A novel method of using sound waves and artificial intelligence for the detection of vehicle's proximity from cyclists and E-scooters.

Outdoor air pollution has been found to have a significant adverse effect on health. When the authors attempted to monitor air quality that cyclists or e-scooter users' breath during commuting in different locations for health and safety analysis, it was found that the existence of internal combustion engine (ICE) cars has a significant effect on the pollution levels and the monitoring process. To comprehensively study the effect of cars and traffic on air quality that cyclists and e-scooters users experience, a low-cost and reliable system was needed to detect the proximity of cars that have diesel or petrol engines. Video cameras can be used to visually detect vehicles, but in the modern age with the existence of many electric and hybrid vehicles and the need to reduce the cost of instrumentation, there was a need to determine the passing of vehicles near e-scooter and bike users from the combined engine and tires sounds. To address this issue, this study suggests a novel approach of using sound waves of internal combustion engines and tire sounds during the passing of cars, combined with AI techniques (neural networks), to detect the proximity of cars from cyclists and e-scooter users. Audio-visual data was collected using Go-Pro cameras in order to combine the data with GPS location and pollution levels. Geographical data maps were produced to demonstrate the density of cars that cyclists encounter when on or near the road. This method will enable air quality monitoring research to detect the existence of ICE cars for future correlation with measured pollution levels. The proposed method allows for:•The automated selection of sensitive features from sound waves to detect vehicles.•Low-cost hardware which is independent of orientation that can be integrated with other air quality and GPS sensors.•The successful application of sensor fusion and neural networks.

A device for improving the visual clarity and dimension of veins.

Vascular access for venepuncture and peripheral intravenous cannulation is a common procedure within health care. First-attempt cannula insertion success rate has been found to be lower in some patient groups. Multiple unsuccessful cannulation attempts have negative impacts for both patients and practitioners. This article reports on research investigating the effectiveness of an innovative device called the Vacuderm-a single-use tourniquet with added manual vacuum pump-in increasing vein dimensions, temperature difference between vein and its surrounding and visual clarity through an additional effect of creating a vacuum on top of the tourniquet. A randomised crossover design was used in this study for looking at the vein visibility, dimensions and thermal behaviour using infrared thermography. Dorsal areas of both hands were assessed in a random crossover study of 20 healthy volunteers with and without the application of the Vacuderm. The results show significant increase in venous diameter and venous cross-sectional area with highly significant increase in vein clarity caused by using the Vacuderm, which creates a negative pressure for transient suction in addition to its vein occlusion effect.