Velocity-Based Detection of Unusual Movements for Enhanced Home Security

Caring for individuals diagnosed with Alzheimer's disease presents unique challenges, particularly in ensuring their safety at home. Memory impairments, a common symptom of Alzheimer's disease, can lead to hazards such as unintentionally leaving principal doors and windows open, increasing vulnerability to intrusions. This paper proposes a novel intrusion detection system designed for smart homes, primarily utilizing motion sensors to protect patient privacy. The system analyzes movement velocities between strategically placed sensor pairs to monitor patient movements and identify unusual activities indicative of potential intrusions. We validate the system using the Technology Integrated Health Management (TIHM) dataset, providing insights into the movement patterns of Alzheimer's patients within a smart home setting. By comparing movement velocities among patients, we identify variations in their patterns. Statistical analysis confirms the effectiveness of our approach in successfully identifying intrusions, thus enhancing the safety of Alzheimer's patients at home.