Michał Rosiak

Composite materials, particularly polymers reinforced with carbon, glass, and aramid fibers, enable the development of lightweight yet mechanically robust structures that enhance user comfort and functional performance. Their high strength-to-weight ratio and fatigue resistance make them ideal for applications requiring repetitive movements in rehabilitation and assistive robotics. However, challenges remain related to cost-effective production, durability under complex loading conditions, and ergonomic fit to human anatomy. Recent advances in materials science and smart materials are expanding the possibilities of multifunctional composites with embedded sensors. Furthermore, machine learning methods are increasingly being used to optimize material selection and structural design. Future advances are expected to improve scalability, personalization, and system integration, positioning composite fibers as a key assistive technology in next-generation robotic systems.

This paper describes the design, operation and test results of a simple microprocessor-based device for measuring slow liquid flows. The device uses a module of 30 digital optical sensors to track the movement of a single air bubble inserted into a tube of flowing liquid. During a measurement session, the air bubble remains within the sensor module at all times, allowing the instrument to take measurements for any length of time. The liquid whose flow rate is being measured moves only in the closed tube system, without contact with other components of the device. The test of the device itself was carried out using a tube with an inner diameter of less than 1 mm, where the device is capable of measuring flow rates on the order of microliters per minute. Tests of the device showed good agreement between the measured volumetric flow rate and the reference flow rates of the infusion pump over the entire measurement range. The advantages and limitations of the device are discussed, as well as the prospects for developing the method.