Federated Action Recognition for Smart Worker Assistance Using FastPose
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912544996720640 |
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| author | Hegiste, Vinit Goyal, Vidit Legler, Tatjana Ruskowski, Martin |
| author_facet | Hegiste, Vinit Goyal, Vidit Legler, Tatjana Ruskowski, Martin |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_14113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Federated Action Recognition for Smart Worker Assistance Using FastPose Hegiste, Vinit Goyal, Vidit Legler, Tatjana Ruskowski, Martin Computer Vision and Pattern Recognition Artificial Intelligence Distributed, Parallel, and Cluster Computing Human-Computer Interaction 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. |
| title | Federated Action Recognition for Smart Worker Assistance Using FastPose |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Distributed, Parallel, and Cluster Computing Human-Computer Interaction |
| url | https://arxiv.org/abs/2508.14113 |