The invention of soft wearable assistive devices, known as exosuits, introduced a new aspect in assisting unimpaired subjects. In this study, we designed and developed an exosuit with compliant biarticular thigh actuators called BATEX. Unlike the conventional method of using rigid actuators in exosuits, the BATEX is made of serial elastic actuators (SEA) resembling artificial muscles. This bioinspired design is complemented by the novel control concept of using the ground reaction force to adjust the artificial muscles’ stiffness in the stance phase. By locking the motors in the swing phase, the SEAs will be simplified to passive biarticular springs, which is sufficient for leg swinging. The key concept in our design and control approach is to synthesize human locomotion to develop an assistive device instead of copying human motor control outputs. Analyzing human walking assistance using experiment-based OpenSim simulations demonstrates the advantages of the proposed design and control of BATEX, such as 9.4% reduction in metabolic cost during normal walking condition. This metabolic reduction increases to 10.4% when the subjects carry a 38 kg load. The adaptability of our proposed model-based control to such an unknown condition outperforms the assistance level of the model-free optimal controller. Moreover, increasing the assistive system’s efficiency by adjusting the actuator compliance with the force feedback supports our previous findings on the LOPES II exoskeleton.

Spinal fusion surgery requires highly accurate implantation of pedicle screw implants, which must be conducted in critical proximity to vital structures with a limited view of the anatomy. Robotic surgery systems have been proposed to improve placement accuracy. Despite remarkable advances, current robotic systems still lack advanced mechanisms for continuous updating of surgical plans during procedures, which hinders attaining higher levels of robotic autonomy. These systems adhere to conventional rigid registration concepts, relying on the alignment of preoperative planning to the intraoperative anatomy. In this paper, we propose a safe deep reinforcement learning (DRL) planning approach (SafeRPlan) for robotic spine surgery that leverages intraoperative observation for continuous path planning of pedicle screw placement. The main contributions of our method are (1) the capability to ensure safe actions by introducing an uncertainty-aware distance-based safety filter; (2) the ability to compensate for incomplete intraoperative anatomical information, by encoding a-priori knowledge of anatomical structures with neural networks pre-trained on pre-operative images; and (3) the capability to generalize over unseen observation noise thanks to the novel domain randomization techniques. Planning quality was assessed by quantitative comparison with the baseline approaches, gold standard (GS) and qualitative evaluation by expert surgeons. In experiments with human model datasets, our approach was capable of achieving over 5% higher safety rates compared to baseline approaches, even under realistic observation noise. To the best of our knowledge, SafeRPlan is the first safety-aware DRL planning approach specifically designed for robotic spine surgery.

Minimally invasive pedicle screw placement (MIPSP) is a widely used treatment for spine diseases. When coupled with intraoperative navigation modalities, robots may help improve surgical outcomes and reduce complications. With such a system, the application of pedicle screws has been expanded from needle insertion to the spine surgery. This paper investigates the possibility and feasibility of robot-assisted MIPSP based on ultrasound (US) guidance. The proposed system is non-radiative and fiducial-free, using purely image information to close the registration loop. Then the system automatically positions the drill tip to a planned screw trajectory and executes the drilling operation. Experiments were conducted on both ex-vivo lamb and human cadaver spines. An entry point accuracy of 2.39±1.41 mm, and orientation accuracy of 2.82±1.85∘ was found for 24 drilled trajectories on three lamb spines. On the ex-vivo human spine, the position error averaged 3.08±2.43 mm at the entry point and 4.05±2.62 mm at the stop point across 16 drilling instances. Moreover, a 87.5% success rate was reported by using Gertzbein-Robbins grade. The experimental results demonstrate the potential for offering a radiation-free alternative. Although restricted to cadaver trials, this work encourages further exploration of this technology to assist surgeons in maximizing performance in clinical practice.

A more human-compatible design of exosuits provides new aspects in assisting people. This study presents a novel bioinspired method to design and control an exosuit to support human locomotion. We introduce a methodological design and development of the BATEX as an exosuit with compliant biarticular thigh actuators. By using series elastic actuator (SEA) resembling artificial muscles (AM) instead of direct drive motors in the BATEX, we propose an efficient switching control for the stance and swing phases of walking. Locking the motors in the swing phase converts the SEAs to passive biarticular springs to produce human-like leg swinging. This design is complemented by the force modulated compliance (FMC) controller that uses the ground reaction force to adjust the AM's stiffness in the stance phase. We utilized unassisted walking experiment data to validate the proposed design and control methodology in a simulation study. Optimizing BATEX control parameters with a training set of subjects predicts the energy consumption reduction in the upper leg (knee and hip joints) for more than 27% at five different walking speeds. Finding this measure of assistance level to be more than 21% for the test set of subjects supports the generalization of our approach. Further, individualization of the parameters for each subject shows significant improvement in slow and moderate walking speeds. Pilot assisted-walking experiments with four subjects support the applicability of the introduced method concerning individualization and generalization.

In the research field of robotic spine surgery, there is a big upcoming momentum for surgeon-like autonomous behaviour and surgical accuracy in robotics which goes beyond the standard engineering notions such as geometric precision. The objective of this review is to present an overview of the state of the art in non-visual, non-radiative spine sensing for the enhancement of surgical techniques in robotic automation. It provides a vantage point that facilitates experimentation and guides new research projects to what has not been investigated or integrated in surgical robotics. Studies were identified, selected and processed according to the PRISMA guidelines. Relevant study characteristics that were searched for include the sensor type and measured feature, the surgical action, the tested sample, the method for data analysis and the system’s accuracy of state identification. The 6DOF f/t sensor, the microphone and the electromyography probe were the most commonly used sensors in each category, respectively. The performance of the electromyography probe is unsatisfactory in terms of preventing nerve damage as it can only signal after the nerve is disturbed. Feature thresholding and artificial neural networks were the most common decision algorithms for state identification. The fusion of different sensor data in the decision algorithm improved the accuracy of state identification.