Advancements in biophotonics have driven the development of miniaturized imaging probes for high-resolution in vivo imaging. Probe-based confocal laser endomicroscopy (pCLE) enables cellular-level visualization of tissues but remains challenging for retinal imaging due to the need for non-contact operation, tremor compensation, and precise focal control. This study introduces a novel handheld confocal endomi-croscope system that integrates a custom-built imaging probe, an optical coherence tomography (OCT) distance sensor, and motor-assisted tremor suppression to improve imaging stability and resolution. The system employs a fiber-based common-path swept-source OCT (CPSS-OCT) sensor to maintain a stable focal distance while compensating for involuntary hand tremors using motorized stabilization. A gated recurrent unit (GRU)-based tremor prediction algorithm further enhances image stability. The imaging probe features a PZT tube-driven fiber cantilever resonance for Lissajous scanning, providing a wide field of view with minimal image distortion. In experiments using bovine eye samples, the CR score improved from 0.318 to 0.472, with a 48.43% increase in the in-focus condition when tremor compensation was activated, confirming enhanced image clarity and stability. Experimental results demonstrate that the system effectively stabilizes imaging, reduces motion artifacts, and ensures high-resolution, non-contact retinal imaging. By addressing the limitations of conventional pCLE devices, this system represents a significant advancement in ophthalmic imaging and can potentially improve retinal diagnostics and precision-guided interventions.

This paper proposes a novel form of a three-dimensional coaxial bowtie-shaped mechanical amplifier. The proposed model incorporates a lever mechanism into the Sarrus linkage structure. It allows the target plate to move along one axis with amplified displacement in a parallel manner. The amplifier was assembled after machining the components using a computer numerical control machine. A flexible hinge was incorporated into the amplifier design for simplified fabrication and reduced friction in the actuation mechanism. Castigliano’s theorem is used to build a mathematical model of the proposed mechanical amplifier, and the performance was validated through finite element analysis and prototype fabrication. We achieved the amplification ratio of ×8.44, resulting in the axial displacement up to 86 µm. The demonstrated amplifier is expected to apply to compact microsurgical robots or biomedical imaging apparatus requiring coaxial displacement amplification in confined spaces.

In minimally invasive pain management procedures, precise needle positioning is paramount for effective treatment and patient safety. Traditional techniques like the loss-of-resistance (LOR) method may be insufficient, especially in patients with narrowed epidural spaces. The use of imaging tools such as C-arms carries risks due to radiation exposure for medical professionals. A new system for detecting the epidural space based on optical interferometry is proposed to tackle this issue. Prior research has focused on force measurement systems to identify tissue puncture or rupture. Although mechanical sensors have been utilized, they add bulk and complexity to systems. Optical sensors like Fiber Bragg grating (FBG) and Fabry-Pérot interferometer (FPI) offer stable, high-resolution measurements suitable for complex biological tissues. This study aims to develop a sensor and needle system for epidural injections, incorporating quantitative metrics for validation. An optical interferometer-based force measurement sensor was integrated into a commercial epidural needle, and calibration was performed to establish a correlation between system output and actual force. The system employs a graphical user interface (GUI) to identify puncture points based on abrupt force decreases. A user study involving interventionalists assessed the system’s performance by measuring invasive depth and success rates. The user study demonstrated that the proposed sensorized system could detect the puncture with an average success rate of 72.63 %. This study represents a significant advancement toward safer and more precise epidural procedures, addressing critical clinical considerations for practical applications.

Diseases affecting the retina, such as retinal detachment, diabetic retinopathy, and macular degeneration, are significant contributors to blindness globally, with a substantial risk of vision loss among those afflicted. Surgical treatment of these conditions is complex due to the delicate nature of retinal tissue and the challenges posed by involuntary hand movements. While existing methods aim to compensate for hand tremors using sensor-based systems, they are hindered by limitations in accurately tracking retinal surface movement during surgery, particularly in response to patient movements under anesthesia. To address these issues, this study proposes a novel handheld micro-surgical tool equipped with a 1-degree of freedom (DOF) mechanism and a 3-axis Hall sensor to mitigate physiological hand tremors effectively. By utilizing magnetic flux density measurements, the tool can pinpoint the position of a magnet embedded within the surgical instrument, enabling precise tremor compensation without reliance on a global coordinate system. The design incorporates a piezoelectric (PZT) linear actuator and a Hall sensor for compactness and sensitivity. Optimization of the magnet’s dimensions through simulation ensures optimal sensor performance. Experimental validation using artificial and ex-vivo porcine eye models demonstrates the tool’s effectiveness in reducing hand tremors, suggesting potential enhancements in the safety and accuracy of retinal surgeries. For the desired positions from 4000 µm to 1000 µm, the RMS error of the synthetic eye model and porcine eye decreased from 71.10 µm to 33.27 µm and 71.36 µm to 33.39 µm, respectively.

Optical Coherence Elastography (OCE) is a method that discerns local tissue stiffness using optical information. This method has recently been explored for laryngeal cancer tumor margin detection but has not been widely deployed clinically. Part of the challenge hindering such clinical deployment is the need for controlled high-precision mechanical probing of the tissue. This paper explores the concept of robot-assisted optical coherence elastography(OCE) and presents a preliminary system integration used to demonstrate the approach for stiffness mapping and discerning tumor margins. The approach is demonstrated on a custom Cartesian stage robot, and a custom-built OCE system comprised of an 830 nm broad-band laser with a vector-analysis method for phase gradient estimation and strain imaging. The paper illustrates one of the advantages of robot-controlled probing in terms of increasing the accuracy of the OCE system in a large range of displacement and strain. By leveraging motion information from the robot, online re-calibration of the OCE strain map may be achieved, thereby reducing OCE errors. After calibration, it is shown that the error in estimating the local Young's modulus is 0.485% in the silicon phantom and 0.531% in the agar phantom. These results suggest that future integration of optical coherence tomography(OCT) in clinically deployable robots may offer advantages in enabling local stiffness map estimation using OCE.

Human-robot interaction (HRI) demands efficient time performance along the tasks. However, some interaction approaches may extend the time to complete such tasks. Thus, the time performance in HRI must be enhanced. This work presents an effective way to enhance the time performance in HRI tasks with a mixed reality (MR) method based on a gaze-speech system. In this paper, we design an MR world for pick-and-place tasks. The hardware system includes an MR headset, the Baxter robot, a table, and six cubes. In addition, the holographic MR scenario offers two modes of interaction: gesture mode (GM) and gaze-speech mode (GSM). The input actions during the GM and GSM methods are based on the pinch gesture and gaze with speech commands, respectively. The proposed GSM approach can improve the time performance in pick-and-place scenarios. The GSM system is 21.33 % faster than the traditional system, GM. Also, we evaluated the target- to-target time performance against a reference based on Fitts' law. Our findings show a promising method for time reduction in HRI tasks through MR environments.

Most virtual reality (VR) applications use a commercial controller for interaction. However, a typical virtual reality controller (VRC) lacks positional precision and accu-racy in millimeter-scale scenarios. This lack of precision and accuracy is caused by built-in sensors drift. Therefore, the tracking performance of a VRC needs to be enhanced for millimeter-scale scenarios. Herein, we introduce a novel way of enhancing the tracking performance of a commercial VRC in a millimeter-scale environment using a deep learning (DL) al-gorithm. Specifically, we use a long short-term memory (LSTM) model trained with data collected from a linear motor, an IMU sensor, and a VRC. We integrate the virtual environment developed in Unity software with the LSTM model running in Python. We designed three experimental conditions: the VRC, Kalman filter (KF), and LSTM modes. Furthermore, we evaluate tracking performances in the three conditions and two other experimental scenarios, namely stationary and dynamic. In the stationary experimental scenario, the system is left motionless for 10 s. By contrast, in the dynamic experimental scenarios, the linear stage moves the system by 12 mm along the X, Y, and Z axes. The experimental results indicate that the deep learning model outperforms the standard controllers positional performance by 85.69 % and 92.14 % in static and dynamic situations, respectively.