The unprecedented shock caused by the COVID-19 pandemic has severely influenced the delivery of regular healthcare services. Most non-urgent medical activities, including elective surgeries, have been paused to mitigate the risk of infection and to dedicate medical resources to managing the pandemic. In this regard, not only surgeries are substantially influenced, but also pre- and post-operative assessment of patients and training for surgical procedures have been significantly impacted due to the pandemic. Many countries are planning a phased reopening, which includes the resumption of some surgical procedures. However, it is not clear how the reopening safe-practice guidelines will impact the quality of healthcare delivery. This perspective article evaluates the use of robotics and AI in 1) robotics-assisted surgery, 2) tele-examination of patients for pre- and post-surgery, and 3) tele-training for surgical procedures. Surgeons interact with a large number of staff and patients on a daily basis. Thus, the risk of infection transmission between them raises concerns. In addition, pre- and post-operative assessment also raises concerns about increasing the risk of disease transmission, in particular, since many patients may have other underlying conditions, which can increase their chances of mortality due to the virus. The pandemic has also limited the time and access that trainee surgeons have for training in the OR and/or in the presence of an expert. In this article, we describe existing challenges and possible solutions and suggest future research directions that may be relevant for robotics and AI in addressing the three tasks mentioned above.

functionality. The desired trajectory of the grasping actions is gathered from a subject with amputated intermediate and distal phalanges of the index finger. Experimental data are obtained from the subject while holding circular objects of specific sizes. The performance of the fabricated mechanism is compared and discussed with regards to simulation results and the acquired data from the subject while performing similar tasks with the healthy finger.

Establishing a validated dynamic model for human daily life movements is a crucial step in designing assistive devices for people with disabilities. Sit-to-stand motion with fixed support is a frequent task in daily life, and in order to provide assistive devices for such task, a validated dynamic model is required. The main contribution of this paper is to present a multi-body dynamic model of sit-to-stand motion with fixed support and to validate the model with experimental results from human joint tracking and force sensors on the support. Dynamic equations governing the body motion during sitto-stand are derived using a multi-body model interconnected to the fixed support through handling muscle force. It is hypothesized that sit-to-stand motion is performed by optimized support force and control effort by the subject. In this regard, the human body attempt for sit-to-stand motion is distributed among various joints such that an energy-based cost function is optimized, and at the same time, the choice of biomechanical actuation is prioritized through adjusting various weighting coefficients in the objective function. The limitations on joint torques and the hand force are included as inequality constraints in the optimization problem. The developed multi-body dynamic model is evaluated through comparison between the computed support force (theoretical) and the measured force (experimental) obtained from a fixed support equipped with three-axis force sensors. The proposed dynamic model uses the visual data that are recorded to track the skeleton configuration of the subjects as the desired trajectory to follow by the controller. A set of experimental data from five healthy subjects is obtained, and the results show acceptable agreement between simulation and experimental results. The results suggest energy-based optimization solution can be considered as a valid approach is multi-body dynamic modelling of sit-to-stand task.

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