用手机传感器同时识别久坐行为和社交场景，提升情境感知准确率。
DySTAN: Joint Modeling of Sedentary Activity and Social Context from Smartphone Sensors
- 设计双标签数据采集应用LogMe，结合传感器与用户自报。
- 提出DySTAN模型，联合建模活动与社交上下文，提升分类性能。
- 在真实数据上实现21.8%的精度提升，适合移动健康研究者使用。
从智能手机传感器数据中准确识别人类情境仍具挑战性，尤其在久坐场景下，如学习、上课、放松和进食等活动的惯性模式高度相似。此外，社交情境对理解用户行为至关重要，却常被移动感知研究忽视。为此，我们开发了LogMe应用，被动采集加速度计、陀螺仪、磁力计和旋转矢量数据，并每小时提示用户进行自报，记录久坐活动与社交情境。基于此双标签数据集，我们提出DySTAN（动态交叉缝合任务注意力网络）——一种多任务学习框架，通过共享传感器输入联合分类两类情境。该模型融合任务特异性层与跨任务注意力机制，有效捕捉细微差异。实验显示，相比单任务CNN-BiLSTM-GRU（CBG）模型，其久坐活动宏平均F1提升21.8%；相较最强多任务基线Sluice Network（SN），提升8.2%。结果表明，联合建模多个共现情境维度能显著提升移动情境识别的准确性和鲁棒性。
原文摘要 · Abstract (English)
Accurately recognizing human context from smartphone sensor data remains a significant challenge, especially in sedentary settings where activities such as studying, attending lectures, relaxing, and eating exhibit highly similar inertial patterns. Furthermore, social context plays a critical role in understanding user behavior, yet is often overlooked in mobile sensing research. To address these gaps, we introduce LogMe, a mobile sensing application that passively collects smartphone sensor data (accelerometer, gyroscope, magnetometer, and rotation vector) and prompts users for hourly self-reports capturing both sedentary activity and social context. Using this dual-label dataset, we propose DySTAN (Dynamic Cross-Stitch with Task Attention Network), a multi-task learning framework that jointly classifies both context dimensions from shared sensor inputs. It integrates task-specific layers with cross-task attention to model subtle distinctions effectively. DySTAN improves sedentary activity macro F1 scores by 21.8% over a single-task CNN-BiLSTM-GRU (CBG) model and by 8.2% over the strongest multi-task baseline, Sluice Network (SN). These results demonstrate the importance of modeling multiple, co-occurring context dimensions to improve the accuracy and robustness of mobile context recognition.
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