Dispositivo IoT para prevenir la violencia de género usando TinyML
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Abstract
The study is framed within the development of a solution based on the Internet of Things (IoT) and machine learning to prevent and detect dangerous situations related to Gender Based Violence (GBV). The goal is to provide a useful and accessible tool for women at risk, thus contributing to the prevention and reduction of GBV.
The problem addressed by the study is gender-based violence, an issue of great social and humanitarian relevance. It seeks to use digital technologies and machine learning to detect words associated with dangerous situations and prevent GBV in real time.
To address the problem, a public data set created by Microsoft containing audio samples of different words, including words associated with dangerous situations such as "yes" and "no", as well as other words and static noise, is used.
Audio data in WAV format is used, divided into one-second windows with a sampling rate of 16000 Hz. A homogeneous data window with a duration of one second is selected and the frequency cepstral coefficient (MFCC) is used to highlight the human voice and reduce background noise.
The developed model showed good overall performance, with an average efficiency of 91.3% in the training set and 85.83% in the evaluation set. High accuracy was obtained in the detection of words associated with danger situations, such as "yes" and "no". It is recognized that technology has a significant role to play in addressing GBV, but it also emphasizes the need for a commitment from society and governments to achieve lasting and significant change in eradicating this problem worldwide
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