Doppler radar sensor positioning in a fall detection system

Extended, continuous measures of functional status in community dwelling persons with Alzheimer's and related dementia: Infrastructure, performance, tradeoffs, preliminary data, and promise

BACKGROUND

The past decades have seen phenomenal growth in the availability of inexpensive and powerful personal computing devices. Efforts to leverage these devices to improve health care outcomes promise to remake many aspects of healthcare delivery, but remain in their infancy.

NEW METHOD

We describe the development of a mobile health platform designed for daily measures of functional status in ambulatory, community dwelling subjects, including those who have Alzheimer's disease or related neurodegenerative disorders. Using Smartwatches and Smartphones we measure subject overall activity and outdoor location (to derive their lifespace). These clinically-relevant measures allow us to track a subject's functional status in their natural environment over prolonged periods of time without repeated visits to healthcare providers. Functional status metrics are integrated with medical information and caregiver reports, which are used by a caregiving team to guide referrals for physician/APRN/NP care. COMPARISON: with Existing Methods We describe the design tradeoffs involved in all aspects of our current system architecture, focusing on decisions with significant impact on system cost, performance, scalability, and user-adherence.

RESULTS

We provide real-world data from current subject enrollees demonstrating system accuracy and reliability.

CONCLUSIONS

We document real-world feasibility in a group of men and women with dementia that Smartwatches/Smartphones can provide long-term, relevant clinical data regarding individual functional status. We describe the underlying considerations of this system so that interested organizations can adapt and scale our approach to their needs. Finally, we provide a potential agenda to guide development of future systems.

A New Paradigm of Technology-Enabled ‘Vital Signs’ for Early Detection of Health Change for Older Adults

Environmentally embedded (nonwearable) sensor technology is in continuous use in elder housing to monitor a new set of ‘vital signs' that continuously measure the functional status of older adults, detect potential changes in health or functional status, and alert healthcare providers for early recognition and treatment of those changes. Older adult participants' respiration, pulse, and restlessness are monitored as they sleep. Gait speed, stride length, and stride time are calculated daily, and automatically assess for increasing fall risk. Activity levels are summarized and graphically displayed for easy interpretation. Falls are detected when they occur and alerts are sent immediately to healthcare providers, so time to rescue may be reduced. Automated health alerts are sent to healthcare staff, based on continuously running algorithms applied to the sensor data, days and weeks before typical signs or symptoms are detected by the person, family members, or healthcare providers. Discovering these new functional status ‘vital signs', developing automated methods for interpreting them, and alerting others when changes occur have the potential to transform chronic illness management and facilitate aging in place through the end of life. Key findings of research in progress at the University of Missouri are discussed in this viewpoint article, as well as obstacles to widespread adoption.

Automated In-Home Fall Risk Assessment and Detection Sensor System for Elders

PURPOSE OF THE STUDY

Falls are a major problem for the elderly people leading to injury, disability, and even death. An unobtrusive, in-home sensor system that continuously monitors older adults for fall risk and detects falls could revolutionize fall prevention and care.

DESIGN AND METHODS

A fall risk and detection system was developed and installed in the apartments of 19 older adults at a senior living facility. The system includes pulse-Doppler radar, a Microsoft Kinect, and 2 web cameras. To collect data for comparison with sensor data and for algorithm development, stunt actors performed falls in participants' apartments each month for 2 years and participants completed fall risk assessments (FRAs) using clinically valid, standardized instruments. The FRAs were scored by clinicians and recorded by the sensing modalities. Participants' gait parameters were measured as they walked on a GAITRite mat. These data were used as ground truth, objective data to use in algorithm development and to compare with radar and Kinect generated variables.

RESULTS

All FRAs are highly correlated (p < .01) with the Kinect gait velocity and Kinect stride length. Radar velocity is correlated (p < .05) to all the FRAs and highly correlated (p < .01) to most. Real-time alerts of actual falls are being sent to clinicians providing faster responses to urgent situations.

IMPLICATIONS

The in-home FRA and detection system has the potential to help older adults remain independent, maintain functional ability, and live at home longer.