HB-PVI：面向复杂活动识别的一种层次贝叶斯个性化与信息价值框架
HB-PVI: A Hierarchical Bayesian Personalization and Value-of-Information Framework for Complex Activity Recognition
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中文总结 AI 辅助
HB-PVI框架通过层次贝叶斯建模和一步EVSI停止规则，在47人复杂活动识别中评估个性化机制，发现适配器个性化增益微小，最优策略为不购买标签，减少100%标注且损失极低，主张人群优先部署。
中文摘要 AI 辅助
个性化可以提升活动识别性能，但参与者特定的收益存在异质性，且每增加一个校准标签都会产生获取成本。本研究提出了HB-PVI，一种层次贝叶斯个性化与信息价值框架，该框架联合建模了参与者异质性、四种个性化机制的收益与危害，以及额外标签的经济价值，应用于包含47名参与者的MUSIC-CAR复杂活动队列。一种防泄漏的留一参与者评估方法将序贯蒙特卡洛参与者效应更新器与Student-t层次增益模型以及一步期望样本信息价值（EVSI）停止规则相结合。适配器个性化产生了较小的平均F1正增益，从1个标签时的0.00099增长到10个标签时的0.00198，而适配器加头部以及原型残差个性化平均为负。在主要实际收益阈值（Δ_min=0.01）和成本设置下，一步EVSI在每个决策状态均为零，因此策略未购买任何标签，并保留了对全部47名参与者的人群推断，与始终停止策略完全一致（实际等价区域概率=1）。相对于固定的十次适配器个性化，该方法将标注量减少了100%，同时保持后验平均F1损失为0.00217（95%可信区间：0.00048至0.00389），后验概率为0.9992，即保持在0.005容差以下。HB-PVI在216种成本阈值设置中的199种中实现了效用最优，并且在所有等于或高于主要标签成本的设置中均实现效用最优。这些结果主张在个性化收益相对于标注、计算和危害成本较小时采用人群优先的部署策略，并表明在健康感知应用中，应基于信息价值推理而非原始预测精度来驱动个性化决策。
英文摘要
Personalization can improve activity-recognition performance, but participant-specific gains are heterogeneous, and every additional calibration label has an acquisition cost. This study presents HB-PVI, a hierarchical Bayesian personalization and value-of-information framework jointly modeling participant heterogeneity, the benefit and harm of four personalization mechanisms, and the economic value of an additional label, for the 47-participant MUSIC-CAR complex-activity cohort. A leakage-safe, leave-one-participant-out evaluation combines a sequential-Monte-Carlo participant-effect updater with a Student-$t$ hierarchical gain model and a one-step expected-value-of-sample-information (EVSI) stopping rule. Adapter personalization produced small positive mean F1 gains, growing from 0.00099 at one label to 0.00198 at ten, while adapter-plus-head and prototype-residual personalization were negative on average. Under the primary practical-benefit threshold ($Δ_{\min}=0.01$) and cost setting, one-step EVSI was zero at every decision state, so the policy purchased no labels and retained population inference for all 47 participants, matching always-stop exactly (region-of-practical-equivalence probability $=1$). Relative to fixed ten-shot adapter personalization, this reduced labeling by 100\% while keeping the posterior mean F1 loss at 0.00217 (95\% credible interval, 0.00048 to 0.00389), with posterior probability 0.9992 of remaining below the 0.005 tolerance. HB-PVI was utility-optimal in 199 of 216 cost-threshold settings and in every setting at or above the primary label cost. These results argue for a population-first deployment policy whenever personalization gains are small relative to labeling, computation, and harm costs, and show that value-of-information reasoning, not raw predictive accuracy, should drive personalization decisions in health-sensing applications.
发表机构
- Worcester Polytechnic Institute（伍斯特理工学院）
机构由 AI 辅助整理，请以论文原文为准。
