SenseAgent: 一种用于自适应跨域IMU感知的LLM智能体
SenseAgent: An LLM Agent for Adaptive Cross-Domain IMU Sensing
AI总结：
SenseAgent利用LLM作为运行时规划器，通过诊断目标偏移并选择验证过的感知工具，实现跨域IMU活动识别的自适应闭环感知，显著提升在困难部署偏移下的性能。
AI中文摘要：
深度学习已经改善了移动和可穿戴应用中的惯性测量单元（IMU）感知。然而，在一个域中训练的IMU模型在用于新用户、新设备或新身体位置时往往变得不可靠。现有方法通常将此问题视为静态模型设计任务：他们预训练更强的表示，添加数据增强，或在部署前选择一种适应方法。在实践中，目标域是逐渐观察到的，标签稀缺，不同的域偏移需要不同的感知动作。本文提出了SenseAgent，一种用于跨域IMU活动识别的LLM引导的感知智能体。SenseAgent不是让LLM对原始IMU信号进行分类，而是将LLM用作运行时规划器，管理感知工具、源域经验记忆、在线目标记忆和验证器。该智能体从目标流中构建无标签诊断报告，并利用该报告决定是保持原始推理还是调用专门工具，包括重力感知、原型迁移和风格归一化。验证器在接受高风险工具决策之前检查源校准、目标记忆可靠性和无伤害标准。SenseAgent还支持无需重新训练骨干网络或替换无标签路线的稀缺反馈。这种设计将跨域IMU感知从固定推理流程转变为闭环感知过程，该过程诊断目标偏移，选择合适的感知动作，并拒绝不安全的适应。我们在多个IMU数据集和部署偏移上评估了SenseAgent。结果表明，其验证的路线选择改善了跨域感知，特别是在更难的放置和复合偏移下，并进一步受益于有限的用户反馈。
英文摘要：
Deep learning has improved inertial measurement unit (IMU) sensing for mobile and wearable applications. However, an IMU model trained in one domain often becomes unreliable when it is used with a new user, device, or body position. Existing methods usually treat this problem as a static model-design task: they pretrain a stronger representation, add data augmentation, or select one adaptation method before deployment. In practice, the target domain is only gradually observed, labels are scarce, and different domain shifts require different sensing actions. This paper presents SenseAgent, an LLM-guided sensing agent for cross-domain IMU activity recognition. Instead of asking an LLM to classify raw IMU signals, SenseAgent uses the LLM as a runtime planner over sensing tools, source-domain experience memory, online target memory, and verifiers. The agent builds a label-free diagnosis report from the target stream and uses it to decide whether to keep raw inference or invoke specialized tools, including gravity-aware sensing, prototype transfer, and style normalization. Verifiers check source calibration, target-memory reliability, and no-harm criteria before accepting high-risk tool decisions. SenseAgent also supports scarce feedback without retraining the backbone or replacing the label-free route. This design converts cross-domain IMU sensing from a fixed inference pipeline into a closed-loop sensing process that diagnoses target shifts, selects suitable sensing actions, and rejects unsafe adaptations. We evaluate SenseAgent across multiple IMU datasets and deployment shifts. Results show that its verified route selection improves cross-domain sensing, especially under harder placement and compound shifts, and further benefits from limited user feedback.
