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

Abstract

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.

Keywords: falls; gait speed; older adults; sensors; stride length.

Conflict of interest statement

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1

Motion sensor data density visualization including night-time motion density: Increase alert.

Figure 2

Gait parameter interface showing calculated stride length and time, gait speed, and alerts. Note. TUG = Timed Up and Go.

Figure 3

Weekly gait speed averages with falls plotted on in-home gait speed graph. Note. ○ = average gait speed; ★ = fall event.