Include50 Full Body Keypoints

It comprises human body skeletal landmarks extracted from frame-level videos of Indian Sign Language (ISL) words, it has 50 classes or Signs. Landmark extraction was performed using the MediaPipe Holistic pipeline, which simultaneously tracks pose, face, and hand keypoints. This process yields 543 landmarks per frame, including 33 pose, 468 facial, and 21 keypoints for each hand. Each landmark is represented by its spatial coordinates (x,y,z)(x, y, z)(x,y,z), resulting in a 1,629-dimensional feature vector (543×3)(543 \times 3)(543×3) per timestep. The extracted features were serialized and stored in NumPy (.npy) format to enable efficient data loading during training. Keypoints were extracted using the INCLUDE-50 dataset. Further details are available in the corresponding research work. INCLUDE 50 Research Work Citation @inproceedings{10.1145/3394171.3413528,author = {Sridhar, Advaith and Ganesan, Rohith Gandhi and Kumar, Pratyush and Khapra, Mitesh},title = {INCLUDE: A Large Scale Dataset for Indian Sign Language Recognition},year = {2020},isbn = {9781450379885},publisher = {Association for Computing Machinery},doi = {10.1145/3394171.3413528},numpages = {10},series = {MM '20}}