When a biker doesn't wear a helmet and gets into an accident at high speed, the impact may be deadly. Protecting one's brain from potentially fatal blows is as simple as donning a helmet. Malaysia is one of the nations that has passed legislation mandating motorcyclists wear protective headgear. Riding a motorbike is now safer than ever before thanks to a novel concept: a smart helmet. This is put into action by use of GSM and GPS systems. This intelligent helmet's operation is straightforward: vibration sensors, linked to a microcontroller board, are positioned in various parts of the helmet depending on the likelihood of impact.As a result, the sensors detect when the rider's helmet hits the ground and send that information to the microcontrollerboard. From there, the controller pulls GPS data from the connected GPS module. A message is automatically sent to the ambulance or family members via the GSM module when the data exceeds the minimal stress level. Also include

Finding the most cost-effective configuration of a two-stage gearbox's primary design characteristics, including the second stage's double gear sets, is the goal of this article. The optimisation process takes ten parameters into account, including the total gearbox ratio, the width coefficient of the first gear stage, the width coefficient of the second gear stage, the allowable contact stress of stage 1, the allowable contact stress of stage 2, the output torque, the cost of the gearbox housing, the cost of helical gears, the cost of straight gears, and the cost of shafts. Furthermore, in order to discover the best answer, a simulation experiment is run. It turns out that the responsiveness is primarily affected by the overall gear ratio. The suggested approach has shown its dependability and is suitable for use in future research

With the recent advancements in computer vision, Human Action Recognition has become one of the aesthetic research fields. There has been little research work in the real world conditions of human action recognition systems, which encourages us to seriously search in this application domain. Although lot of healthy approaches have been introduced in the literature, they are still insufficient to fully cover the challenges. This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity recognition and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods.