Wearable robotic devices are receiving rapidly growing attentions for human-centered scenarios from medical, rehabilitation, to industrial applications. Supernumerary robotic limbs have been widely investigated for the augmentation of human limb functions, both as fingers and manipulator arms. Soft robotics offers an alternative approach to conventional motor-driven robot limbs towards safer and lighter systems, while pioneering soft supernumerary limbs are strongly limited in payload and dexterity by the soft robotic design approach, as well as the fabrication techniques. In this work, we proposed a wearable supernumerary soft robot for the human forearm, inspired by the fore flippers of otariids (eared seals). A flat flipper design was adopted, differing from the finger or arm shaped state-of-the-art works, with multiple soft actuators embedded as different joints for manipulation dexterity. The soft actuators were designed following origami (the art of paper folding) patterns, reinforced by kirigami (the art of paper cutting) fabrics. With this new approach, the proposed soft flipper incorporated 8 independent muscles, achieving over 20 times payload to self-weight ratio, while weighing less than 500g. The versatility, dexterity, and payload capability were experimentally demonstrated using a fabricated prototype with proprietary actuation and control. This work demonstrates the feasibility and unique advantages for origami + kirigami soft robots as a new approach to strong, dexterous, and yet safe and lightweight wearable robotic devices.

The quality of breast ultrasound imaging is greatly affected by the contact force of the probe, which largely requires experienced sonographers to complete the clinical examination. We propose a flexible and fully autonomous ultrasound scanning system for breast ultrasound imaging. It consists of an ultrasound machine, a dual robotic arms system, a multi-structured light system, a human–computer interaction system, and a flexible ultrasound probe clamping device (FUPCD). First, the dynamics model of the FUPCD was analyzed, and a closed-loop force control strategy was established. We then implemented an automatic scanning system. The hysteresis characteristics of the FUPCD and transient response of the force controller were experimentally verified. The system could keep the steady-state error less than $\pm$ 5 $\%$ within 0.5 s. Second, the performance of the control system to maintain constant contact force at different scanning speeds ( $<$ 20 mm/s) was evaluated. In a series of comparative studies, the fluctuation range of the target contact force applied by the flexible automatic scanning was 10 $\%$ . The force control capability of the system was 30.84 times higher than that of traditional manual scanning, 18.48 times higher than that of flexible automatic scanning (FC off), and 24.3 times higher than that of rigid automatic scanning (FC on). Furthermore, the effectiveness of the FUPCD arc design and flexibility was verified. In addition, the change trend of the average grayscale level (GSL) of the ultrasound image was basically the same as the contact force, demonstrating that the stability and repeatability of the ultrasound image can be improved by controlling the stability of the contact force. In conclusion, the proposed system can simplify the workflow of breast ultrasound examination and improve the diagnostic capabilities of the ultrasound imaging system. Note to Practitioners—The motivation of this study is to solve the problem of poor image reproducibility in breast ultrasound scanning, but the proposed system can also be applied to ultrasound scanning of other parts of the body. The position, direction, and contact force of the ultrasound probe affects the image quality and repeatability of the ultrasound, thereby affecting the diagnostic ability. Therefore, we propose and develop a flexible and fully autonomous breast ultrasound scanning system. We aimed to improve the autonomy and stability of the scanning process by designing end-to-end automated scanning strategies, flexible clamping devices, and closed-loop force control strategies. Among them, the fully automatic three-dimensional perception and trajectory planning can provide global fitting capabilities to different forms of breast surfaces and control the probe to maintain the best contact posture. At the same time, the fully automatic workflow reduces additional interference and reduces the complexity of the workflow. The flexible ultrasound probe clamping device improves the passive applicability to flexible tissues and reduces the resistance in the scanning process. The closed-loop force control strategy can adjust the contact force in real time. Experiments verified that the repeatability could be evaluated by contact force. A series of human–machine comparison experiments verified the advancement and effectiveness of the system. In future research, we will further solve the problem of abnormalities and repeatability of ultrasound images acquired during the scanning process by combining the multi-modal feedback of images and forces.

Supervised deep learning depends on massive accurately annotated examples, which is usually impractical in many real-world scenarios. A typical alternative is learning from multiple noisy annotators. Numerous earlier works assume that all labels are noisy, while it is usually the case that a few trusted samples with clean labels are available. This raises the following important question: how can we effectively use a small amount of trusted data to facilitate robust classifier learning from multiple annotators This paper proposes a data-efficient approach, called Trustable Co-label Learning (TCL), to learn deep classifiers from multiple noisy annotators when a small set of trusted data is available. This approach follows the coupled-view learning manner, which jointly learns the data classifier and the label aggregator. It effectively uses trusted data as a guide to generate trustable soft labels (termed co-labels). A co-label learning can then be performed by alternately reannotating the pseudo labels and refining the classifiers. In addition, we further improve TCL for a special complete data case, where each instance is labeled by all annotators and the label aggregator is represented by multilayer neural networks to enhance model capacity. Extensive experiments on synthetic and real datasets clearly demonstrate the effectiveness and robustness of the proposed approach. The code will be available at https://github.com/ShikunLi/TCL.

The fusion of robotics and ultrasound scanning has been used to overcome the shortcomings of traditional ultrasound examinations, such as high variability and low repeatability of ultrasound images acquired by operators. However, there are huge differences in the surface morphology of different human bodies, and the current clinical practice still relies heavily on the manually generating a suitable scanning trajectory for the freeform surface. In this paper, a practical strategy for automatic generation of ultrasound scanning trajectory on breast surfaces is proposed, which can automatically and efficiently generate scanning trajectory for any free-form breast surface. First, the probe attitude and contact model of ultrasound automatic scanning are analyzed. Second, an end-to-end automatic trajectory generation workflow based on 3D point clouds is proposed. Furthermore, a trajectory point offset strategy considering skin deformation and a simultaneous double breast scanning strategy are proposed. In addition, a probe attitude normal admittance controller based on position inner loop control is designed. The experimental results show that the overall time of trajectory generation is less than 4 s. The automatic scanning is 30.84 times more stable in controlling the contact force than the manual scanning, which effectively improves the repeatability of the scanning.

Two-dimensional lung ultrasound (LUS) has widely emerged as a rapid and noninvasive imaging tool for the detection and diagnosis of coronavirus disease 2019 (COVID-19). However, image differences will be magnified due to changes in ultrasound (US) imaging experience, such as US probe attitude control and force control, which will directly affect the diagnosis results. In addition, the risk of virus transmission between sonographer and patients is increased due to frequent physical contact. In this study, a fully automatic dual-probe US scanning robot for the acquisition of LUS images is proposed and developed. Furthermore, the trajectory was optimized based on the velocity look-ahead strategy, the stability of contact force of the system and the scanning efficiency were improved by 24.13% and 29.46%, respectively. Also, the control ability of the contact force of robotic automatic scanning was 34.14 times higher than that of traditional manual scanning, which significantly improves the smoothness of scanning. Importantly, there was no significant difference in image quality obtained by robotic automatic scanning and manual scanning. Furthermore, the scanning time for a single person is less than 4 min, which greatly improves the efficiency of screening triage of group COVID-19 diagnosis and suspected patients and reduces the risk of virus exposure and spread.

Ultrasound image guidance could display the movement of soft tissue in real time, which provides an important basis for the operation path selection of tumor for examinations and interventions such as precise localization and puncture biopsy. However, factors such as tissue deformation, patient motion, and instrument contact pose great challenges for real-time smooth localization tracking of target tissues and maintaining a suitable acoustic window. Not only does it reduce the accuracy of ultrasound examination, but also may prolong the examination time. In this paper, a real-time autonomous ultrasound robot trajectory planning framework is proposed to achieve global scanning of breast tissue and real-time local tracking of suspicious tumor targets. In addition, an ultrasound suspicious tumor target next moment motion attitude and position estimation algorithm and an acceleration-continuous online trajectory generation (ACOTG) algorithm are proposed. Experiments on breast phantom showed that the fluctuation difference of both global scanning and tracking scanning force was less than 5 N $\pm$ 15%, and ACOTG takes less than 1 microsecond to calculate the next moment state. The position, velocity, and acceleration of the tracking path did not change abruptly when the unknown target changed. The ultrasound image of the suspected tumor target could always be smoothly maintained in a suitable acoustic window, while the errors between the center of the suspected tumor target image and the center of the acoustic window in the horizontal direction and the depth direction were less than $\pm$ 1 mm and 30 mm $\pm$ 10%, respectively. Note to Practitioners —The motivation of this study was to solve the clinical problem of difficulty in keeping the suspected breast tumor target stably in the center of the field of view under the influence of tissue deformation, patient motion and instrument contact in ultrasound guidance. We proposed the idea of global conventional scanning and local online tracking to achieve the autonomous scanning, detecting and tracking of suspicious tumor targets within the global scope of the breast. ACOTG algorithm is proposed and implemented, which can make the ultrasound image of suspicious tumor target can be displayed stably in the center of the acoustic window. With the help of this system, the accuracy of ultrasound examination can be improved and the time of examination can be reduced. The proposed system can also be applied to ultrasound scanning of other parts of the body.