In order to develop provably safe human-in-the-loop systems, accurate and precise models of human behavior must be developed. Driving is a good example of such a system because the driver has full control of the vehicle, and her likely actions are highly dependent on her mental state and the context of the current situation. This paper presents a testbed for collecting driver data that allows us to collect realistic data, while maintaining safety and control of the environmental surroundings. We extend previous work that focuses on set predictions consisting of trajectories observed from the nonlinear dynamics and behaviors of the human driven car, accounting for the driver mental state, the context or situation that the vehicle is in, and the surrounding environment in both highway and intersection scenarios. This allows us to predict driving behavior over long time horizons with extremely high accuracy. By using this realistic data and flexible algorithm, a precise and accurate driver model can be developed that is tailored to an individual and usable in semi-autonomous frameworks.

Threat assessment during semiautonomous driving is used to determine when correcting a driver's input is required. Since current semiautonomous systems perform threat assessment by predicting a vehicle's future state while treating the driver's input as a disturbance, autonomous controller intervention is limited to a restricted regime. Improving vehicle safety demands threat assessment that occurs over longer prediction horizons wherein a driver cannot be treated as a malicious agent. In this paper, we describe a real-time semiautonomous system that utilizes empirical observations of a driver's pose to inform an autonomous controller that corrects a driver's input when possible in a safe manner. We measure the performance of our system using several metrics that evaluate the informativeness of the prediction and the utility of the intervention procedure. A multisubject driving experiment illustrates the usefulness, with respect to these metrics, of incorporating the driver's pose while designing a semiautonomous system.

Within biomedical engineering, the use of wearable wireless accelerometers for gait analysis can provide useful information for multiple health-related applications. The minimization of hardware for an accurate and simple estimation of the patient's velocity and stride length represents a difficult task. In this paper we propose a new methodology to determine the velocity and stride length of the patient through the application of the wavelet transform to the waist acceleration signal minimizing the hardware requirements. We introduce 4 novel formulations to take into account the trade-off between accuracy and computational costs, showing that an adaptive optimum approach can deliver results with average errors below 5%.