MultiPosture: A Dataset of body joints keypoints extracted using MediaPipe for multi-task sitting posture recognition with upper and lower body labels

This dataset contains skeletal pose data extracted from video recordings of 13 participants performing various sitting postures in home environments. The data was processed using MediaPipe Pose Heavy model and includes 4,800 frames of 3D skeletal coordinates (x, y, z) for 11 key body joints, with each frame manually labeled for both upper and lower body posture classifications. The data is stored in CSV format with normalized coordinates relative to hip center, containing 33 input dimensions (11 joints × 3 coordinates) representing key skeletal points. To protect participant privacy, only the processed skeletal coordinates are included, with no raw video or image data due to privacy constraints. Upper Body Labels: TUP: Upright trunk position TLB: Trunk leaning backward TLF: Trunk leaning forward TLR: Trunk leaning right TLL: Trunk leaning left Lower Body Labels: LAP: Legs apart LWA: Legs wide apart LCS: Legs closed LCR: Legs crossed right over left LCL: Legs crossed left over right LLR: Legs lateral right LLL: Legs lateral left Each frame in the dataset has been manually labeled and validated by experts, making it particularly suitable for developing and evaluating machine learning models for ergonomic monitoring systems, ambient assisted living applications, and general posture recognition research. This dataset was collected as part of the study: D. Carneros-Prado, L. Cabañero-Gómez, E. Johnson, I. González, J. Fontecha and R. Hervás, "A Comparison Between Multilayer Perceptrons and Kolmogorov-Arnold Networks for Multi-Task Classification in Sitting Posture Recognition," in IEEE Access, vol. 12, pp. 180198-180209, 2024, doi: 10.1109/ACCESS.2024.3510034. link