Research on emotion management for elderly based on speech signal analysis technology

This paper presents a study on emotion management for solitary elderly individuals using voice signal analysis technology. The research encompasses the collection of training samples, voice analysis employing specific algorithms, and the statistical output of results, all seamlessly integrated with a router. The outcomes are transmitted to the cloud, providing accessible data for family members, community hospitals, and relevant stakeholders. The key focus lies in the extraction of vocal features, specifically resonance peaks and Mel-Frequency Cepstral Coefficients (MFCC), for effective emotion recognition. The classification of emotional states is achieved through the application of the k-Nearest Neighbors (KNN) algorithm. This comprehensive system not only ensures efficient emotion analysis but also facilitates continuous monitoring, offering valuable insights into emotional trends over time. The integration of cloud storage and real-time accessibility by caregivers and healthcare institutions demonstrates the practical utility of the proposed approach in enhancing the emotional well-being of elderly individuals living alone.