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.

通过对 PRAXIS 数据集中的身体关键点坐标进行分析，我们提出了比先前模型更有效的手势分类器。利用窗口技术与深度学习架构（如循环神经网络），我们仅使用身体关键数据就实现了 70.8% 的准确率。此外，我们通过长短时记忆（LSTM）提取和分析关节的时间动态来识别手势，静态手势识别率为 74.3%，动态手势识别率为 67.3%。该方法可用于开发自动、准确且廉价的方法，用于诊断多种皮层病理的医疗应用。