Can Liu

Jiyao Wang, Xiao Yang, Qingyong Hu et al. · tsinghua

Robust and unobtrusive in-vehicle physiological monitoring is crucial for ensuring driving safety and user experience. While remote physiological measurement (RPM) offers a promising non-invasive solution, its translation to real-world driving scenarios is critically constrained by the scarcity of comprehensive datasets. Existing resources are often limited in scale, modality diversity, the breadth of biometric annotations, and the range of captured conditions, thereby omitting inherent real-world challenges in driving. Here, we present PhysDrive, the first large-scale multimodal dataset for contactless in-vehicle physiological sensing with dedicated consideration on various modality settings and driving factors. PhysDrive collects data from 48 drivers, including synchronized RGB, near-infrared camera, and raw mmWave radar data, accompanied with six synchronized ground truths (ECG, BVP, Respiration, HR, RR, and SpO2). It covers a wide spectrum of naturalistic driving conditions, including driver motions, dynamic natural light, vehicle types, and road conditions. We extensively evaluate both signal-processing and deep-learning methods on PhysDrive, establishing a comprehensive benchmark across all modalities, and release full open-source code with compatibility for mainstream public toolboxes. We envision PhysDrive will serve as a foundational resource and accelerate research on multimodal driver monitoring and smart-cockpit systems.

Ran Tong, Jiaqi Liu, Su Liu et al.

We present a compact, strictly causal benchmark for streaming clinical time series on the MIT--BIH Arrhythmia Database using per-second heart rate. Two tasks are studied under record-level, non-overlapping splits: near-term tachycardia risk (next ten seconds) and one-step heart rate forecasting. We compare a GRU-D (RNN) and a Transformer under matched training budgets against strong non-learned baselines. Evaluation is calibration-aware for classification and proper for forecasting, with temperature scaling and grouped bootstrap confidence intervals. On MIT-BIH, GRU-D slightly surpasses the Transformer for tachycardia risk, while the Transformer clearly lowers forecasting error relative to GRU-D and persistence. Our results show that, in longitudinal monitoring, model choice is task-dependent: compact RNNs remain competitive for short-horizon risk scoring, whereas compact Transformers deliver clearer gains for point forecasting.

Can Liu, Shridatt Sugrim, Gradeigh D. Clark et al.

Gesture and signature passwords are two-dimensional figures created by drawing on the surface of a touchscreen with one or more fingers. Prior results about their security have used resilience to either shoulder surfing, a human observation attack, or dictionary attacks. These evaluations restrict generalizability since the results are: non-comparable to other password systems (e.g. PINs), harder to reproduce, and attacker-dependent. Strong statements about the security of a password system use an analysis of the statistical distribution of the password space, which models a best-case attacker who guesses passwords in order of most likely to least likely. Estimating the distribution of recognition passwords is challenging because many different trials need to map to one password. In this paper, we solve this difficult problem by: (1) representing a recognition password of continuous data as a discrete alphabet set, and (2) estimating the password distribution through modeling the unseen passwords. We use Symbolic Aggregate approXimation (SAX) to represent time series data as symbols and develop Markov chains to model recognition passwords. We use a partial guessing metric, which demonstrates how many guesses an attacker needs to crack a percentage of the entire space, to compare the security of the distributions for gestures, signatures, and Android unlock patterns. We found the lower bounds of the partial guessing metric of gestures and signatures are much higher than the upper bound of the partial guessing metric of Android unlock patterns.