HUM Systems introduces help call detection and relies on ChatGTP integration

The all-in-one Livy sensor station already offers a number of functions for safety and comfort in the home. With the new features, the solution is now also suitable for care facilities and can be used in outpatient care and senior apartments in the future. Together with the Livy Care digital assistance system, the Livy Sensor Station forms a complete safety solution for the care environment, enabling caregivers to provide more efficient care to their clients. The new functions are based on Artificial Intelligence as well as sensor-based technologies, thus offering maximum precision and safety.

"We want to increase the safety and comfort of people in need of care and set new standards in care quality with our solution," says founder and CEO Ali Reza Humanfar. "The help call and fall detections are the optimal additions to Livy's already implemented "leave bed and room" features. They are an important support for increasing safety, especially for care facilities that look after dementia patients with tendencies to wander off, and relieve the burden on staff," Humanfar continues. "We also see the great potential of ChatGPT in this regard. By incorporating artificial intelligence, call for help recognition is not limited to individual signal words, but can recognize complete sentences or other words as triggers as well as the speaker's intention behind them. Currently, we are therefore working intensively on extending our help call recognition with ChatGTP. Initial tests are yielding impressive results."

Livy Care's new call for help recognition enables people to verbally call for help in medical emergencies, regardless of triggers such as emergency buttons or emergency bracelets. This makes it easier to identify hazards more quickly, especially during periods of particular stress or night duty, and to alert those affected. 
The new fall detection system automatically detects falls and immediately informs nursing staff or a central monitoring point. The company deliberately relies on a combination of radar- and camera-based technology to ensure maximum accuracy of detection during the day and at night and thus maximum safety for those in need of care. The images processed during fall detection are anonymized to protect the privacy of all involved and thus comply with the high data protection requirements in care facilities.