Robotic arm-assisted arthroplasty: The latest developments / 中华创伤杂志(英文版)

Joint arthroplasty is an effective method for treating end-stage joint lesions and damages. Robotic arm-assisted arthroplasty, a rapidly developing technology that combines navigation technology, minimally invasive technology, and precise control technology of the robotic arm, can achieve accurate preoperative planning, optimal selection of implants, minimally invasive surgery, precise osteotomy, and accurate placement of the artificial joint. It has the characteristics of high accuracy and stability, and thus is more and more widely used in the field of joint surgery. In this paper, we systematically reviewed the application and clinical efficacy of robotic arm-assisted technology in hip and knee arthroplasty to provide reference for its future promotion.

Robotic arm control system based on augmented reality brain-computer interface and computer vision / 生物医学工程学杂志

Brain-computer interface (BCI) has great potential to replace lost upper limb function. Thus, there has been great interest in the development of BCI-controlled robotic arm. However, few studies have attempted to use noninvasive electroencephalography (EEG)-based BCI to achieve high-level control of a robotic arm. In this paper, a high-level control architecture combining augmented reality (AR) BCI and computer vision was designed to control a robotic arm for performing a pick and place task. A steady-state visual evoked potential (SSVEP)-based BCI paradigm was adopted to realize the BCI system. Microsoft's HoloLens was used to build an AR environment and served as the visual stimulator for eliciting SSVEPs. The proposed AR-BCI was used to select the objects that need to be operated by the robotic arm. The computer vision was responsible for providing the location, color and shape information of the objects. According to the outputs of the AR-BCI and computer vision, the robotic arm could autonomously pick the object and place it to specific location. Online results of 11 healthy subjects showed that the average classification accuracy of the proposed system was 91.41%. These results verified the feasibility of combing AR, BCI and computer vision to control a robotic arm, and are expected to provide new ideas for innovative robotic arm control approaches.

Role of robotic arm assistance in computed tomography guided bone biopsy

Background: Use of robotic assistance technique has significant benefits over conventional techniques. The present study looks at the recent technological developments in image guidance for bone biopsy procedures.Methods: Patients who were referred to the department of radiodiagnosis, Bharti Hospital and Dot3d scanning center, Sangli, Maharashtra, India from July 2017 till December 2018 with suspected bone lesions were included in the study. These patients underwent robotic arm CT guided bone biopsy of their lesions.Results: In the present study, 47 patients were included. Authors observed that 93.6% had a positive diagnosis based on CT guided bone biopsy. Metastatic lesions were diagnosed in 8 cases. Inflammatory lesions and tuberculosis were other commonly observed diagnosis.Conclusions: Further growth and development of medical imaging devices have allowed more interventional procedures to be performed and more patients to benefit from them. Radiologists needs to develop a thorough understanding of the anatomical structure involved and need to acquire both solid grounding in technology and the practical skills to visualize a nerve structure.

Kinematics parameter identification and accuracy evaluation method for neurosurgical robot / 生物医学工程学杂志

The kinematic model parameter deviation is the main factor affecting the positioning accuracy of neurosurgical robots. To obtain more realistic kinematic model parameters, this paper proposes an automatic parameters identification and accuracy evaluation method. First, an identification equation contains all robot kinematics parameter was established. Second, a multiple-pivot strategy was proposed to find the relationship between end-effector and tracking marker. Then, the relative distance error and the inverse kinematic coincidence error were designed to evaluate the identification accuracy. Finally, an automatic robot parameter identification and accuracy evaluation system were developed. We tested our method on both laboratory prototypes and real neurosurgical robots. The results show that this method can realize the neurosurgical robot kinematics model parameters identification and evaluation stably and quickly. Using the identified parameters to control the robot can reduce the robot relative distance error by 33.96% and the inverse kinematics consistency error by 67.30%.