ORIGINAL RESEARCH article
Front. Oncol.
Sec. Surgical Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1604041
This article is part of the Research TopicExploring Robotic-Assisted Techniques in Urologic Oncology: Challenges and Future DirectionsView all 5 articles
Personalized Sports Recommendation Systems Using Robotic-Assisted Techniques in Urologic Oncology Recovery
Provisionally accepted
- Zhongyuan University of Technology, Zhengzhou, China
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The integration of robotic-assisted techniques in urologic oncology recovery has significantly improved surgical precision and patient outcomes. However, postoperative rehabilitation remains a crucial challenge, necessitating innovative approaches for enhancing physical recovery and quality of life. Personalized sports recommendation systems have emerged as a promising solution, leveraging sports analytics, machine learning, and biomechanical modeling to tailor rehabilitation exercises. Traditional methods rely on generalized rehabilitation protocols, often failing to consider individual patient conditions, recovery progress, and biomechanical constraints. These limitations hinder optimal rehabilitation and prolong recovery times. To address these challenges, we propose a novel framework integrating robotic-assisted assessment with personalized sports analytics.Our approach utilizes a Dynamic Sports Performance Network (DSPN), which combines spatiotemporal data analysis, reinforcement learning, and real-time feedback mechanisms to optimize exercise recommendations. By incorporating multi-agent learning and predictive modeling, the system adapts rehabilitation plans based on patient performance, ensuring a tailored and effective recovery process. The system can integrate wearable sensor data and EMG signals to further refine exercise precision and monitor muscular responses in real time. Experimental evaluations demonstrate that our method significantly outperforms conventional rehabilitation strategies, offering higher precision in exercise recommendations, improved adherence rates, and enhanced recovery efficiency. This research provides a new direction in robotic-assisted rehabilitation, bridging the gap between sports science, intelligent systems, and urologic oncology recovery through interdisciplinary innovation and patient-centered design.
Keywords: robotic rehabilitation, Personalized exercise, Sports analytics, reinforcement learning, Urologic oncology
Received: 01 Apr 2025; Accepted: 02 Jun 2025.
Copyright: © 2025 Shen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yawen Shen, Zhongyuan University of Technology, Zhengzhou, China
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