Hand Movement Trajectories Tracking (Based on OpenPose Skeleton)

Methodology

To utilise OpenPose library for hand movement trajectory tracking

• Firstly please intall the tf-openpose model following the instructions of the link: https://github.com/ildoonet/tf-pose-estimation. Thanks to the amazing work of Ildoo Kim, that translated most code of the OpenPose Library to Python.
• Then download the code for hand movenment tracking of this repository. Hopefully you will get the results as shown below. 😉

Results

Version Notes

For the reference, this model has been developed and tested in the the CPU desktop of 8 GB RAM 3.00 GHz Intel Core i5-4590SCPU processor, also on a GPU desktop with two NVIDIA GeForce GTX 1080Ti adapter cards and 3.3 GHz In-tel Core i9-7900X CPU with 16 GB RAM.

for CPU environment the model was implemeted in:

• Tensorflow 1.11
• python 3.6.5
• OpenCV 3.3.1

for GPU environment the model was implemeted in

• Tensorflow 1.12
• Python 3.6.8
• OpenCV 3.4.2

Citations

@inproceedings{liang2019handtracking,
  author = {X. Liang, E. Kapetanios, B. Woll and A. Angelopoulou},
  booktitle = {Cross Domain Conference for Machine
Learning and Knowledge Extraction (CD-MAKE2019)},
  title = {Real Time Hand Movement Trajectory Tracking for Enhancing
Dementia Screening in Ageing Deaf Signers of British Sign Language},
  year = {2019}
}