A Kalman filter to estimate altitude change during a fall.

Barometers have been incorporated into fall detectors in order to enhance the accuracy of fall detection algorithms, however they are power-hungry devices. We present an offline evaluation of a Kalman filter (KF) for estimating the pressure change during a fall that enables low-power operation of the barometer. The KF takes advantage of the fact that a semi-permeable air membrane on a waterproof fall detector enclosure causes a delay in the equilibrium between internal and external enclosure pressure, and this delay enables the barometer to be switched off until a free-fall is detected. We assessed the KF using data obtained from simulated falls and activities of daily living. The KF was able to differentiate between fall and non-fall activities, with the average measured pressure change during a fall of 8 Pa best determined using a delay in pressure equalization of 20 seconds. The KF detected a change in altitude faster than a simple moving average filter (MAF), reaching 66% of its final value before the MAF was able to initialize.