Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities.

The use of artificial intelligence (AI) in a variety of research fields is speeding up multiple digital revolutions, from shifting paradigms in healthcare, precision medicine and wearable sensing, to public services and education offered to the masses around the world, to future cities made optimally efficient by autonomous driving. When a revolution happens, the consequences are not obvious straight away, and to date, there is no uniformly adapted framework to guide AI research to ensure a sustainable societal transition. To answer this need, here we analyze three key challenges to interdisciplinary AI research, and deliver three broad conclusions: 1) future development of AI should not only impact other scientific domains but should also take inspiration and benefit from other fields of science, 2) AI research must be accompanied by decision explainability, dataset bias transparency as well as development of evaluation methodologies and creation of regulatory agencies to ensure responsibility, and 3) AI education should receive more attention, efforts and innovation from the educational and scientific communities. Our analysis is of interest not only to AI practitioners but also to other researchers and the general public as it offers ways to guide the emerging collaborations and interactions toward the most fruitful outcomes.

Active impedance control of a knee-joint orthosis during swing phase.

In this paper, an active impedance control strategy for a knee-joint orthosis is proposed to assist individuals suffering from lower-limb muscular weaknesses during the swing phase of walking activities. The goal of the proposed strategy is to decrease the human effort required for ensuring a successful knee joint movement during walking without sacrificing the wearer's control priority. In this study, a gait-phase based desired knee-joint admittance model is designed by analyzing the kinematic and kinetic characteristics of the wearer's shank-foot segment during walking. Moreover, the mechanical impedance of the human/orthosis system is adapted to the desired one using an active impedance compensation. The control approach was implemented using a knee joint orthosis and tested with four healthy subjects. The EMG signals of the short head of the biceps femoris and the vastus medialis are used as metrics to evaluate the effectiveness of the proposed strategy. The experimental results show that the human effort can be significantly decreased when the wearers are assisted using the proposed approach.