We propose an evaluation protocol to benchmark text-to-video (T2V) and image-to-video (I2V) models as implicit simulators of pedestrian dynamics. We use 3D reconstruction and depth estimation to extract pedestrian trajectories without known camera parameters.

We investigate whether image-to-video (I2V) models based on diffusion transformers can generate realistic pedestrian movement patterns in crowded public scenes by conditioning on keyframes from pedestrian benchmark datasets.

We introduce a novel deep-learning-based footstep localization system using ground vibrations measured using geophones and a scalable method to automatically collect training data. We show that it significantly outperforms acoustic localization.

We present a privacy-preserving localization system that uses geophones to map pedestrian locations in outdoor spaces. The system uses a 1D-CNN to compute time differences of arrival (TDOA) and achieves localization accuracy of less than 1 meter.

We develop a shipboard monitoring system using extremely low-power wearable sensors and BLE connectivity. The system measures kinematic and biometric data plus ship environmental conditions for real-time crew health tracking during emergency response scenarios.

We describe an intelligent environment that unifies wearable skin-strain sensors for physiological monitoring, geophones and microphones to record ambient vibrations and sounds, and video cameras to visually observe human activities.

We present a power-efficient wearable sensing platform that uses graphene motion-tape sensors integrated with an IoT sensor node to infer distributed muscle exertion in real time. We use compressed sensing to efficiently transmit signal information.