The broader impact/commercial potential of this I-Corps project is the development of a system that detects falls, predicts a fall before it occurs, and monitors a person’s age-related mental and physical status such as cognitive decline. In assisted living facilities, the majority of residents have age-related disabilities with a concomitant increased risk of falls. Fall prevention and detection are significant and urgent problems. The proposed technology will monitor a person’s movements in their living space. In the event of a fall, the system will automatically recognize the change in movement and position and inform the caretakers to ensure a timely response. The next-generation system will use the analysis from continuous monitoring of the patient’s daily activities to catch behavioral and gait changes that have a correlation with an increased likelihood of a fall, giving caregivers a chance to take necessary corrective action to help prevent a problem. Detecting falls quickly will provide better health outcomes to patients.
This I-Corps project is based on the development of a system that uses sensors and artificial intelligence (AI) to monitor movement patterns and identify age-related decline without the use of privacy-invading cameras. The proposed technology uses sensors and movement patterns to send alerts for increased patient safety and protection in all settings. The advantage is the ability to track people and identify postures with a very low-resolution (8x8 pixels) off-the-shelf, Time-of-Flight sensor, enabling a low cost, low energy solution. Deployed on ceilings, the sensor measures distances of reflecting body parts that are analyzed by unique machine learning models. The sensor can identify the subjects’ poses, movement vectors, and potential for a fall. A full system will contain multiple sensors, installed in strategic locations. The primary mode of the system is to give an alert to caregivers when a fall occurs. The next version will include a real-time AI engine that will analyze subjects’ movement patterns and will predict gait changes, serving as a predictive tool to prevent falls before they happen.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
