We introduce a framework for dynamic evaluation of the fingers movements. This framework estimates angle measurements from joints computed by a hand pose estimation algorithm. The angle between joints can be used as an indicative of current movement capabilities and function.
We introduce a framework for dynamic evaluation of the fingers movements:
flexion, extension, abduction and adduction. This framework estimates angle
measurements from joints computed by a hand pose estimation algorithm using a
depth sensor (Realsense SR300). Given depth maps as input, our framework uses
Pose-REN, which is a state-of-art hand pose estimation method that estimates 3D
hand joint positions using a deep convolutional neural network. The pose
estimation algorithm runs in real-time, allowing users to visualise 3D skeleton
tracking results at the same time as the depth images are acquired. Once 3D
joint poses are obtained, our framework estimates a plane containing the wrist
and MCP joints and measures flexion/extension and abduction/aduction angles by
applying computational geometry operations with respect to this plane. We
analysed flexion and abduction movement patterns using real data, extracting
the movement trajectories. Our preliminary results show that this method allows
an automatic discrimination of hands with Rheumatoid Arthritis (RA) and healthy
patients. The angle between joints can be used as an indicative of current
movement capabilities and function. Although the measurements can be noisy and
less accurate than those obtained statically through goniometry, the
acquisition is much easier, non-invasive and patient-friendly, which shows the
potential of our approach. The system can be used with and without orthosis.
Our framework allows the acquisition of measurements with minimal intervention
and significantly reduces the evaluation time.