Using Structured Light Sensing with Machine Learning to Detect Unwitnessed In-Home Falls

Project Summary/Abstract Older adults are disproportionately affected by falls. Older adults who have memory loss (mild to moderate cognitive impairment) can forget to wear wireless alert pendants or wristbands that are used in case they fall in their home. Falls among adults 65 and older caused over 34,000 deaths in 2019, making it the leading cause of injury death for that group. Older adult falls cost $50 billion in medical costs annually. Of those who fall, many suffer serious injuries, such as hip fractures and head traumas, which reduces their mobility, independence, and life expectancy. Studies have found an increased risk of complications associated with prolonged periods of lying on the floor following a fall. Older adults living alone or with memory loss are at the greatest risk of delayed assistance following a fall and cannot always be counted on to use their wearable emergency alert button. A low-cost, unobtrusive system capable of automatically detecting and alerting falls in the homes of older adults living alone or those with mild to moderate cognitive impairment, could help significantly reduce the incidence of delayed assistance after a fall. This phase II project, building on a successful phase I project, will develop an innovative new in-home fall monitoring system that solves many practical problems with existing systems. The technical approach uses structured light sensing (SLS) that creates 3D point clouds of a scene to allow detection of motion sequences using machine learning (ML) algorithms which will allow for the automatic detection of a person’s fall. There are multiple benefits of this approach for the target users: 1. The person is not required to carry or wear an electronic device that might be forgotten to be worn. 2. No action is required to be taken by the person after a fall. 3. The system does not use visible light video that would create privacy concerns for the person. 4. The system can work in darkness or very low light unlike visible light camera-based approaches. 5. The system is unobtrusive and works with existing Personal Emergency Response Systems (PERS), with minimal or no active user interaction. The SLS fall detection system is intended to work with multiple vendors of in-home alert systems. It will operate in lieu of or in parallel with, wearable buttons to signal an alert. The proposed system would be used if caregivers determine that a wearable button is not an adequate solution for the person being monitored. The proposed devices will be mounted high on the wall of each room and will wirelessly communicate to a central device in the home. The central device will send the alert to the in-home alert system upon detecting a fall. The proposed solution will not require any Internet connectivity. The out-of-home communication method is provided by the chosen vendor of the in-home alert system.