WSKL/FederatedHAR

Federated Pose HAR is a pose-based human activity recognition dataset designed for evaluating federated learning approaches in industrial and edge-computing scenarios. It contains eight distinct upper-body gestures, represented as sequences of 13 skeletal joint keypoints extracted using a modified FastPose pipeline from RGB-D video recordings.

The dataset is organized into:

Centralized splits: centralized_x_[train|val|test].txt and centralized_y_[train|val|test].txt, representing aggregated data from all clients for traditional machine learning baselines.

Client-wise splits: client_1 through client_5, each containing x_[train|val|test].txt and y_[train|val|test].txt, showing non-IID distributions for federated learning experiments.

This structure enables direct, apples-to-apples comparisons between centralized training, local per-client training, standard FedAvg, and ensemble federated setups.

Each file in the dataset stores:

X files: Normalized skeletal coordinates over time for each gesture sample.

Y files: Corresponding gesture class labels (1–8).

Data Collection & Preprocessing

Gesture videos were recorded in controlled indoor conditions using an RGB-D camera. The skeletal keypoints were extracted with FastPose, modified to output 13 key upper-body joints relevant for industrial gesture control tasks. The extracted 2D keypoint coordinates were normalized relative to torso position and scaled to account for subject height variations.

For federated learning experiments, the dataset was partitioned into five non-IID clients, with each client containing samples from a specific subset of gesture instances to mimic realistic heterogeneity. Centralized splits combine all samples across clients for baseline comparisons.

This dataset is particularly suited for human activity recognition, pose estimation-based sequence classification, and time-series classification tasks in privacy-sensitive environments. It supports experimentation in multi-client, non-IID scenarios, enabling research into robust FL strategies, personalization techniques, and aggregation methods.