ADABase: A Multimodal Dataset for Cognitive Load Estimation.

Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task workloads of increasing intensity on 51 subjects, while continuously measuring signals from multiple modalities, based on physiological measurements such as ECG, EDA, EMG, PPG, respiration rate, skin temperature and eye tracker data, as well as behavioral measurements such as action units extracted from facial videos, performance metrics like reaction time and subjective feedback using questionnaires, we create ADABase (Autonomous Driving Cognitive Load Assessment Database) As a reference method to induce cognitive load onto subjects, we use the well-established n-back test, in addition to our novel simulator-based k-drive test, motivated by real-world semi-autonomously vehicles. We extract expert features of all measurements and find significant changes in multiple modalities. Ultimately we train and evaluate machine learning algorithms using single and multimodal inputs to distinguish cognitive load levels. We carefully evaluate model behavior and study feature importance. In summary, we introduce a novel cognitive load test, create a cognitive load database, validate changes using statistical tests, introduce novel classification and regression tasks for machine learning and train and evaluate machine learning models.

Estimating the 3D pore size distribution of biopolymer networks from directionally biased data.

The pore size of biopolymer networks governs their mechanical properties and strongly impacts the behavior of embedded cells. Confocal reflection microscopy and second harmonic generation microscopy are widely used to image biopolymer networks; however, both techniques fail to resolve vertically oriented fibers. Here, we describe how such directionally biased data can be used to estimate the network pore size. We first determine the distribution of distances from random points in the fluid phase to the nearest fiber. This distribution follows a Rayleigh distribution, regardless of isotropy and data bias, and is fully described by a single parameter--the characteristic pore size of the network. The bias of the pore size estimate due to the missing fibers can be corrected by multiplication with the square root of the visible network fraction. We experimentally verify the validity of this approach by comparing our estimates with data obtained using confocal fluorescence microscopy, which represents the full structure of the network. As an important application, we investigate the pore size dependence of collagen and fibrin networks on protein concentration. We find that the pore size decreases with the square root of the concentration, consistent with a total fiber length that scales linearly with concentration.

Biphasic response of cell invasion to matrix stiffness in three-dimensional biopolymer networks.

When cells come in contact with an adhesive matrix, they begin to spread and migrate with a speed that depends on the stiffness of the extracellular matrix. On a flat surface, migration speed decreases with matrix stiffness mainly due to an increased stability of focal adhesions. In a three-dimensional (3-D) environment, cell migration is thought to be additionally impaired by the steric hindrance imposed by the surrounding matrix. For porous 3-D biopolymer networks such as collagen gels, however, the effect of matrix stiffness on cell migration is difficult to separate from effects of matrix pore size and adhesive ligand density, and is therefore unknown. Here we used glutaraldehyde as a crosslinker to increase the stiffness of self-assembled collagen biopolymer networks independently of collagen concentration or pore size. Breast carcinoma cells were seeded onto the surface of 3-D collagen gels, and the invasion depth was measured after 3 days of culture. Cell invasion in gels with pore sizes >5 µm increased with higher gel stiffness, whereas invasion in gels with smaller pores decreased with higher gel stiffness. These data show that 3-D cell invasion is enhanced by higher matrix stiffness, opposite to cell behavior in two dimensions, as long as the pore size does not fall below a critical value where it causes excessive steric hindrance. These findings may be important for optimizing the recellularization of soft tissue implants or for the design of 3-D invasion models in cancer research.