Researchers tend to agree that falls are, by definition, unintentional and that
sensor algorithms (the processes that allows a computer program to identify a
fall among data from sensors) perform poorly when attempting to detect falls ‘in
the wild’ (a phrase some scientists use to mean ‘in reality’). Algorithm
development has been reliant on simulation, i.e. asking actors to throw
themselves intentionally to the ground. This is unusual (no one studies faked
coughs or headaches) and uninformative (no one can intend the unintentional).
Researchers would increase their chances of detecting ‘real’ falls in ‘the real
world’ by studying the behaviour of fallers, however, vulnerable, before, during
and after the event: the literature on the circumstances of falling is more
informative than any number of faked approximations. A complimentary knowledge
base (in falls, sensors and/or signals) enables multidisciplinary teams of
clinicians, engineers and computer scientists to tackle fall detection and aim
for fall prevention. Throughout this paper, I discuss differences between falls,
‘intentional falling’ and simulations, and the balance between simulation and
reality in falls research, finally suggesting ways in which researchers can
access examples of falls without resorting to fakery.