Randomized Trial of Intelligent Sensor System for Early Illness Alerts in Senior Housing.

OBJECTIVES: Measure the clinical effectiveness and cost effectiveness of using sensor data from an environmentally embedded sensor system for early illness recognition. This sensor system has demonstrated in pilot studies to detect changes in function and in chronic diseases or acute illnesses on average 10 days to 2 weeks before usual assessment methods or self-reports of illness. DESIGN: Prospective intervention study in 13 assisted living (AL) communities of 171 residents randomly assigned to intervention (n=86) or comparison group (n=85) receiving usual care. METHODS: Intervention participants lived with the sensor system an average of one year. MEASUREMENTS: Continuous data collected 24 hours/7 days a week from motion sensors to measure overall activity, an under mattress bed sensor to capture respiration, pulse, and restlessness as people sleep, and a gait sensor that continuously measures gait speed, stride length and time, and automatically assess for increasing fall risk as the person walks around the apartment. Continuously running computer algorithms are applied to the sensor data and send health alerts to staff when there are changes in sensor data patterns. RESULTS: The randomized comparison group functionally declined more rapidly than the intervention group. Walking speed and several measures from GaitRite, velocity, step length left and right, stride length left and right, and the fall risk measure of functional ambulation profile (FAP) all had clinically significant changes. The walking speed increase (worse) and velocity decline (worse) of 0.073 m/s for comparison group exceeded 0.05 m/s, a value considered to be a minimum clinically important difference. No differences were measured in health care costs. CONCLUSIONS: These findings demonstrate that sensor data with health alerts and fall alerts sent to AL nursing staff can be an effective strategy to detect and intervene in early signs of illness or functional decline.

Using Embedded Sensors in Independent Living to Predict Gait Changes and Falls.

This study explored using Big Data, totaling 66 terabytes over 10 years, captured from sensor systems installed in independent living apartments to predict falls from pre-fall changes in residents' Kinect-recorded gait parameters. Over a period of 3 to 48 months, we analyzed gait parameters continuously collected for residents who actually fell ( n = 13) and those who did not fall ( n = 10). We analyzed associations between participants' fall events ( n = 69) and pre-fall changes in in-home gait speed and stride length ( n = 2,070). Preliminary results indicate that a cumulative change in speed over time is associated with the probability of a fall ( p < .0001). The odds of a resident falling within 3 weeks after a cumulative change of 2.54 cm/s is 4.22 times the odds of a resident falling within 3 weeks after no change in in-home gait speed. Results demonstrate using sensors to measure in-home gait parameters associated with the occurrence of future falls.

Testing non-wearable fall detection methods in the homes of older adults.

In this paper, we describe two longitudinal studies in which fall detection sensor technology was tested in the homes of older adults. The first study tested Doppler radar, a two-webcam system, and a depth camera system in ten apartments for two years. This continuous data collection allowed us to investigate the real-world setting of target users and compare the advantages and limitations of each sensor modality. Based on this study, the depth camera was chosen for a current ongoing study in which depth camera systems have been installed in 94 additional older adult apartments. We include a discussion of the different sensor systems, the pros and cons of each, and results of the fall detection and false alarms in the older adult homes.