Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle).

The robotic breast ultrasound scanning (RBUS) system can help sonographer in the early screening of breast cancer. However, it still faces rigorous safety concerns, including non-smooth scanning paths and improper contact force regulation. In this letter, we propose a new path optimization method to improve the scanning smoothness, as well as, a dynamic contact regulation strategy considering both the breast deformation and ultrasound images. For the path planning, the anti-radial scanning approach is first adopted to generate some initial path points together with their normal vectors; then, a dual-objective optimization function is formulated, which considers both the acceleration continuity and deviation of the path, to filter out local path anomalies. For the force control, a contact force-strain regression model is first proposed and used to predict the desired force between the breast and probe at each path point. While within the adjacent path points, an updating algorithm is introduced to dynamically adjust the contact force in real-time by surveying the confidence of the ultrasound image. Our experimental results show that the proposed methods can make the scanning path smoother, and obtain intimate probe-tissue contact with less desired force.

Purpose: The purpose of this study is to present a novel hybrid closed-loop control method together with its performance validation for the dexterous prosthetic hand. Design/methodology/approach: The hybrid closed-loop control is composed of a high-level closed-loop control with the user in the closed loop and a low-level closed-loop control for the direct robot motion control. The authors construct the high-level control loop by using electromyography (EMG)-based human motion intent decoding and electrical stimulation (ES)-based sensory feedback. The human motion intent is decoded by a finite state machine, which can achieve both the patterned motion control and the proportional force control. The sensory feedback is in the form of transcutaneous electrical nerve stimulation (TENS) with spatial-frequency modulation. To suppress the TENS interfering noise, the authors propose biphasic TENS to concentrate the stimulation current and the variable step-size least mean square adaptive filter to cancel the noise. Eight subjects participated in the validation experiments, including pattern selection and egg grasping tasks, to investigate the feasibility of the hybrid closed-loop control in clinical use. Findings: The proposed noise cancellation method largely reduces the ES noise artifacts in the EMG electrodes by 18.5 dB on average. Compared with the open-loop control, the proposed hybrid closed-loop control method significantly improves both the pattern selection efficiency and the egg grasping success rate, both in blind operating scenarios (improved by 1.86 s, p < 0.001, and 63.7 per cent, p < 0.001) or in common operating scenarios (improved by 0.49 s, p = 0.008, and 41.3 per cent, p < 0.001). Practical implications: The proposed hybrid closed-loop control method can be implemented on a prosthetic hand to improve the operation efficiency and accuracy for fragile objects such as eggs. Originality/value: The primary contribution is the proposal of the hybrid closed-loop control, the spatial-frequency modulation method for the sensory feedback and the noise cancellation method for the integrating of the myoelectric control and the ES-based sensory feedback.

Robotic ultrasound-guided system improves surgical flexibility and accuracy. Ultrasound (US) calibration is the key to obtain spatial relationship and localization of US images. However, existing calibration methods mainly rely on an external tracker, which leads to complicated workflow and prone to error accumulation. In this paper, we proposed a robot-assisted automatic US calibration method. This method can automatically complete the US calibration without any external tracker, thus improving the calibration accuracy and simplifying the workflow. In addition, we propose an automatic image segmentation method incorporating prior knowledge. It automatically segments the features of the calibration model in the US image during the US scanning process, and ensures the smooth execution of the automatic robot calibration process. Specifically, we transformed the calibration problem into a point-to-point registration problem and conducted experiments using three different registration algorithms. The results show that the calibration accuracy of our method is 0.65±0.20 mm. Our method significantly reduces calibration time compared to manual calibration. We also compare the proposed method with state-of-the-art methods, and the results demonstrated that the proposed method performs better.

Experienced sonographers can adjust their arm impedance in real-time to obtain high-quality ultrasound (US) images during US scanning. These operational skills can be captured through robot systems with multimodal data collection capabilities (position, force, and impedance). However, low operational transparency between the system (generally, a serial robot with admittance control) and its users will result in significant delays and errors, interfering with the skill acquisition process. The paper proposes a new system that adopts the parallel mechanism (Omega.7) to improve the transparency of the operation. The scanning probe and a 6-axis force sensor are attached to the end of Omega.7. When operating the probe, a zero-force drag effect can be realized through gravity and torque compensations. The arm impedance during the scanning can be measured through the force disturbance method by analyzing external forces on the device. Ultrasonic scans were conducted on phantoms of different hardness, and arm impedance was measured. Statistical analysis reveals that when scanning softer phantoms, arms exhibit higher stiffness. The transparency analysis results show that the equipment designed in this paper has a higher level of transparency than the scheme of serial robot with admittance control. The high operation transparency of the system makes it an ideal skill-acquisition device with broad applications.