The aging population, outbreak of new infectious diseases and shortage of medical resources promote rapid development of telemedicine. Wireless textile body area network (TBAN), which combines functional textile and wireless body area network (WBAN), is gaining great attention as an efficient medium of remote medical care. This is because of its unique materials and application scenario, as well as its convenience and friendliness to the elderly. Moreover, it is an effective application for integrating edge computing with next generation of wearable technology. Nonetheless, it is unavoidable that TBAN has to deal with reliability and energy issues. Given these deficiencies and challenges, this paper focuses on the feasibility of achieving wearable energy neutral operation (ENO) in TBAN while maintaining robustness. In addition to adding user posture factors regarding network specifics, we combine hybrid energy harvesting (EH) techniques and duty cycle schemes. A hybrid radio frequency (RF) energy and Triboelectric nanogenerator (TENG) EH-assisted TBAN system is built in this work. We analyze and discuss the delay, data rate and packet error rate (PER) under five typical daily activities (standing, sitting, lying, walking, and running). To optimize the ENO problem, two reinforcement learning (Q-learning and Deep Q-Network (DQN)) based algorithms are proposed. According to numerical results, both algorithms ultimately lead to stable power levels compared to the continuous decline of battery power without optimization. DQN-based optimization performs better than Q-Learning. For instance, 14% and 56% improvements in PER and battery power, respectively.

人口老龄化、新型传染病的爆发和医疗资源的紧缺促进了远程医疗的快速发展。结合功能性纺织品和无线体域网（WBAN）的无线纺织品体域网（TBAN）作为远程医疗的有效媒介而受到广泛关注。这是因为它独特的材料和应用场景，以及它对老年人的便利性和友好性。此外，它是将边缘计算与下一代可穿戴技术相结合的有效应用。尽管如此，TBAN 还是不可避免地要处理可靠性和能源问题。鉴于这些不足和挑战，本文重点探讨了在 TBAN 中实现可穿戴能源中性运行 (ENO) 同时保持稳健性的可行性。除了添加有关网络细节的用户姿态因素外，我们还结合了混合能量收集 (EH) 技术和占空比方案。这项工作建立了混合射频 (RF) 能量和摩擦纳米发电机 (TENG) EH 辅助 TBAN 系统。我们分析和讨论了五种典型日常活动（站立、坐着、躺着、步行和跑步）下的延迟、数据速率和误包率 (PER)。为了优化 ENO 问题，提出了两种基于强化学习（Q 学习和深度 Q 网络 (DQN)）的算法。根据数值结果，与没有优化的电池电量持续下降相比，这两种算法最终都导致了稳定的电量水平。基于 DQN 的优化比 Q-Learning 表现更好。例如，PER 和电池电量分别提高了 14% 和 56%。