  The ageing population trend is correlated with an increased prevalence of
acquired cognitive impairments such as dementia. Although there is no cure for
dementia, a timely diagnosis helps in obtaining necessary support and
appropriate medication. Researchers are working urgently to develop effective
technological tools that can help doctors undertake early identification of
cognitive disorder. In particular, screening for dementia in ageing Deaf
signers of British Sign Language (BSL) poses additional challenges as the
diagnostic process is bound up with conditions such as quality and availability
of interpreters, as well as appropriate questionnaires and cognitive tests. On
the other hand, deep learning based approaches for image and video analysis and
understanding are promising, particularly the adoption of Convolutional Neural
Network (CNN), which require large amounts of training data. In this paper,
however, we demonstrate novelty in the following way: a) a multi-modal machine
learning based automatic recognition toolkit for early stages of dementia among
BSL users in that features from several parts of the body contributing to the
sign envelope, e.g., hand-arm movements and facial expressions, are combined,
b) universality in that it is possible to apply our technique to users of any
sign language, since it is language independent, c) given the trade-off between
complexity and accuracy of machine learning (ML) prediction models as well as
the limited amount of training and testing data being available, we show that
our approach is not over-fitted and has the potential to scale up.
