Penetration detection with intention recognition for cooperatively controlled robotic needle insertion

Abstract

In lumbar puncture surgeries, force and position information throughout the insertion procedure is vital for needle tip localization, because it reflects different tissue properties. Especially in pediatric cases, the changes are always insignificant for surgeons to sense the crucial feeling of loss of resistance. In this study, a robot system is developed to tackle the major clinical difficulties. Four different control algorithms with intention recognition ability are applied on a novel lumbar puncture robot system for better human–robot cooperation. Specific penetration detection based on force and position derivatives captures the feeling of loss of resistance, which is deemed crucial for needle tip location. Kinematic and actuation modeling provides a clear description of the hardware setup. The control algorithm experiment compares the human–robot cooperation performance of proposed algorithms. The experiment also dictates the clear role of designed penetration detection criteria in capturing the penetration, improving the success rate, and ensuring operational safety.

An admittance‐controlled amplified force tracking scheme for collaborative lumbar puncture surgical robot system

“…The experimental setup is shown in Figure 5. It was a four degree of freedom robot developed at the Shanghai Jiao Tong University for medical application [25][26][27][28] For the experiments reported in this section, all the joints of the robot, except one degree of freedom of the section of this study focuses on both the system performance of the free motion and constrained situation. The following two system properties will be verified in the collaborative lumbar puncture robotic system to ensure stable manipulation with low effort, namely:…”

“…The collaborative lumbar puncture robot system was a four degree of freedom robot developed at the Shanghai Jiao Tong University for medical needle insertion applications. [25][26][27][28] The dynamic model of the surgical robot in the joint space is of the form:…”

“…The collaborative lumbar puncture robot system was a four degree of freedom robot developed at the Shanghai Jiao Tong University for medical needle insertion applications 25–28 . The dynamic model of the surgical robot in the joint space is of the form: $$$M(q)\ddot{q}+C\left(q,\dot{q}\right)\dot{q}+g(q)+{\tau }_{f}={\tau }_{c}+{J}^{T}(q){f}_{ext}$$$ where $$q\in {\mathbb{R}}^{n}$$, with $$n=4$$, is the vector of the joint variables, $$\dot{q},\ddot{q}\in {\mathbb{R}}^{n}$$ are the velocity and acceleration of the joint angles, $$M(q)$$ denotes the inertia matrix, $$C\left(q,\dot{q}\right)\dot{q}$$ is the vector of the Coriolis/centrifugal torques, $$g(q)$$ is the vector of the gravitational torques, $${\tau }_{f}$$ is the vector of the joint friction torques, $${\tau }_{c}$$ denotes the control input to the robot actuators, $$J(q)$$ is the robot Jacobian, and …”

Factors affecting trust in the transparency index for stable and intuitive physical human–robot cooperation

“…The ways human and robot work together, human–robot cooperation (HRC), are expected to play an important role in scenarios such as industrial applications (Lin et al, 2022; Malik and Brem, 2021), surgery (Wang and Li, 2022), rehabilitation (Shi et al, 2021), and manipulations in hazardous environments (Schmaus et al, 2020). Humans are expected to work alongside collaborative robots to integrate the benefits of both humans and robots in executing tasks (Darvish et al, 2021).…”

Development and evaluation of a robotic system for lumbar puncture and epidural steroid injection

“…Halter and Kim (2014) We refer to some related studies using various approaches to detect the subarachnoid space. Several methods use tactile devices that monitor the force feedback and its change or derivative as the needle tip penetrates through different soft tissue layers (Singh et al, 1994;Ambastha et al, 2016;Li et al, 2021;Wang and Li, 2022). A different CSF detection method proposed by Sievänen et al (2021) uses a bio-impedance needle, which measures the electrical impedance of the tissues as a function of time by applying and sensing an alternating current.…”