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Physiological measures, such as pain, anxiety, effort, or energy consumption, play a crucial role in the evaluation and development of assistive robotic devices. Physiological data are collected and analyzed by researchers and clinicians, and are often used to inform an iterative tuning process of a device and its controller. Currently, these data are collected then analyzed offline such that they are only evaluated after the experiment has ended. This makes any iterative design process tedious and time consuming since tuning must be done on a subject-by-subject basis and for a variety of tasks that the device is intended to be used for. To overcome these drawbacks, we are proposing a new type of human-machine interaction that is based on measuring and using physiological measurements in real-time. By continuously monitoring a physiological objective through a set of suitable sensors, we propose conducting an optimization of a set of controller parameters that shape the assistance provided by the device. In other words, we pose an optimization that includes the human body in the loop. This Body-in-the-Loop optimization allows for optimal subject specific control and has the potential to be used for controller adaptation to changing environments. We validated this concept in an extensive human subject study where we autonomously optimized the actuation onset of a pair of bilateral ankle exoskeletons to minimize user’s metabolic effort.

'Body-in-the-Loop' Optimization of Assistive Robotic Devices: A Validation Study