通过时间上下文提升智能家居中老年人活动识别准确率。
Integrating Temporal Context into Streaming Data for Human Activity Recognition in Smart Home
- 按时间段聚类活动，动态调整特征权重以捕捉时序模式。
- 在四个真实数据集上，三项指标优于现有方法，低数据场景提升显著。
- 适合关注智慧养老与传感器数据分析的开发者和研究者。
随着全球人口老龄化，支持老年人居家独立安全生活至关重要。利用被动红外传感器（PIR）和门磁传感器等无处不在的传感设备，正成为监测日常活动并推动预防性健康干预的重要手段。基于被动传感器的人类活动识别（HAR）通常依赖传统机器学习，包括数据分段、特征提取与分类。尽管传感器加权互信息（SWMI）能捕获空间上下文，但有效利用时序信息仍是挑战。本文通过将活动聚类为早、午、晚三时段，并将其编码至特征加权方法中，计算不同的互信息矩阵。进一步，将一天中的时刻和星期几作为循环时间特征加入特征向量，并增加用户位置追踪特征。实验表明，在四个真实数据集中的三个上，本方法在准确率和F1分数上优于现有最先进方法，尤其在低数据条件下提升最为显著。结果凸显了该方法在构建有效智能家庭解决方案以支持老年人居家养老方面的潜力。
原文摘要 · Abstract (English)
With the global population ageing, it is crucial to enable individuals to live independently and safely in their homes. Using ubiquitous sensors such as Passive InfraRed sensors (PIR) and door sensors is drawing increasing interest for monitoring daily activities and facilitating preventative healthcare interventions for the elderly. Human Activity Recognition (HAR) from passive sensors mostly relies on traditional machine learning and includes data segmentation, feature extraction, and classification. While techniques like Sensor Weighting Mutual Information (SWMI) capture spatial context in a feature vector, effectively leveraging temporal information remains a challenge. We tackle this by clustering activities into morning, afternoon, and night, and encoding them into the feature weighting method calculating distinct mutual information matrices. We further propose to extend the feature vector by incorporating time of day and day of week as cyclical temporal features, as well as adding a feature to track the user's location. The experiments show improved accuracy and F1-score over existing state-of-the-art methods in three out of four real-world datasets, with highest gains in a low-data regime. These results highlight the potential of our approach for developing effective smart home solutions to support ageing in place.
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