type: concept tags: [edge-ai, medical, multimodal, transformer, agentic, energy-efficient, 模型] related: [[multimodal-edge-pruning]], [[networking-energy-agentic]], [[sustainability-ondevice-intelligence]] sources: - "[arXiv] Sense Less, Infer More: Agentic Multimodal Transformers for Edge Medical Intelligence" created: 2026-04-14
Sense Less, Infer More：Agent 驱动的边缘医疗智能¶
概念定义¶
"Sense Less, Infer More" 提出了一种基于 Agent 的多模态 Transformer 架构，用于边缘医疗监测。核心思想是：持续采集传感器数据（ECG、PPG、EMG、IMU）会快速耗尽可穿戴设备电量，因此应该让 Agent 决定"何时感知、何时推理"，减少不必要的数据采集。
关键创新¶
- Agent 级感知控制：由 AI Agent 决定何时激活传感器，而非持续采集
- 多模态融合：同时处理心电、脉搏、肌电、惯性等多种信号
- 能量感知设计：在诊断精度和能耗之间动态权衡
为什么重要¶
这代表了端侧 AI 的一个重要范式转变——从"被动感知+推理"到"主动决策+选择性感知"。在手机/可穿戴设备上，能耗是核心约束，Agent 级的感知控制可以： - 延长电池续航 2-5 倍 - 减少不必要的数据处理和存储 - 保持甚至提升诊断准确性
与手机端 AIOS 的关联¶
手机和可穿戴设备是健康监测的主要平台。"Sense Less, Infer More" 的 Agent 化感知控制理念，与 [[networking-energy-agentic]] 中 Agent 能耗优化的思路一脉相承。未来手机 AIOS 可以将这种能力作为系统级服务提供给健康类 App。
相关概念¶
- [[multimodal-edge-pruning]] — 多模态边缘推理剪枝
- [[networking-energy-agentic]] — Agent 推理能耗优化
- [[sustainability-ondevice-intelligence]] — 端侧智能可持续性
核心问题¶
Smart Voice Assistants (SVAs) are deeply embedded in the lives of youth, yet the mechanisms driving the privacy-protective behaviors among young users remain poorly understood. This study investigates how Canadian youth (aged 16-24) negotiate privacy with SVAs by developing and testing a structural model grounded in five key constructs: perceived privacy risks (PPR), perceived benefits (PPBf), algorithmic transparency and trust (ATT), privacy self-efficacy (PSE), and privacy-protective behaviors (PPB). A cross-sectional survey of N=469 youth was analyzed using partial least squares structural equation modeling. Results reveal that PSE is the strongest predictor of PPB, while the effect of ATT on PPB is fully mediated by PSE. This identifies a critical efficacy gap, where youth's confidence
为什么重要¶
本研究/产品对手机端 AIOS 生态有重要参考价值。推动端侧 AI 从概念走向实际部署。
关联¶
- [[clawmobile-agentic]] — Agent 系统架构
- [[mnn-350]] — 推理引擎
- [[kv-cache-quantization-ondevice]] — 内存优化