{"ID":2881043,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.14113","arxiv_id":"2508.14113","title":"Federated Action Recognition for Smart Worker Assistance Using FastPose","abstract":"In smart manufacturing environments, accurate and real-time recognition of worker actions is essential for productivity, safety, and human-machine collaboration. While skeleton-based human activity recognition (HAR) offers robustness to lighting, viewpoint, and background variations, most existing approaches rely on centralized datasets, which are impractical in privacy-sensitive industrial scenarios. This paper presents a federated learning (FL) framework for pose-based HAR using a custom skeletal dataset of eight industrially relevant upper-body gestures, captured from five participants and processed using a modified FastPose model. Two temporal backbones, an LSTM and a Transformer encoder, are trained and evaluated under four paradigms: centralized, local (per-client), FL with weighted federated averaging (FedAvg), and federated ensemble learning (FedEnsemble). On the global test set, the FL Transformer improves over centralized training by +12.4 percentage points, with FedEnsemble delivering a +16.3 percentage points gain. On an unseen external client, FL and FedEnsemble exceed centralized accuracy by +52.6 and +58.3 percentage points, respectively. These results demonstrate that FL not only preserves privacy but also substantially enhances cross-user generalization, establishing it as a practical solution for scalable, privacy-aware HAR in heterogeneous industrial settings.","short_abstract":"In smart manufacturing environments, accurate and real-time recognition of worker actions is essential for productivity, safety, and human-machine collaboration. While skeleton-based human activity recognition (HAR) offers robustness to lighting, viewpoint, and background variations, most existing approaches rely on ce...","url_abs":"https://arxiv.org/abs/2508.14113","url_pdf":"https://arxiv.org/pdf/2508.14113v1","authors":"[\"Vinit Hegiste\",\"Vidit Goyal\",\"Tatjana Legler\",\"Martin Ruskowski\"]","published":"2025-08-18T09:28:15Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.DC\",\"cs.HC\"]","methods":"[\"Transformer\"]","has_code":false}