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Abstract
Unique features of body segment kinematics in falls and activities of daily living
(ADL) are applied to make automatic detection of a fall in its descending phase, prior
to impact, possible. Fall-related injuries can thus be prevented or reduced by deploying
fall impact reduction systems, such as an inflatable airbag for hip protection, before
the impact. In this application, the authors propose the following hypothesis: "Thigh
segments normally do not exceed a certain threshold angle to the side and forward
directions in ADL, whereas this abnormal behavior occurs during a fall activity".
Torso and thigh wearable inertial sensors (3D accelerometer and 2D gyroscope) are
used and the whole system is based on a body area network (BAN) for the comfort of
the wearer during a long term application. The hypothesis was validated in an experiment
with 21 young healthy volunteers performing both normal ADL and fall activities. Results
show that falls could be detected with an average lead-time of 700 ms before the impact
occurs, with no false alarms (100% specificity), a sensitivity of 95.2%. This is the
longest lead-time achieved so far in pre-impact fall detection.