By far the biggest injury risk faced by people with late onset dementia is a serious fall. Commercial fall detectors are available which automatically alert a call centre or carer if they detect a fall. They use accelerometers to look for the kinematics of a fall but this method is unreliable and the frequent false alarms must be cancelled by the wearer. This is inappropriate for someone with dementia. This thesis examines how a wrist-worn fall detector better suited to someone with dementia might be built. It reviews what other sensors could be used alongside accelerometers, and whether looking for the physiological effects of falling might be beneficial. It concludes that the pulse provides a source of data and describes three empirical trials to examine whether the body pose can be determined from the pulse waveform. A small convenience sample proved the viability of the concept, followed by a larger study to investigate it further, and finally a trial in people of the same age group as late onset dementia sufferers. Producing a technically better device is not sufficient, as it must also be usable by the people it is intended for. The thesis describes two qualitative studies which use carers to define, and then evaluate, a conceptual fall detector suitable for people with moderate or severe dementia which fits underneath a wrist watch.The thesis argues that wearable fall detectors should utilise physiological data to complement kinematic data. It demonstrates the practicality of a novel technique for determining body position using the pulse waveform, and finally concludes that it would be possible to build the conceptual fall detector utilising this technique.
|Date of Award||28 Jun 2016|
|Supervisor||Christopher Eccleston (Supervisor), Edmund Keogh (Supervisor) & Nigel Harris (Supervisor)|
- fall detection
- fall detector
- user centred design