Extracting information from the sensors installed in the homes of elderly pose a unique set of challenges. Add to it the short amount of time the clinicians and nurses have to analyze this data, and the problem becomes more complicated. The ongoing work in this project focuses on development of algorithms to glean information from in-home sensor data and then presenting it in the form of textual summaries using Natural Language Generation techniques.
This project is a collaboration between three different disciplines: Music, Engineering and Health Science. The long-term goal is to develop strategies for injury-prevention in undergraduate piano students. Common causes of injury among young pianists are: skeletal misalignment, excessive muscular tension and repetitive stress injury.
In this randomized controlled study, we are investigating and refining health alerts produced by environmentally-embedded in-home sensor networks designed to detect early signs of health change and functional decline in older adults, the keys to successful intervention.
The general objective is to develop and test a prototype ACL Gold computer software utilizing the Microsoft Kinect motion sensor that includes a screening tool and intervention to help prevent ACL tears in female youth athletes. The program measures the knee abduction angle during specific jumping and cutting tasks.
The main goal of this work was to develop algorithms for early illness recognition in elderly. Early illness recognition (EIR) is important, as research has shown that results in better medical outcomes and a reduction in health care cost. We developed methodologies (see Figure 1) that link sensor data to the medical (nursing) records for monitoring the residents of TigerPlace, an aging in place community from Columbia, Missouri.
Building on our current work, we propose to validate and deploy an innovative technological approach that automatically detects when falls have occurred or when the risk of falls is increasing. Subjects will not have to press buttons, pull cords or wear any devices. This new “passive” approach using sensors in the home could revolutionize detecting and preventing falls as well as measuring fall risk.
The dream of older Americans is to remain as active and independent as possible for as long as possible. They want to age in place, not in institutions like nursing homes. Recently, enabling technology in the form of low cost sensors, computers, and communications systems has become available, which with supportive health care services makes the dream of aging in place a reality.