A novel method to identify trampoline skills using a single video camera is proposed herein. Conventional computer vision techniques are used for identification, estimation, and tracking of the gymnast's body in a video recording of the routine. For each frame, an open source convolutional neural network is used to estimate the pose of the athlete's body. Body orientation and joint angle estimates are extracted from these pose estimates. The trajectories of these angle estimates over time are compared with those of labelled reference skills. A nearest neighbour classifier utilising a mean squared error distance metric is used to identify the skill performed. A dataset containing 714 skill examples with 20 distinct skills performed by adult male and female gymnasts was recorded and used for evaluation of the system. The system was found to achieve a skill identification accuracy of 80.7 for the dataset.
In @cite_11 , inertial sensors were used to measure body point acceleration and orientation. The gymnast was required to wear a body suit containing ten inertial measurement units. The sensor data streams were transformed into a feature sequence for classification. For each skill, a motion template was learned. The feature sequence of the unknown trampoline motions were compared with a set of skill templates using a variant of dynamic time warping. The best accuracy achieved was 84.7 A survey of vision-based methods for general human motion representation, segmentation and recognition can be found in @cite_5 . In @cite_9 , judging of rhythmic gymnastics skills from video was investigated. The movement of the gymnast was tracked using optical flow. Velocity field information was extracted across all frames of a skill and projected into a velocity covariance eigenspace. Similar movements were found to trace out unique, but similar, trajectories. New video recordings were classified based on their distance from reference trajectories. The system's specificity was approximately 85
@cite_5 @cite_9 @cite_11
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Abstract We describe a conceptually simple algorithm for assigning judgement scores to rhythmic gymnastic movements, which could improve scoring objectivity and reduce judgemental bias during competitions. Our method, implemented as a real-time computer vision software, takes a video shot or a live performance video stream as input and extracts detailed velocity field information from body movements, transforming them into specialized spatio-temporal image templates. The collection of such images over time, when projected into a velocity covariance eigenspace, trace out unique but similar trajectories for a particular gymnastic movement type. By comparing separate executions of the same atomic gymnastic routine, our method assigns a quality judgement score that is related to the distance between the respective spatio-temporal trajectories. For several standard gymnastic movements, the method accurately assigns scores that are comparable to those assigned by expert judges. We also describe our rhythmic gymnastic video shot database, which we have made freely available to the human movement research community. The database can be obtained at http: www.milegroup.net apps gymdb . This paper proposes an ego-motion tracking method that utilizes visual-inertial sensors for wearable blind navigation. The unique challenge of wearable motion tracking is to cope with arbitrary body motion and complex environmental dynamics. We introduce a visual sanity check to select accurate visual estimations by comparing visually estimated rotation with measured rotation by a gyroscope. The movement trajectory is recovered through adaptive fusion of visual estimations and inertial measurements, where the visual estimation outputs motion transformation between consecutive image captures, and inertial sensors measure translational acceleration and angular velocities. The frame rates of visual and inertial sensors are different, and vary with respect to time owning to visual sanity checks. We hence employ a multirate extended Kalman filter (EKF) to fuse visual and inertial estimations. The proposed method was tested in different indoor environments, and the results show its effectiveness and accuracy in ego-motion tracking. Many systems for recording human movement need some reference from beacons near the subject, such as video cameras. Our goal is to measure human kinematics with sensors that are placed on the segments of interest. This way, experiments in which human movement is recorded are not restricted to a lab. Our inertial sensor-unit consists of a little box with three miniature gyroscopes (Murata ENC05E) and three linear accelerometers (AD xl05) that measure 3D angular velocity and linear acceleration, respectively. Both the gyroscope and accelerometer signals contain information about the orientation of the sensor. The sensor orientation can be obtained by integration of the angular velocity signals obtained from the gyroscopes [1]. This operation introduces drift in the estimated orientation. Accelerometers do not only measure the acceleration of the sensor, but also the gravitational vector. This gravitational component not only has a bigger magnitude for many human movements but also always points downwards. This knowledge can be used to make an estimation of the tilt. The tilt is the angle between the sensor axes and the vertical. This tilt estimation is not very precise but does not suffer from drift. The abstract describes a way to fuse both sensors (gyroscopes and accelerometers) to obtain an estimate of the orientation that is both accurate and is limited in integration drift. 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 motion capture 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.