Dynamic Gesture Recognition Using a Smart Glove in Hand-Assisted Laparoscopic Surgery

A Framework for the Evaluation of Human Machine Interfaces of Robot-Assisted Colonoscopy

The Human Machine Interface (HMI) of intraluminal robots has a crucial impact on the clinician's performance. It increases or decreases the difficulty of the tasks, and is connected to the users' physical and mental stress.

OBJECTIVE

This article presents a framework to compare and evaluate different HMIs for robotic colonoscopy, with the objective of identifying the optimal HMI that minimises the clinician's effort and maximises the clinical outcomes.

METHODS

The framework comprises a 1) a virtual simulator (clinically validated), 2) wearable sensors measuring the cognitive load, 3) a data collection unit of metrics correlated to the clinical performance, and 4) questionnaires exploring the users' impressions and perceived stress. The framework was tested with 42 clinicians investigating the optimal device for tele-operated control of robotic colonoscopes. Two control devices were selected and compared: a haptic serial-kinematic device and a standard videogame joypad.

RESULTS

The haptic device was preferred by the endoscopists, but the joypad enabled better clinical performance and reduced cognitive and physical load.

CONCLUSION

The framework can be used to evaluate different aspects of a HMI, both hardware and software, and determine the optimal HMI that can reduce the burden on clinicians while improving the clinical outcome.

SIGNIFICANCE

The findings of this study, and of future studies performed with this framework, can inform the design and development of HMIs for intraluminal robots, leading to improved clinical performance, reduced physical and mental stress for clinicians, and ultimately better patient outcomes.

Hybrid-Flexible Bimodal Sensing Wearable Glove System for Complex Hand Gesture Recognition

As 5G communication technology allows for speedier access to extended information and knowledge, a more sophisticated human-machine interface beyond touchscreens and keyboards is necessary to improve the communication bandwidth and overcome the interfacing barrier. However, the full extent of human interaction beyond operation dexterity, spatial awareness, sensory feedback, and collaborative capability to be replicated completely remains a challenge. Here, we demonstrate a hybrid-flexible wearable system, consisting of simple bimodal capacitive sensors and a customized low power interface circuit integrated with machine learning algorithms, to accurately recognize complex gestures. The 16 channel sensor array extracts spatial and temporal information of the finger movement (deformation) and hand location (proximity) simultaneously. Using machine learning, over 99 and 91% accuracy are achieved for user-independent static and dynamic gesture recognition, respectively. Our approach proves that an extremely simple bimodal sensing platform that identifies local interactions and perceives spatial context concurrently, is crucial in the field of sign communication, remote robotics, and smart manufacturing.

Comparison of E-Textile Techniques and Materials for 3D Gesture Sensor with Boosted Electrode Design

There is an interest in new wearable solutions that can be directly worn on the curved human body or integrated into daily objects. Textiles offer properties that are suitable to be used as holders for electronics or sensors components. Many sensing technologies have been explored considering textiles substrates in combination with conductive materials in the last years. In this work, a novel solution of a gesture recognition touchless sensor is implemented with satisfactory results. Moreover, three manufacturing techniques have been considered as alternatives: screen-printing with conductive ink, embroidery with conductive thread and thermosealing with conductive fabric. The main critical parameters have been analyzed for each prototype including the sensitivity of the sensor, which is an important and specific parameter of this type of sensor. In addition, user validation has been performed, testing several gestures with different subjects. During the tests carried out, flick gestures obtained detection rates from 79% to 89% on average. Finally, in order to evaluate the stability and strength of the solutions, some tests have been performed to assess environmental variations and washability deteriorations. The obtained results are satisfactory regarding temperature and humidity variations. The washability tests revealed that, except for the screen-printing prototype, the sensors can be washed with minimum degradation.

A Wearable Textile 3D Gesture Recognition Sensor Based on Screen-Printing Technology

Research has developed various solutions in order for computers to recognize hand gestures in the context of human machine interface (HMI). The design of a successful hand gesture recognition system must address functionality and usability. The gesture recognition market has evolved from touchpads to touchless sensors, which do not need direct contact. Their application in textiles ranges from the field of medical environments to smart home applications and the automotive industry. In this paper, a textile capacitive touchless sensor has been developed by using screen-printing technology. Two different designs were developed to obtain the best configuration, obtaining good results in both cases. Finally, as a real application, a complete solution of the sensor with wireless communications is presented to be used as an interface for a mobile phone.

Advantage of Steerable Catheter and Haptic Feedback for a 5-DOF Vascular Intervention Robot System

Vascular intervention involves inserting a catheter and guidewire into blood vessels to diagnose and treat a disease in an X-ray environment. In this conventional vascular intervention procedure, the doctor is exposed to considerable radiation. To reduce the exposure, we developed a master–slave robot system. A steerable catheter is employed to shorten the task-time and reduce the contact force applied to the vessel walls during catheter insertion. The steerable catheter helps to select a vascular branch; thus, the radiation exposure time for patients is reduced, and perforation in the patient’s vessel is prevented. Additionally, the robot system employs a haptic function to replicate the physician’s tactile sensing in vascular intervention. In this study, the effectiveness of the steering catheter and haptic function was demonstrated experimentally in comparison with a conventional catheter.

Augmented visualization with depth perception cues to improve the surgeon's performance in minimally invasive surgery

Minimally invasive techniques, such as laparoscopy and radiofrequency ablation of tumors, bring important advantages in surgery: by minimizing incisions on the patient's body, they can reduce the hospitalization period and the risk of postoperative complications. Unfortunately, they come with drawbacks for surgeons, who have a restricted vision of the operation area through an indirect access and 2D images provided by a camera inserted in the body. Augmented reality provides an "X-ray vision" of the patient anatomy thanks to the visualization of the internal organs of the patient. In this way, surgeons are free from the task of mentally associating the content from CT images to the operative scene. We present a navigation system that supports surgeons in preoperative and intraoperative phases and an augmented reality system that superimposes virtual organs on the patient's body together with depth and distance information. We implemented a combination of visual and audio cues allowing the surgeon to improve the intervention precision and avoid the risk of damaging anatomical structures. The test scenarios proved the good efficacy and accuracy of the system. Moreover, tests in the operating room suggested some modifications to the tracking system to make it more robust with respect to occlusions. Graphical Abstract Augmented visualization in minimally invasive surgery.