• Vanshkumar Kanjani
• Harsh Ramwani
• Dhruv Karelia
• Zalak Trivedi
• Hirak Modi

Wearable technologies can significantly impact people's daily lives by providing innovative ways to experience the world through augmented reality (AR) applications. From accessories to integrated clothing and even body-integrated electronics, these devices offer many benefits. However, their widespread use raises significant data protection issues and complex data security issues. This paper provides an in-depth survey of recent applications in the analysis of large data sets collected by wearable sensors for privacy preservation. It has a strong focus on security and privacy when it comes to wearables. The study also explores the possibility of combining deep learning algorithms for wearable sensors with encryption. It includes a case study that evaluates the performance of a privacy-preserving deep learning framework both theoretically and empirically. The paper investigates the implementation of a secure prediction service using a convolutional neural network (CNN) model and the Cheon-Kim-Kim-Song (CHKS) homomorphic encryption algorithm. Recent advances in computing, communication and artificial intelligence technologies, along with the widespread availability of smartphones and the proliferation of multimedia data and edge computing devices, have led to new designs and frameworks for mobile devices. This article explores and classifies smart clothing and experimental designs that use machine learning and artificial intelligence techniques. The main goal is to explore these emerging paradigms for the application of machine learning and artificial intelligence in wearable technology, covering several technological aspects, including (1) smart wearables enhanced by machine learning and artificial intelligence; (2) data collection architectures and models for smart wearables powered by artificial intelligence; and (3) versatile applications of smart wearables controlled by AI. Augmented reality (AR) offers the opportunity to fundamentally change the way people interact in a dynamic urban environment. However, the design and evaluation of these new urban AR experiences present several challenges, including technical limitations and safety issues associated with using AR outdoors. In this regard, our contribution is to explore the use of virtual reality (VR) to simulate portable urban AR experiences, providing people with a realistic, safe and controlled environment to interact with potential AR interfaces. This paper presents two urban AR applications for mobile devices (pedestrian navigation and autonomous mobility) simulated using VR technology. In recent years, there have been significant advances in the field of mobile devices, and the integration of Augmented Reality (AR) and Virtual Reality (VR) technologies has played a key role in transforming the industry. Machine learning (ML) has emerged as a powerful tool to improve the features and functionality of AR and VR wearables, leading to new applications that improve user experience, personalization and overall product efficiency. This brief provides an overview of the intersection of ML, AR and VR in the field of wearables. Wearable AR and VR have gained popularity in various industries, including gaming, healthcare, education and retail, providing an immersive digital experience by superimposing virtual elements over the real world (AR) or completely immersing users in a virtual environment (VR).

Prototyping, Augmented Reality, Virtual Reality, Urban Applications, Artificial Intelligence (AI), Smart Wearables, Deep Learning, Wearable Technology, Privacy-Preserving, Big Data.

"Machine Learning Application for Augmented Reality and Virtual Reality in Wearable Goods", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 10, page no.e470-e484, October-2023, Available :http://www.jetir.org/papers/JETIR2310372.pdf

"Machine Learning Application for Augmented Reality and Virtual Reality in Wearable Goods", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 10, page no. ppe470-e484, October-2023, Available at : http://www.jetir.org/papers/JETIR2310372.pdf