Application

Automated medical monitoring device for older adults that can alert caregivers, medical personnel, and family of adverse medical events.

Key Benefits

• Low-cost and easy-to-use medical alert system for falls and adverse health events.
• The technology does not require the individual to carry a device.
• Provides voice-assisted interface, self-correction, behavioral prediction, safety monitoring, visitor recognition, and data collection capabilities.
• Preliminary data using a prototype show the system can analyze sleep to determine the severity of illnesses like obstructive sleep apnea.
• Global revenues of medical alert systems are more than $7 billion annually.

Market Summary

The number of Americans ages 65 and older is projected to increase significantly in coming years, predicted to eventually exceed 23% of the overall population. In addition, the increasing prevalence of mental illness among the population requires effective monitoring of individuals for safety in acute health events. Commercial products like wearable devices and mobile phone application apps can be unreliable as many do not always have access to a device or phone during an event. Also, these products can be cost-prohibitive, with limited access to low-income adults.

Technical Summary

Emory researchers are developing a non-invasive, affordable, and easy-to-use system for the elderly to monitor safety and identify early signs of illness. The system comprises a central monitoring unit, a passive infrared sensor (PIR) sensor, and a Raspberry Pi. The PIR sensor detects coarse movements associated with various health conditions and falls, while the Raspberry Pi collects data from the PIR sensor and sends it to the central unit. The main unit then communicates relevant events to family, emergency responders, and caregivers. A system prototype has been developed and tested on elderly male participants with severe obstructive sleep apnea (OSA). The resultant data from the study showed the system could classify OSA with an accuracy of 81%. Furthermore, the system predicted behavioral changes with an accuracy of 66%.

Developmental Stage

Advanced stage of development.