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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.11245 |
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| _version_ | 1866909957607129088 |
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| author | Chen, Zikang Xie, Tan Wang, Qinchuan Zheng, Heming Lu, Xudong |
| author_facet | Chen, Zikang Xie, Tan Wang, Qinchuan Zheng, Heming Lu, Xudong |
| contents | Postoperative upper limb dysfunction is prevalent among breast cancer survivors, yet their adherence to at-home rehabilitation exercises is low amidst limited nursing resources. The hardware overhead of commonly adopted VR-based mHealth solutions further hinders their widespread clinical application. Therefore, we developed Breast-Rehab, a novel, low-cost mHealth system to provide patients with out-of-hospital upper limb rehabilitation management. Breast-Rehab integrates a bespoke human action recognition algorithm with a retrieval-augmented generation (RAG) framework. By fusing visual and 3D skeletal data, our model accurately segments exercise videos recorded in uncontrolled home environments, outperforming standard models. These segmented clips, combined with a domain-specific knowledge base, guide a multi-modal large language model to generate clinically relevant assessment reports. This approach significantly reduces computational overhead and mitigates model hallucinations. We implemented the system as a WeChat Mini Program and a nurse-facing dashboard. A preliminary clinical study validated the system's feasibility and user acceptance, with patients achieving an average exercise frequency of 0.59 sessions/day over a two-week period. This work thus presents a complete, validated pipeline for AI-driven, at-home rehabilitation monitoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11245 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Breast-Rehab: A Postoperative Breast Cancer Rehabilitation Training Assessment System Based on Human Action Recognition Chen, Zikang Xie, Tan Wang, Qinchuan Zheng, Heming Lu, Xudong Human-Computer Interaction Postoperative upper limb dysfunction is prevalent among breast cancer survivors, yet their adherence to at-home rehabilitation exercises is low amidst limited nursing resources. The hardware overhead of commonly adopted VR-based mHealth solutions further hinders their widespread clinical application. Therefore, we developed Breast-Rehab, a novel, low-cost mHealth system to provide patients with out-of-hospital upper limb rehabilitation management. Breast-Rehab integrates a bespoke human action recognition algorithm with a retrieval-augmented generation (RAG) framework. By fusing visual and 3D skeletal data, our model accurately segments exercise videos recorded in uncontrolled home environments, outperforming standard models. These segmented clips, combined with a domain-specific knowledge base, guide a multi-modal large language model to generate clinically relevant assessment reports. This approach significantly reduces computational overhead and mitigates model hallucinations. We implemented the system as a WeChat Mini Program and a nurse-facing dashboard. A preliminary clinical study validated the system's feasibility and user acceptance, with patients achieving an average exercise frequency of 0.59 sessions/day over a two-week period. This work thus presents a complete, validated pipeline for AI-driven, at-home rehabilitation monitoring. |
| title | Breast-Rehab: A Postoperative Breast Cancer Rehabilitation Training Assessment System Based on Human Action Recognition |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2512.11245 |