Recently, highly stretchable strain sensors have attracted considerable attention. Identifying alternatives to sensitive unit materials and flexible substrates is critical in the fabrication of sensors. Herein, a trinary hybrid carbon material consisting of carbon nanotubes (CNTs), graphene, and fullerene was chosen due to its dense interconnections and robust mechanism. Additionally, the cost-effective fabrication of styrene ethylene butylene styrene (SEBS) provides a platform for the strong adhesion of substrates, which contributes to the strong interaction between the substrates and the sensitive unit materials. Furthermore, the intrinsically high elasticity of SEBS allows the sensors to endure large stretching ranges. Owing to the above-mentioned merits, the fabricated sensor based on CNT/graphene/fullerene-SEBS has a high conductivity of 5.179 S m⁻¹, a moderate gauge factor (GF) of 15, an optimum stretching range of 203%, a linearity of 136% (R² = 0.998), and adaptive-rate repeatability, which reveals its potential in the fields of human motion monitoring and scalable applications.

In medical and surgical scenarios, the trajectory planning of a collaborative robot arm is a difficult problem. The artificial potential field (APF) algorithm is a classic method for robot trajectory planning, which has the characteristics of good real-time performance and low computing consumption. There are many variants of the APF algorithm, among which the most widely used variants is the velocity potential field (VPF) algorithm. However, the traditional VPF algorithm has inherent defects and problems, such as easily falling into local minimum, being unable to reach the target, poor dynamic obstacle avoidance ability, and safety and efficiency problems. Therefore, this work presents the improved velocity potential field (IVPF) algorithm, which considers direction factors, obstacle velocity factor, and tangential velocity. When encountering dynamic obstacles, the IVPF algorithm can avoid obstacles better to ensure the safety of both the human and robot arm. The IVPF algorithm also does not easily fall into a local problem when encountering different obstacles. The experiments informed the RRT* algorithm, VPF algorithm, and IVPF algorithm for comparison. Compared with the informed RRT* and VPF algorithm, the result of experiments indicate that the performances of the IVPF algorithm have significant improvements when dealing with different obstacles. The main aim of this paper is to provide a safe and efficient path planning algorithm for the robot arm in the medical field. The proposed algorithm can ensure the safety of both the human and the robot arm when the medical and surgical robot arm is working, and enables the robot arm to cope with emergencies and perform tasks better. The application of the proposed algorithm could make the collaborative robots work in a flexible and safe condition, which could open up new opportunities for the future development of medical and surgical scenarios.

Flexible pressure sensors usually exhibit high sensitivity, excellent resolution, and can be mass-produced. Herein, a high-resolution, capacitive, paper-based, 3D-printed pressure sensor with a simple, low-cost preparation method is proposed. The sensor has a wide detection range (300–44,000 Pa), a short response time (<50 ms), and high mechanical stability during repeated loading/unloading (3750 Pa). It can measure the weight of an object precisely, from which the shape of the object can be predicted. The sensor can also perform gait detection. The advantages presented by low-cost, high sensitivity, wide detection range, and the ability to be mass-produced make these sensors potential candidates for applications in contact detection and wearable medical devices.