REINA: A Recognize-Then-Infer Wearable-to-App AI Framework for Breast Cancer Rehabilitation

Abstract

AO_SCPLOWBSTRACTC_SCPLOWBreast cancer survivors frequently experience upper-limb impairments, making continuous monitoring essential for effective rehabilitation. We propose REINA (Recognize-Then-Infer Wearable-to-App AI Framework), a two-stage deep-learning approach for remote monitoring of motor function during breast cancer rehabilitation using wearable-device data. Inertial measurement unit (IMU) signals from wearable devices are first used to recognize physical activities via supervised learning, followed by an activity-specific recurrent neural network (RNN) to infer corresponding electromyography (EMG) signals. REINA establishes reliable inference of neuromuscular activity from wearable IMU data, enabling real-time, cost-effective assessment of motor function recovery in real-world settings.

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BibTeX RIS

BibTeX

@misc{zhuang2026reina,
  title = {{REINA: A Recognize-Then-Infer Wearable-to-App AI Framework for Breast Cancer Rehabilitation}},
  author = {Q. Zhuang and C. Mou and B. Liu and M. R. Fu and G. W. King},
  howpublished = {medRxiv},
  year = {2026},
  month = aug,
  doi = {10.64898/2026.08.29.26361725},
  url = {https://www.medrxiv.org/content/10.64898/2026.08.29.26361725v1},
  note = {CC BY-NC-ND},
}

RIS

TY  - UNPB
TI  - REINA: A Recognize-Then-Infer Wearable-to-App AI Framework for Breast Cancer Rehabilitation
AU  - Q. Zhuang
AU  - C. Mou
AU  - B. Liu
AU  - M. R. Fu
AU  - G. W. King
PY  - 2026
DA  - 2026/08/29
PB  - medRxiv
DO  - 10.64898/2026.08.29.26361725
UR  - https://www.medrxiv.org/content/10.64898/2026.08.29.26361725v1
AB  - AO_SCPLOWBSTRACTC_SCPLOWBreast cancer survivors frequently experience upper-limb impairments, making continuous monitoring essential for effective rehabilitation. We propose REINA (Recognize-Then-Infer Wearable-to-App AI Framework), a two-stage deep-learning approach for remote monitoring of motor function during breast cancer rehabilitation using wearable-device data. Inertial measurement unit (IMU) signals from wearable devices are first used to recognize physical activities via supervised learning, followed by an activity-specific recurrent neural network (RNN) to infer corresponding electromyography (EMG) signals. REINA establishes reliable inference of neuromuscular activity from wearable IMU data, enabling real-time, cost-effective assessment of motor function recovery in real-world settings.
ER  -

Abstract

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