Irene Lu¹ and Richard Guo², ¹USA, ²California State Polytechnic University, USA

This paper addresses the pressing issue of female safety through the design of a wearable device capable of detecting abnormal movements and recognizing voice commands [1]. Our approach integrates KNN-based motion detection and real-time speech recognition, offering a more reliable and accurate system for emergency detection [2]. Unlike previous methods that rely on computationally intensive models, such as ANNs and SVMs, our solution is optimized for low-power devices, ensuring real-time response without compromising efficiency [3]. The system was tested on various activities and in noisy environments, demonstrating its ability to detect emergency situations reliably. The results highlighted the importance of addressing edge cases, such as slower movements in elderly users and fast actions in athletes, which we plan to refine in future iterations. The combination of motion detection and voice recognition provides a flexible and dynamic safety mechanism, enhancing personal security [4]. Our proposed solution offers a significant step toward affordable and accessible safety technology, with the potential to empower women globally by improving their security in everyday situations.

KNN Classifier, Voice Recognition, Datasets, Real-Time