Search

Existing statistical data indicates that an increasing number of people now require rehabilitation to restore compromised physical mobility. During the rehabilitation process, physical therapists evaluate and guide the movements of patients, aiding them in a more effective recovery of rehabilitation and preventing secondary injuries. However, the immutability of mobility and the expensive price of rehabilitation training hinder some patients from timely access to rehabilitation. Utilizing virtual reality for rehabilitation training might offer a potential alleviation to these issues. However, prevalent pose reconstruction algorithms in rehabilitation primarily rely on images, limiting their applicability to virtual reality. Furthermore, existing pose evaluation and correction methods in the field of rehabilitation focus on providing clinical metrics for doctors, and failed to offer patients efficient movement guidance. In this paper, a virtual reality-based rehabilitation training method is proposed. The sparse motion signals from virtual reality devices, specifically head-mounted displays hand controllers, is used to reconstruct full body poses. Subsequently, the reconstructed poses and the standard poses are fed into a natural language processing model, which contrasts the difference between the two poses and provides effective pose correction guidance in the form of natural language. Quantitative and qualitative results indicate that the proposed method can accurately reconstruct full body poses from sparse motion signals in real-time. By referencing standard poses, the model generates professional motion correction guidance text. This approach facilitates virtual reality-based rehabilitation training, reducing the cost of rehabilitation training and enhancing the efficiency of self-rehabilitation training.

In recent years, the integration of robots in minimally invasive surgery has gained significant traction in clinical practice. However, conventional contact-based human-computer interaction poses the risk of bacterial infection, significantly limiting the role of robots in surgery. To address this limitation, we propose an innovative interaction method rooted in gestures and visual tags, allowing surgeons to control and fine-tune surgical robots without physical contact with the environment. By encoding the six gestures collected using LeapMotion, we can effectively control the surgical robot in a non-contact manner. Moreover, utilizing Aruco technology, we have accurately identified the 3D spatial position of the visual label, and developed 12 fine-tuning operations to refine surgical instruments. To evaluate the applicability of our proposed system in surgery, we designed a relevant experimental setup. In the experiment, we achieved enough precision. These results demonstrate that our system meets the clinical standard, providing doctors with a non-contact and flexible means of interacting with robots during surgery.

Robot-assisted minimally invasive surgery (RAMIS) has gained significant traction in clinical practice in recent years. However, most surgical robots rely on touch-based human-robot interaction (HRI), which increases the risk of bacterial diffusion. This risk is particularly concerning when surgeons must operate various equipment with their bare hands, necessitating repeated sterilization. Thus, achieving touch-free and precise manipulation with a surgical robot is challenging. To address this challenge, we propose a novel HRI interface based on gesture recognition, leveraging hand-keypoint regression and hand-shape reconstruction methods. By encoding the 21 keypoints from the recognized hand gesture, the robot can successfully perform the corresponding action according to predefined rules, which enables the robot to perform fine-tuning of surgical instruments without the need for physical contact with the surgeon. We evaluated the surgical applicability of the proposed system through both phantom and cadaver studies. In the phantom experiment, the average needle tip location error was 0.51 mm, and the mean angle error was 0.34 degrees. In the simulated nasopharyngeal carcinoma biopsy experiment, the needle insertion error was 0.16 mm, and the angle error was 0.10 degrees. These results indicate that the proposed system achieves clinically acceptable accuracy and can assist surgeons in performing contactless surgery with hand gesture interaction.