The aging population has led to an increasing number of elderly individuals living alone, making it crucial to address their need for prompt and effective emergency assistance. Older adults, often facing physical limitations or illnesses, require reliable systems for immediate help during life-threatening situations. To meet this need, smart devices like emergency call systems are being developed, enhancing seniors’ safety and improving health and social care responses. Our research explores how passive and active speech analysis on mobile devices can support automatic emergency assistance. We show that this can be achieved on Edge devices using tiny machine learning (ML) models for wake-word detection, speech-to-text conversion, and intention recognition, paving the way for safer, smarter living environments for seniors.