Senior Fall Detection Through Vision and Vibration Techniques with Raspberry Pi.

The number of seniors living alone without caretakers is on the rise. With falls accounting for a significant percentage of injuries and mortality in the elderly, fall detection devices are critical, allowing immediate medical attention through promptly alerting caretakers. We reviewed current fall detection methods and improved their accuracy by implementing new considerations. A "double-checking" system minimizes errors by combining vision and vibration-based detection reports using decision trees. Adjustments to existing vision-based detection methods were made by improving camera positioning and introducing angular velocity. We reviewed various vision and vibration-based fall detection models, analyzed their inconsistency patterns, and optimally consolidated their results. [ABSTRACT FROM AUTHOR]