Using Embedded Sensors in Independent Living to Predict Gait Changes and Falls. (January 2017) - Phillips, Lorraine J.; DeRoche, Chelsea B.; Rantz, Marilyn; Alexander, Gregory L.; Skubic, Marjorie; Despins, Laurel; Abbott, Carmen; Harris, Bradford H.; Galambos, Colleen; Koopman, Richelle J.
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.