  In this paper, we investigate hand gesture classifiers that rely upon the
abstracted 'skeletal' data recorded using the RGB-Depth sensor. We focus on
'skeletal' data represented by the body joint coordinates, from the Praxis
dataset. The PRAXIS dataset contains recordings of patients with cortical
pathologies such as Alzheimer's disease, performing a Praxis test under the
direction of a clinician. In this paper, we propose hand gesture classifiers
that are more effective with the PRAXIS dataset than previously proposed
models. Body joint data offers a compressed form of data that can be analyzed
specifically for hand gesture recognition. Using a combination of windowing
techniques with deep learning architecture such as a Recurrent Neural Network
(RNN), we achieved an overall accuracy of 70.8% using only body joint data. In
addition, we investigated a long-short-term-memory (LSTM) to extract and
analyze the movement of the joints through time to recognize the hand gestures
being performed and achieved a gesture recognition rate of 74.3% and 67.3% for
static and dynamic gestures, respectively. The proposed approach contributed to
the task of developing an automated, accurate, and inexpensive approach to
diagnosing cortical pathologies for multiple healthcare applications.
