Understanding the underpinnings of biological motor control is an important issue in movement neuroscience. Optimal control theory is a leading framework to rationalize this problem in computational terms. Previously, optimal control models have been devised either in deterministic or in stochastic settings to account for different aspects of motor control (e.g. average behavior versus trial-to-trial variability). While these approaches have yielded valuable insights about motor control, they typically fail in explaining muscle co-contraction. Co-contraction of a group of muscles associated to a motor function (e.g. agonist and antagonist muscles spanning a joint) contributes to modulate the mechanical impedance of the neuromusculoskeletal system (e.g. joint viscoelasticity) and is thought to be mainly under the influence of descending signals from the brain. Here we present a theory suggesting that one primary goal of motor planning may be to issue feedforward (open-loop) motor commands that optimally specify both force and impedance, according to noisy neuromusculoskeletal dynamics and to optimality criteria based on effort and variance. We show that the proposed framework naturally accounts for several previous experimental findings regarding the regulation of force and impedance via muscle co-contraction in the upper-limb. Stochastic optimal (closed-loop) control, preprogramming feedback gains but requiring on-line state estimation processes through long-latency sensory feedback loops, may then complement this nominal feedforward motor command to fully determine the limb's mechanical impedance. The proposed stochastic optimal open-loop control theory may provide new insights about the general articulation of feedforward/feedback control mechanisms and justify the occurrence of muscle co-contraction in the neural control of movement.

This paper focuses on video summarization of abnormal behavior for remote invigilation of online exams. While the last decade has seen a massive increase in e-learning and online courses offered at postsecondary institutions, preserving the integrity of online examinations still heavily relies on web video conference invigilation performed by a remote proctor. Live remote invigilation is limited in the number of students that can be handled at once, and manual post-exam review is labor intensive. We propose a novel computer vision-based video content analysis system for the automatic creation of video summaries of online exams to assist remote proctors in post-exam reviews. The proposed method models normal and abnormal student behavior patterns using head pose estimations and a semantically meaningful two-state hidden Markov model. Video summaries are created from detected sequences of abnormal behavior. Experimental results are promising and demonstrate the viability of the proposed approach, which could readily be expanded to generate real-time alerts for live remote invigilation.

In this paper, we present a challenging dataset for the purpose of segmentation and change detection in photographic images of mountain habitats. We also propose a baseline algorithm for habitats segmentation to allow for performance comparison. The dataset consists of high resolution image pairs of historic and repeat photographs of mountain habitats acquired in the Canadian Rocky Mountains for ecological surveys. With a time lapse of 70 to 100 years between the acquisition of historic and repeat images, these photographs contain critical information about ecological change in the Rockies. The challenging aspects of analyzing these image pairs come mostly from the perspective (oblique) view of the photographs and the lack of color information in the historic photographs. The baseline algorithm that we propose here is based on texture analysis and machine learning techniques. Classifier training and results validation are made possible by the availability of expert manual ground-truth segmentation for each image. The results obtained with the baseline algorithm are promising and serve as a reference for new and improved segmentation and change detection algorithms.

This paper presents the results of a comparison study using the Visual VIPERS interface, a graphical interface which can be applied as an educational tool for novice computer vision students. The goal of the study was to evaluate the change in usability of the interface after the addition of a monitor tool, which can be used to view intermediate image results at specific stages of an algorithm. A user study was conducted in which participants were asked to find an error in a pre-assembled algorithm. Results indicate that participants using the older version of the interface (with no monitor tool) took, on average, less time to find the error than participants who used the monitor tool. However, interview responses indicated a greater level of understanding of the algorithm from participants who used the monitor tool. Interview responses also demonstrated a clear desire from users of the old interface for the addition of a debugging tool (such as the newly introduced monitor tool). Our belief is that participants using the monitor tool performed a more thorough search of the algorithm, and thus gained a greater understanding of how the algorithm operated, while attempting to determine the source of the error.

This study focuses on biomimetic sensory motor control of a robotic arm. We have developed a command circuit that was mathematically deduced from physical and mathematical constraints describing the function of cerebellar pathways. The control circuit contains an internal predictive model of the direct biomechanical function of the limb placed in a closed loop, so that the circuit computes an approximate inverse function. The structure of the model resembles the anatomic connectivity of the cerebellar pathways. In this paper, we present an application of this model to the control of a 2-link robotic arm actuated by four single-joint McKibben muscles and report the results obtained by simulation and real-time learning of 2 degrees of freedom pointing movements.

This paper proposes a method to obtain fronto-parallel (side-view) body part trajectories for a walk observed from an arbitrary view. The method is based on homography transformations computed for each gait half-cycle detected in the walk. Each homography maps the body part trajectories to a simulated side view of the walk. The proposed method is stable as the resulting normalized trajectories are not influenced by missing or omitted parts of the raw trajectories. Experiments confirm that normalized trajectories are in agreement with the ones that would be obtained from a side view.

An important question in the literature focusing on motor control is to determine which laws drive biological limb movements. This question has prompted numerous investigations analyzing arm movements in both humans and monkeys. Many theories assume that among all possible movements the one actually performed satisfies an optimality criterion. In the framework of optimal control theory, a first approach is to choose a cost function and test whether the proposed model fits with experimental data. A second approach (generally considered as the more difficult) is to infer the cost function from behavioral data. The cost proposed here includes a term called the absolute work of forces, reflecting the mechanical energy expenditure. Contrary to most investigations studying optimality principles of arm movements, this model has the particularity of using a cost function that is not smooth. First, a mathematical theory related to both direct and inverse optimal control approaches is presented. The first theoretical result is the Inactivation Principle, according to which minimizing a term similar to the absolute work implies simultaneous inactivation of agonistic and antagonistic muscles acting on a single joint, near the time of peak velocity. The second theoretical result is that, conversely, the presence of non-smoothness in the cost function is a necessary condition for the existence of such inactivation. Second, during an experimental study, participants were asked to perform fast vertical arm movements with one, two, and three degrees of freedom. Observed trajectories, velocity profiles, and final postures were accurately simulated by the model. In accordance, electromyographic signals showed brief simultaneous inactivation of opposing muscles during movements. Thus, assuming that human movements are optimal with respect to a certain integral cost, the minimization of an absolute-work-like cost is supported by experimental observations. Such types of optimality criteria may be applied to a large range of biological movements.