Pedestrian Traffic Light Control with Crosswalk FMCW Radar and Group Tracking Algorithm.

The increased mobility requirements of modern lifestyles put more stress on existing traffic infrastructure, which causes reduced traffic flow, especially in peak traffic hours. This calls for new and advanced solutions in traffic flow regulation and management. One approach towards optimisation is a transition from static to dynamic traffic light intervals, especially in spots where pedestrian crossing cause stops in road traffic flow. In this paper, we propose a smart pedestrian traffic light triggering mechanism that uses a Frequency-modulated continuous-wave (FMCW) radar for pedestrian detection. Compared to, for example, camera-surveillance systems, radars have advantages in the ability to reliably detect pedestrians in low-visibility conditions and in maintaining privacy. Objects within a radar's detection range are represented in a point cloud structure, in which pedestrians form clusters where they lose all identifiable features. Pedestrian detection and tracking are completed with a group tracking (GTRACK) algorithm that we modified to run on an external processor and not integrated into the used FMCW radar itself. The proposed prototype has been tested in multiple scenarios, where we focused on removing the call button from a conventional pedestrian traffic light. The prototype responded correctly in practically all cases by triggering the change in traffic signalization only when pedestrians were standing in the pavement area directly in front of the zebra crossing.

Training of deep neural networks for the generation of dynamic movement primitives.

Dynamic movement primitives (DMPs) have proven to be an effective movement representation for motor skill learning. In this paper, we propose a new approach for training deep neural networks to synthesize dynamic movement primitives. The distinguishing property of our approach is that it can utilize a novel loss function that measures the physical distance between movement trajectories as opposed to measuring the distance between the parameters of DMPs that have no physical meaning. This was made possible by deriving differential equations that can be applied to compute the gradients of the proposed loss function, thus enabling an effective application of backpropagation to optimize the parameters of the underlying deep neural network. While the developed approach is applicable to any neural network architecture, it was evaluated on two different architectures based on encoder-decoder networks and convolutional neural networks. Our results show that the minimization of the proposed loss function leads to better results than when more conventional loss functions are used.

Effects of robotic knee exoskeleton on human energy expenditure.

A number of studies discuss the design and control of various exoskeleton mechanisms, yet relatively few address the effect on the energy expenditure of the user. In this paper, we discuss the effect of a performance augmenting exoskeleton on the metabolic cost of an able-bodied user/pilot during periodic squatting. We investigated whether an exoskeleton device will significantly reduce the metabolic cost and what is the influence of the chosen device control strategy. By measuring oxygen consumption, minute ventilation, heart rate, blood oxygenation, and muscle EMG during 5-min squatting series, at one squat every 2 s, we show the effects of using a prototype robotic knee exoskeleton under three different noninvasive control approaches: gravity compensation approach, position-based approach, and a novel oscillator-based approach. The latter proposes a novel control that ensures synchronization of the device and the user. Statistically significant decrease in physiological responses can be observed when using the robotic knee exoskeleton under gravity compensation and oscillator-based control. On the other hand, the effects of position-based control were not significant in all parameters although all approaches significantly reduced the energy expenditure during squatting.