Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Longitudinal tracking of physiological state with electromyographic signals.

Electrophysiological measurements have been used in recent history to classify instantaneous physiological configurations, e.g., hand gestures. This work investigates the feasibility of working with changes in physiological configurations over time (i.e., longitudinally) using a variety of algorithms from the machine learning domain. We demonstrate a high degree of classification accuracy for a binary classification problem derived from electromyography measurements before and after a 35-day bedrest. The problem difficulty is increased with a more dynamic experiment testing for changes in astronaut sensorimotor performance by taking electromyography and force plate measurements before, during, and after a jump from a small platform. A LASSO regularization is performed to observe changes in relationship between electromyography features and force plate outcomes. SVM classifiers are employed to correctly identify the times at which these experiments are performed, which is important as these indicate a trajectory of adaptation

Toward Simulation-Based Training Validation Protocols: Exploring 3d Stereo with Incremental Rehearsal and Partial Occlusion to Instigate and Modulate Smooth Pursuit and Saccade Responses in Baseball Batting

“Keeping your eye on the ball” is a long-standing tenet in baseball batting. And yet, there are no protocols for objectively conditioning, measuring, and/or evaluating eye-on-ball coordination performance relative to baseball-pitch trajectories. Although video games and other virtual simulation technologies offer alternatives for training and obtaining objective measures, baseball batting instruction has relied on traditional eye-pitch coordination exercises with qualitative “face validation”, statistics of whole-task batting performance, and/or subjective batter-interrogation methods, rather than on direct, quantitative eye-movement performance evaluations. Further, protocols for validating transfer-of-training (ToT) for video games and other simulation-based training have not been established in general ― or for eye-movement training, specifically. An exploratory research study was conducted to consider the ecological and ToT validity of a part-task, virtual-fastball simulator implemented in 3D stereo along with a rotary pitching machine standing as proxy for the live-pitch referent. The virtual-fastball and live-pitch simulation couple was designed to facilitate objective eye-movement response measures to live and virtual stimuli. The objective measures 1) served to assess the ecological validity of virtual fastballs, 2) informed the characterization and comparison of eye-movement strategies employed by expert and novice batters, 3) enabled a treatment protocol relying on repurposed incremental-rehearsal and partial-occlusion methods intended to instigate and modulate strategic eye movements, and 4) revealed whether the simulation-based treatment resulted in positive (or negative) ToT in the real task. Results indicated that live fastballs consistently elicited different saccade onset time responses than virtual fastballs. Saccade onset times for live fastballs were consistent with catch-up saccades that follow the smooth-pursuit maximum velocity threshold of approximately 40-70˚/sec while saccade onset times for virtual fastballs lagged in the order of 13%. More experienced batters employed more deliberate and timely combinations of smooth pursuit and catch-up saccades than less experienced batters, enabling them to position their eye to meet the ball near the front edge of home plate. Smooth pursuit and saccade modulation from treatment was inconclusive from virtual-pitch pre- and post-treatment comparisons, but comparisons of live-pitch pre- and post-treatment indicate ToT improvements. Lagging saccade onset times from virtual-pitch suggest possible accommodative-vergence impairment due to accommodation-vergence conflict inherent to 3D stereo displays

A video-based framework for automatic 3d localization of multiple basketball players : a combinatorial optimization approach

Sports complexity must be investigated at competitions; therefore, non-invasive methods are essential. In this context, computer vision, image processing, and machine learning techniques can be useful in designing a non-invasive system for data acquisition that identifies players’ positions in official basketball matches. Here, we propose and evaluate a novel video-based framework to perform automatic 3D localization of multiple basketball players. The introduced framework comprises two parts. The first stage is player detection, which aims to identify players’ heads at the camera image level. This stage is based on background segmentation and on classification performed by an artificial neural network. The second stage is related to 3D reconstruction of the player positions from the images provided by the different cameras used in the acquisition. This task is tackled by formulating a constrained combinatorial optimization problem that minimizes the re-projection error while maximizing the number of detections in the formulated 3D localization problem8286CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPNão temNão temNão temWe would like to thank the CAPES, FAEPEX, FAPESP, and CNPq for funding their research. This paper has content from master degree’s dissertation previously published (Monezi, 2016) and available onlin

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion