of events from a single modality, such as visual or inertial sensors, this thesis investigates the possibility of cap- turing and automatically indexing events from multimodal sensor streams. In this work, we detail a novel approach to infer human actions by fusing multimodal sensors to improve recognition accuracy. State of the art visual action recognition approaches are also investigated. Firstly we apply these action recognition detectors to basic human actions in a non-sporting con- text. We then perform action recognition to infer tennis events in a tennis court instrumented with cameras and inertial sensing infrastructure. The system proposed in this thesis can use either visual or inertial sensors to au- tomatically recognise the main tennis events during play. A complete event retrieval system is also presented to allow coaches to build advanced queries, which existing sports coaching solutions cannot facilitate, without an inordi- nate amount of manual indexing. The event retrieval interface is evaluated against a leading commercial sports coaching tool in terms of both usability and efficiency

ICS Materials. Towards a re-Interpretation of material qualities through interactive, connected, and smart materials.

The domain of materials for design is changing under the influence of an increased technological advancement, miniaturization and democratization. Materials are becoming connected, augmented, computational, interactive, active, responsive, and dynamic. These are ICS Materials, an acronym that stands for Interactive, Connected and Smart. While labs around the world are experimenting with these new materials, there is the need to reflect on their potentials and impact on design. This paper is a first step in this direction: to interpret and describe the qualities of ICS materials, considering their experiential pattern, their expressive sensorial dimension, and their aesthetic of interaction. Through case studies, we analyse and classify these emerging ICS Materials and identified common characteristics, and challenges, e.g. the ability to change over time or their programmability by the designers and users. On that basis, we argue there is the need to reframe and redesign existing models to describe ICS materials, making their qualities emerge

CP-AGCN: Pytorch-based attention informed graph convolutional network for identifying infants at risk of cerebral palsy

Early prediction is clinically considered one of the essential parts of cerebral palsy (CP) treatment. We propose to implement a low-cost and interpretable classification system for supporting CP prediction based on General Movement Assessment (GMA). We design a Pytorch-based attention-informed graph convolutional network to early identify infants at risk of CP from skeletal data extracted from RGB videos. We also design a frequency-binning module for learning the CP movements in the frequency domain while filtering noise. Our system only requires consumer-grade RGB videos for training to support interactive-time CP prediction by providing an interpretable CP classification result

Evaluating Martial Arts Punching Kinematics Using a Vision and Inertial Sensing System

Martial arts has many benefits not only in self-defence, but also in improving physical fitness and mental well-being. In our research we focused on analyzing the velocity, impulse, momentum and impact force of the Taekwondo sine-wave punch and reverse-step punch. We evaluated these techniques in comparison with the martial arts styles of Hapkido and Shaolin Wushu and investigated the kinematic properties. We developed a sensing system which is composed of an ICSensor Model 3140 accelerometer attached to a punching bag for measuring dynamic acceleration, Kinovea motion analysis software and 2 GoPro Hero 3 cameras, one focused on the practitioner’s motion and the other focused on the punching bag’s motion. Our results verified that the motion vectors associated with a Taekwondo practitioner performing a sine-wave punch, uses a unique gravitational potential energy to optimise the impact force of the punch. We demonstrated that the sine-wave punch on average produced an impact force of 6884 N which was higher than the reverse-step punch that produced an average impact force of 5055 N. Our comparison experiment showed that the Taekwondo sine-wave punch produced the highest impact force compared to a Hapkido right cross punch and a Shaolin Wushu right cross, however the Wushu right cross had the highest force to weight ratio at 82:1. The experiments were conducted with high ranking black belt practitioners in Taekwondo, Hapkido and Shaolin Wushu.</jats:p

1934-1935 College Circular

The Circular included a list of regulations, faculty members, information on the classrooms & Cortland Normal School Building, it also included various course listings as well. Circulars transformed into “college catalogs” across the country beginning in the early 20th Century.https://digitalcommons.cortland.edu/collegecatalogs/1117/thumbnail.jp