Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos

We consider the problem of smartphone video-based heart rate estimation, which typically relies on measuring the green color intensity of the user's skin. We describe a novel signal in fingertip videos used for smartphone-based heart rate estimation: fingertip contact surface area. We propose a model relating contact surface area to pressure, and validate it on a dataset of 786 videos from 62 participants by demonstrating a statistical correlation between contact surface area and green color intensity. We estimate heart rate on our dataset with two algorithms, a baseline using the green signal only and a novel algorithm based on both color and area. We demonstrate lower rates of substantial errors (>10 beats per minute) using the novel algorithm (4.1%), compared both to the baseline algorithm (6.4%) and to published results using commercial color-based applications (>6%)

Inferring Occluded Agent Behavior in Dynamic Games with Noise-Corrupted Observations

Robots and autonomous vehicles must rely on sensor observations, e.g., from lidars and cameras, to comprehend their environment and provide safe, efficient services. In multi-agent scenarios, they must additionally account for other agents' intrinsic motivations, which ultimately determine the observed and future behaviors. Dynamic game theory provides a theoretical framework for modeling the behavior of agents with different objectives who interact with each other over time. Previous works employing dynamic game theory often overlook occluded agents, which can lead to risky navigation decisions. To tackle this issue, this paper presents an inverse dynamic game technique which optimizes the game model itself to infer unobserved, occluded agents' behavior that best explains the observations of visible agents. Our framework concurrently predicts agents' future behavior based on the reconstructed game model. Furthermore, we introduce and apply a novel receding horizon planning pipeline in several simulated scenarios. Results demonstrate that our approach offers 1) robust estimation of agents' objectives and 2) precise trajectory predictions for both visible and occluded agents from observations of only visible agents. Experimental findings also indicate that our planning pipeline leads to safer navigation decisions compared to existing baseline methods

Game-theoretic Occlusion-Aware Motion Planning: an Efficient Hybrid-Information Approach

We present a novel algorithm for motion planning in complex, multi-agent scenarios in which occlusions prevent all agents from seeing one another. In this setting, the fundamental information that each agent has, i.e., the information structure of the interaction, is determined by the precise configurations in which agents come into view of one another. Occlusions prevent the use of existing pure feedback solutions, which assume availability of the state information of all agents at every time step. On the other hand, existing open-loop solutions only assume availability of the initial agent states. Thus, they do not fully utilize the information available to agents during periods of unhampered visibility. Here, we first introduce an algorithm for solving an occluded, linear-quadratic (LQ) dynamic game, which computes Nash equilibrium by using hybrid information and switching between feedback and open-loop information structures. We then design an efficient iterative algorithm for decision-making which exploits this hybrid information structure. Our method is demonstrated in overtaking and intersection traffic scenarios. Results confirm that our method outputs trajectories with favorable running times, converging much faster than recent methods employing reachability analysis