Lumisafe utilizes advanced machine-learning models to ensure highly accurate fall detection. Accuracy is paramount in a fall detection system, as it directly impacts the safety and well-being of individuals. Our devices come equipped with multiple built-in AI models that have been extensively pre-trained. These models are designed to analyze the data captured by the Time-of-Flight depth sensor and determine if a fall event has occurred.

Specifically, Lumisafe employs at least two key machine-learning models. One model is dedicated to pose estimation, which helps the system understand the position and movement of a person within the monitored space. The other model is specifically for fall detection, analyzing the pose data and movement patterns to identify the distinct characteristics of a fall. By combining these models, Lumisafe can differentiate between normal activities and actual fall incidents, significantly reducing false alarms.

A crucial aspect of Lumisafe's accuracy is its ability to adapt to specific environments. While the devices come with powerful pre-trained models, they are also designed to improve over time. The machine-learning models can continue to train locally using data from the personalized environment. This local training allows the system to fine-tune its detection capabilities based on the unique layout and typical activities of the installation area, further enhancing accuracy and reliability.

The entire process, from data capture by the depth sensor to analysis and model training, happens 100% locally on the Lumisafe device. This local processing is not only essential for privacy but also contributes to the system's responsiveness and accuracy. By processing data on the edge, the system can react quickly to potential fall events. The combination of pre-trained, sophisticated models and the capability for personalized local training makes Lumisafe a highly accurate and dependable solution for fall detection in various settings, including public restrooms, bathrooms, and healthcare environments.