GESTURE RECOGNITION FOR PENCAK SILAT TAPAK SUCI REAL-TIME ANIMATION

The main target in this research is a design of a virtual martial arts training system in real-time and as a tool in learning martial arts independently using genetic algorithm methods and dynamic time warping. In this paper, it is still in the initial stages, which is focused on taking data sets of martial arts warriors using 3D animation and the Kinect sensor cameras, there are 2 warriors x 8 moves x 596 cases/gesture = 9,536 cases. Gesture Recognition Studies are usually distinguished: body gesture and hand and arm gesture, head and face gesture, and, all three can be studied simultaneously in martial arts pencak silat, using martial arts stance detection with scoring methods. Silat movement data is recorded in the form of oni files using the OpenNI ™ (OFW) framework and BVH (Bio Vision Hierarchical) files as well as plug-in support software on Mocap devices. Responsiveness is a measure of time responding to interruptions, and is critical because the system must be able to meet the demand

Analysis of movement quality in full-body physical activities

Full-body human movement is characterized by fine-grain expressive qualities that humans are easily capable of exhibiting and recognizing in others' movement. In sports (e.g., martial arts) and performing arts (e.g., dance), the same sequence of movements can be performed in a wide range of ways characterized by different qualities, often in terms of subtle (spatial and temporal) perturbations of the movement. Even a non-expert observer can distinguish between a top-level and average performance by a dancer or martial artist. The difference is not in the performed movements-the same in both cases-but in the \u201cquality\u201d of their performance. In this article, we present a computational framework aimed at an automated approximate measure of movement quality in full-body physical activities. Starting from motion capture data, the framework computes low-level (e.g., a limb velocity) and high-level (e.g., synchronization between different limbs) movement features. Then, this vector of features is integrated to compute a value aimed at providing a quantitative assessment of movement quality approximating the evaluation that an external expert observer would give of the same sequence of movements. Next, a system representing a concrete implementation of the framework is proposed. Karate is adopted as a testbed. We selected two different katas (i.e., detailed choreographies of movements in karate) characterized by different overall attitudes and expressions (aggressiveness, meditation), and we asked seven athletes, having various levels of experience and age, to perform them. Motion capture data were collected from the performances and were analyzed with the system. The results of the automated analysis were compared with the scores given by 14 karate experts who rated the same performances. Results show that the movement-quality scores computed by the system and the ratings given by the human observers are highly correlated (Pearson's correlations r = 0.84, p = 0.001 and r = 0.75, p = 0.005)

Compositions Inspired by Shotokan Karate Katas

This compositional thesis consists of six works inspired by the katas or forms of the Shotokan karate style. A kata is a fixed sequence of karate movements with an embedded natural rhythm. The Origins of Shotokan section reviews the history of martial arts in Asia and introduces some of the underlying inspirational elements such as techniques based on animal predatory movements and the katas unique names. The Kata Common Elements section expands on the meaning and structure of a kata. The traditional documentation of the natural rhythm ignores the move to move time interval. This thesis introduces the use of Western music notation to capture the timing relationships and to explain the katas natural rhythm. Four composition sections use a melodic line that is influenced by the physical movement of the karate technique. Picture examples are used to clarify the linkage to the melody. The later sections describe the history of the six inspirational katas. The compositional decisions associated with each is described with respect to their origin, katas name, natural rhythm, orchestration and melodic lines. The musical score accompanies the respective descriptions

a multimodal dataset for the analysis of movement qualities in karate martial art

A multimodal dataset is presented, which has been collected for analyzing and measuring the quality of movement performed during sport activities. Martial arts (namely karate) are taken as test-beds for investigation. Karate encompasses predefined sequences of movements ("katas") that can be carried out with different qualities, e.g., by performers at different skill levels (highly vs. poorly skilled). The experimental setup and method are described. The dataset is composed of motion capture (MoCap) data, synchronized with video and audio recordings, of several participants with different levels of experience. The raw MoCap data are independent of any particular post-processing algorithm and can be used for other research purposes. In the second part of the paper, a set of measures is proposed to evaluate a kata performance. They are based on the geometrical and kinematic features, such as posture correctness and synchronization between limbs. and were chosen according to karate experts' opinion

Motion Capture Study of Human Movement Recognition

In this thesis, we address questions about recognition of human movement through motion capture. We begin by capturing the martial arts movements of four actors as they each perform three different techniques. We then conduct a survey to determine if participants in the survey are able to distinguish the various martial arts techniques and differentiate the actors that portray said techniques. A secondary consideration of our survey is to determine if the participants can distinguish the sex of the actors as they perform the martial arts techniques. Our hypothesis is that people are able to distinguish actors and sex; however, determining sex may be more difficult with digital actor representations that are anatomically ambiguous. With this thesis we will attempt to provide insight into human perception and motion capture, and help validate the use of motion capture in video games and movies for realistic human animation and interactions and to help improve the immersion of the player/viewer

Multi-scale techniques for multi-dimensional data analysis

Large datasets of geometric data of various nature are becoming more and more available as sensors become cheaper and more widely used. Due to both their size and their noisy nature, special techniques must be employed to deal with them correctly. In order to efficiently handle this amount of data and to tackle the technical challenges they pose, we propose techniques that analyze a scalar signal by means of its critical points (i.e. maxima and minima), ranking them on a scale of importance, by which we can extrapolate important information of the input signal separating it from noise, thus dramatically reducing the complexity of the problem. In order to obtain a ranking of critical points we employ multi-scale techniques. The standard scale-space approach, however, is not sufficient when trying to track critical points across various scales. We start from an implementation of the scale-space which computes a linear interpolation between scales in order to make tracking of critical points easier. The linear interpolation of a process which is not itself linear, though, does not fulfill some theoretical properties of scale-space, thus making the tracking of critical points much harder. We propose an extension of this piecewiselinear scale-space implementation, which recovers the theoretical properties (e.g., to avoid the generation of new critical points as the scale increases) and keeps the tracking consistent. Next we combine the scale-space with another technique that comes from the topology theory: the classification of critical points based on their persistence value. While the scale-space applies a filtering in the frequency domain, by progressively smoothing the input signal with low-pass filters of increasing size, the computation of the persistence can be seen as a filtering applied in the amplitude domain, which progressively removes pairs of critical points based on their difference in amplitude. The two techniques, while being both relevant to the concept of scale, express different qualities of the critical points of the input signal; depending on the application domain we can use either of them, or, since they both have non-zero values only at critical points, they can be used together with a linear combination. The thesis will be structured as follows: In Chapter 1 we will present an overview on the problem of analyzing huge geometric datasets, focusing on the problem of dealing with their size and noise, and of reducing the problem to a subset of relevant samples. The Chapter 2 will contain a study of the state of the art in scale-space algorithms, followed by a more in-depth analysis of the virtually continuous framework used as base technique will be presented. In its last part, we will propose methods to extend these techniques in order to satisfy the axioms present in the continuous version of the scale-space and to have a stronger and more reliable tracking of critical points across scales, and the extraction of the persistence of critical points of a signal as a variant to the standard scale-space approach; we will show the differences between the two and discuss how to combine them. The Chapter 3 will introduce an ever growing source of data, the motion capture systems; we will motivate its importance by discussing the many applications in which it has been used for the past two decades. We will briefly summarize the different systems existing and then we will focus on a particular one, discussing its peculiarities and its output data. In Chapter 4, we will discuss the problem of studying intra-personal synchronization computed on data coming from such motion-capture systems. We will show how multi-scale approaches can be used to identify relevant instants in the motion and how these instants can be used to precisely study synchronization between the different parts of the body from which they are extracted. We will apply these techniques to the problem of generating a classifier to discriminate between martial artists of different skills who have been recorded doing karate\u2019s movements. In Chapter 5 will present a work on the automatic detection of relevant points of the human face from 3D data. We will show that the Gaussian curvature of the 3D surface is a good feature to distinguish the so-called fiducial points, but also that multi-scale techniques must be used to extract only relevant points and get rid of the noise. In closing, Chapter 6 will discuss an ongoing work about motion segmentation; after an introduction about the meaning and different possibilities of motion segmentation we will present the data we work with, the approach used to identify segments and some preliminary tools and results

Wearable Sensors and Smart Devices to Monitor Rehabilitation Parameters and Sports Performance: An Overview

A quantitative evaluation of kinetic parameters, the joint’s range of motion, heart rate, and breathing rate, can be employed in sports performance tracking and rehabilitation monitoring following injuries or surgical operations. However, many of the current detection systems are expensive and designed for clinical use, requiring the presence of a physician and medical staff to assist users in the device’s positioning and measurements. The goal of wearable sensors is to overcome the limitations of current devices, enabling the acquisition of a user’s vital signs directly from the body in an accurate and non–invasive way. In sports activities, wearable sensors allow athletes to monitor performance and body movements objectively, going beyond the coach’s subjective evaluation limits. The main goal of this review paper is to provide a comprehensive overview of wearable technologies and sensing systems to detect and monitor the physiological parameters of patients during post–operative rehabilitation and athletes’ training, and to present evidence that supports the efficacy of this technology for healthcare applications. First, a classification of the human physiological parameters acquired from the human body by sensors attached to sensitive skin locations or worn as a part of garments is introduced, carrying important feedback on the user’s health status. Then, a detailed description of the electromechanical transduction mechanisms allows a comparison of the technologies used in wearable applications to monitor sports and rehabilitation activities. This paves the way for an analysis of wearable technologies, providing a comprehensive comparison of the current state of the art of available sensors and systems. Comparative and statistical analyses are provided to point out useful insights for defining the best technologies and solutions for monitoring body movements. Lastly, the presented review is compared with similar ones reported in the literature to highlight its strengths and novelties

Constructing a reference standard for sports science and clinical movement sets using IMU-based motion capture technology

Motion analysis has improved greatly over the years through the development of low-cost inertia sensors. Such sensors have shown promising accuracy for both sport and medical applications, facilitating the possibility of a new reference standard to be constructed. Current gold standards within motion capture, such as high-speed camera-based systems and image processing, are not suitable for many movement-sets within both sports science and clinical movement analysis due to restrictions introduced by the movement sets. These restrictions include cost, portability, local environment constraints (such as light level) and poor line of sight accessibility. This thesis focusses on developing a magnetometer-less IMU-based motion capturing system to detect and classify two challenging movement sets: Basic stances during a Shaolin Kung Fu dynamic form, and severity levels from the modified UPDRS (Unified Parkinson’s Disease Rating Scale) analysis tapping exercise. This project has contributed three datasets. The Shaolin Kung Fu dataset is comprised of 5 dynamic movements repeated over 350 times by 8 experienced practitioners. The dataset was labelled by a professional Shaolin Kung Fu master. Two modified UPDRS datasets were constructed, one for each of the two locations measured. The modified UPDRS datasets comprised of 5 severity levels each with 100 self-emulated movement samples. The modified UPDRS dataset was labelled by a researcher in neuropsychological assessment. The errors associated with IMU systems has been reduced significantly through a combination of a Complementary filter and applying the constraints imposed by the range of movements available in human joints. Novel features have been extracted from each dataset. A piecewise feature set based on a moving window approach has been applied to the Shaolin Kung Fu dataset. While a combination of standard statistical features and a Durbin Watson analysis has been extracted from the modified UPDRS measurements. The project has also contributed a comparison of 24 models has been done on all 3 datasets and the optimal model for each dataset has been determined. The resulting models were commensurate with current gold standards. The Shaolin Kung Fu dataset was classified with the computational costly fine decision tree algorithm using 400 splits, resulting in: an accuracy of 98.9%, a precision of 96.9%, a recall value of 99.1%, and a F1-score of 98.0%. A novel approach of using sequential forward feature analysis was used to determine the minimum number of IMU devices required as well as the optimal number of IMU devices. The modified UPDRS datasets were then classified using a support vector machine algorithm requiring various kernels to achieve their highest accuracies. The measurements were repeated with a sensor located on the wrist and finger, with the wrist requiring a linear kernel and the finger a quadratic kernel. Both locations achieved an accuracy, precision, recall, and F1-score of 99.2%. Additionally, the project contributed an evaluation to the effect sensor location has on the proposed models. It was concluded that the IMU-based system has the potential to construct a reference standard both in sports science and clinical movement analysis. Data protection security and communication speeds were limitations in the system constructed due to the measured data being transferred from the devices via Bluetooth Low Energy communication. These limitations were considered and evaluated in the future works of this project

Evaluation of human movement qualities: A methodology based on transferable-utility games on graphs.

Abstract A novel computational method for the analysis of expressive full-body movement qualities is introduced, which exploits concepts and tools from graph theory and game theory. The human skeletal structure is modeled as an undirected graph, where the joints are the vertices and the edge set contains both physical and nonphysical links. Physical links correspond to connections between adjacent physical body joints (e.g., the forearm, which connects the elbow to the wrist). Nonphysical links act as \u201cbridges\u201d between parts of the body not directly connected by the skeletal structure, but sharing very similar feature values. The edge weights depend on features obtained by using Motion Capture data. Then, a mathematical game is constructed over the graph structure, where the vertices represent the players and the edges represent communication channels between them. Hence, the body movement is modeled in terms of a game built on the graph structure. Since the vertices and the edges contribute to the overall quality of the movement, the adopted game-theoretical model is of cooperative nature. A game-theoretical concept, called Shapley value, is exploited as a centrality index to estimate the contribution of each vertex to a shared goal (e.g., to the way a particular movement quality is transferred among the vertices). The proposed method is applied to a data set of Motion Capture data of subjects performing expressive movements, recorded in the framework of the H2020-ICT-2015 EU Project WhoLoDance, Project no. 688865. Results are presented: development of novel method, contribution to the scientific community with a new data corpus, application the discussed method to 100 movement recordings and creation of database archive of stimuli for further use in research studies in the framework of the WhoLoDance Project

The effect of weighted body armor on close combat reaction time and core muscle activation

The purpose of this study was to measure the choice reaction time and myoelectric activity of the right and left rectus abdominus, and right and left external obliques required to initiate movement in response to a visual stimulus that signaled performance of four different closecombat movements (left or right cross and left or right dodge). Reaction time and myoelectric activity were then compared with performing the movements in response to the visual stimulus while wearing a weighted vest that simulated wearing tactical body armor. Myoelectric activity was measured as the average root mean square (RMS) of the surface electromyography (sEMG) values. The hypotheses were that average time to react to the visual stimulus for each movement and average myoelectric activity to initiate the movement would be greater under the weighted vest condition. The participants were 10 active martial arts/boxing performers from different disciplines with a minimum one year experience in their discipline. During two separate sessions, the participants completed eight warm-up trials and 24 measured trials, with the first four trials deleted. The stimuli were activated in a random order with foreperiods ranging from 10 to 20 seconds between trials. The sessions were randomly chosen to be either loaded or unloaded conditions. Surface EMG electrodes detected the myoelectric activity of the right and left rectus abdominus, and right and left external oblique muscles. The electrodes pre-amplified the myoelectric signals by a factor of 35. The sEMG signals of the four muscles were treated with a 20 Hz low cut/high pass filter, amplified by a factor of 20,000, and the RMS of the filtered signals were derived using an 11.75 ms time window. The analog RMS sEMG was sampled at 1020 Hz and converted to digital form. The reaction time for each movement was determined from the initiation of the stimulus to the point at which myoelectric activity iv reached the threshold of 0.5 volts. A two-tailed paired samples t-test was run to determine differences between the average reaction time and average RMS sEMG for each core muscle during each movement for the unweighted and weighted conditions. A one-way ANOVA with repeated measures was run to determine significant differences in average reaction time and average total muscle activity between the unweighted and weighted conditions for the group. Alpha was set at 0.05. Significant differences were found between the unweighted and weighted conditions for reaction time while performing the left dodge, right cross, and right dodge with slight significance for the left cross (p = 0.047, p = 0.014, p = 0.002, and p = 0.059, respectively). Further, group average reaction time was significantly greater in the weighted condition (p = 0.001). No significant differences were found in initial muscle activity between the conditions. These results support the first hypothesis that mean reaction time would significantly increase when performing close-quarters combat movements in response to a visual stimulus while wearing a loaded vest. Combatives instructors, specifically military and law enforcement, can use this information as a means to further protect the armed forces by training them in the protective gear that they will be wearing out in the field. This will hopefully acclimatize the armed forces to a point where performance will not hinder to complete missions in hostile environments