Motion synthesis for sports using unobtrusive lightweight body-worn and environment sensing

The ability to accurately achieve performance capture of athlete motion during competitive play in near real-time promises to revolutionise not only broadcast sports graphics visualisation and commentary, but also potentially performance analysis, sports medicine, fantasy sports and wagering. In this paper, we present a highly portable, non-intrusive approach for synthesising human athlete motion in competitive game-play with lightweight instru- mentation of both the athlete and field of play. Our data-driven puppetry technique relies on a pre-captured database of short segments of motion capture data to construct a motion graph augmented with interpolated mo- tions and speed variations. An athlete’s performed motion is synthesised by finding a related action sequence through the motion graph using a sparse set of measurements from the performance, acquired from both worn inertial and global location sensors. We demonstrate the efficacy of our approach in a challenging application scenario, with a high-performance tennis athlete wearing one or more lightweight body-worn accelerometers and a single overhead camera providing the athlete’s global position and orientation data. However, the approach is flexible in both the number and variety of input sensor data used. The technique can also be adopted for searching a motion graph efficiently in linear time in alternative applications

MOCA: A Low-Power, Low-Cost Motion Capture System Based on Integrated Accelerometers

Human-computer interaction (HCI) and virtual reality applications pose the challenge of enabling real-time interfaces for natural interaction. Gesture recognition based on body-mounted accelerometers has been proposed as a viable solution to translate patterns of movements that are associated with user commands, thus substituting point-and-click methods or other cumbersome input devices. On the other hand, cost and power constraints make the implementation of a natural and efficient interface suitable for consumer applications a critical task. Even though several gesture recognition solutions exist, their use in HCI context has been poorly characterized. For this reason, in this paper, we consider a low-cost/low-power wearable motion tracking system based on integrated accelerometers called motion capture with accelerometers (MOCA) that we evaluated for navigation in virtual spaces. Recognition is based on a geometric algorithm that enables efficient and robust detection of rotational movements. Our objective is to demonstrate that such a low-cost and a low-power implementation is suitable for HCI applications. To this purpose, we characterized the system from both a quantitative point of view and a qualitative point of view. First, we performed static and dynamic assessment of movement recognition accuracy. Second, we evaluated the effectiveness of user experience using a 3D game application as a test bed

Combining inertial and visual sensing for human action recognition in tennis

In this paper, we present a framework for both the automatic extraction of the temporal location of tennis strokes within a match and the subsequent classification of these as being either a serve, forehand or backhand. We employ the use of low-cost visual sensing and low-cost inertial sensing to achieve these aims, whereby a single modality can be used or a fusion of both classification strategies can be adopted if both modalities are available within a given capture scenario. This flexibility allows the framework to be applicable to a variety of user scenarios and hardware infrastructures. Our proposed approach is quantitatively evaluated using data captured from elite tennis players. Results point to the extremely accurate performance of the proposed approach irrespective of input modality configuration

Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

We address the problem of making human motion capture in the wild more practical by using a small set of inertial sensors attached to the body. Since the problem is heavily under-constrained, previous methods either use a large number of sensors, which is intrusive, or they require additional video input. We take a different approach and constrain the problem by: (i) making use of a realistic statistical body model that includes anthropometric constraints and (ii) using a joint optimization framework to fit the model to orientation and acceleration measurements over multiple frames. The resulting tracker Sparse Inertial Poser (SIP) enables 3D human pose estimation using only 6 sensors (attached to the wrists, lower legs, back and head) and works for arbitrary human motions. Experiments on the recently released TNT15 dataset show that, using the same number of sensors, SIP achieves higher accuracy than the dataset baseline without using any video data. We further demonstrate the effectiveness of SIP on newly recorded challenging motions in outdoor scenarios such as climbing or jumping over a wall.Comment: 12 pages, Accepted at Eurographics 201

3D human action recognition and motion analysis using selective representations

With the advent of marker-based motion capture, attempts have been made to recognise and quantify attributes of “type”, “content” and “behaviour” from the motion data. Current work exists to obtain quick and easy identification of human motion for use in multiple settings, such as healthcare and gaming by using activity monitors, wearable technology and low-cost accelerometers. Yet, analysing human motion and generating representative features to enable recognition and analysis in an efficient and comprehensive manner has proved elusive thus far. This thesis proposes practical solutions that are based on insights from clinicians, and learning attributes from motion capture data itself. This culminates in an application framework that learns the type, content and behaviour of human motion for recognition, quantitative clinical analysis and outcome measures. While marker-based motion capture has many uses, it also has major limitations that are explored in this thesis, not least in terms of hardware costs and practical utilisation. These drawbacks have led to the creation of depth sensors capable of providing robust, accurate and low-cost solution to detecting and tracking anatomical landmarks on the human body, without physical markers. This advancement has led researchers to develop low-cost solutions to important healthcare tasks, such as human motion analysis as a clinical aid in prevention care. In this thesis a variety of obstacles in handling markerless motion capture are identified and overcome by employing parameterisation of Axis- Angles, applying Euler Angles transformations to Exponential Maps, and appropriate distance measures between postures. While developing an efficient, usable and deployable application framework for clinicians, this thesis introduces techniques to recognise, analyse and quantify human motion in the context of identifying age-related change and mobility. The central theme of this thesis is the creation of discriminative representations of the human body using novel encoding and extraction approaches usable for both marker-based and marker-less motion capture data. The encoding of the human pose is modelled based on the spatial-temporal characteristics to generate a compact, efficient parameterisation. This combination allows for the detection of multiple known and unknown motions in real-time. However, in the context of benchmarking a major drawback exists, the lack of a clinically valid and relevant dataset to enable benchmarking. Without a dataset of this type, it is difficult to validated algorithms aimed at healthcare application. To this end, this thesis introduces a dataset that will enable the computer science community to benchmark healthcare-related algorithms

A Comparison of Video and Accelerometer Based Approaches Applied to Performance Monitoring in Swimming.

The aim of this paper is to present a comparison of video- and sensor based studies of swimming performance. The video-based approach is reviewed and contrasted to the newer sensor-based technology, specifically accelerometers based upon Micro-Electro-Mechanical Systems (MEMS) technology. Results from previously published swim performance studies using both the video and sensor technologies are summarised and evaluated against the conventional theory that upper arm movements are of primary interest when quantifying free-style technique. The authors conclude that multiple sensor-based measurements of swimmers’ acceleration profiles have the potential to offer significant advances in coaching technique over the traditional video based approach

Voltage stability analysis of load buses in electric power system using adaptive neuro-fuzzy inference system (anfis) and probabilistic neural network (pnn)

This paper presents the application of neural networks for analysing voltage stability of load buses in electric power system. Voltage stability margin (VSM) and load power margin (LPM) are used as the indicators for analysing voltage stability. The neural networks used in this research are divided into two types. The first type is using the neural network to predict the values of VSM and LPM. Multilayer perceptron back propagation (MLPBP) neural network and adaptive neuro-fuzzy inference system (ANFIS) will be used. The second type is to classify the values of VSM and LPM using the probabilistic neural network (PNN). The IEEE 30-bus system has been chosen as the reference electrical power system. All of the neural network-based models used in this research is developed using MATLAB