Posture Control in Personal Mobility Robots through Pressure Interfaces

Interfaces for shared control of assistive mobility robots are often limited to either joysticks or wearable devices. While recent works have showcased the potential of wearables to promote physical activity, their setup can be cumbersome. This paper explores the potential of non-intrusive methods for controlling robotic wheelchairs, advancing the development of more user-friendly mobility solutions. Using pressure sensors embedded in the wheelchair seat and backrest, our objective is to assess whether a data-based approach can offer advantages over model-based controllers. Our baseline for the model-based controller is the state-of-the-art control methods based on the measured pressure distributions. We compare to this baseline the control performances achieved with data-based approaches. Such methods have the advantage to not require a calibration step. We collected a novel open-source dataset with six different drivers. The dataset, gathered using a commercial pressure mat, can be readily applied to the control of other robotic systems. We successfully demonstrate controllability without the need for wearables or other external systems, paving the way for a zero-shot approach. The dataset and sample code are available at: https://github.com/tchernin/posture-control.