The problem of human activity recognition from mobile sensor data applies to
multiple domains, such as health monitoring, personal fitness, daily life
logging, and senior care. A critical challenge for training human activity
recognition models is data quality. Acquiring balanced datasets containing
accurate activity labels requires humans to correctly annotate and potentially
interfere with the subjects' normal activities in real-time. Despite the
likelihood of incorrect annotation or lack thereof, there is often an inherent
chronology to human behavior. For example, we take a shower after we exercise.
This implicit chronology can be used to learn unknown labels and classify
future activities. In this work, we propose HAR-GCCN, a deep graph CNN model
that leverages the correlation between chronologically adjacent sensor
measurements to predict the correct labels for unclassified activities that
have at least one activity label. We propose a new training strategy enforcing
that the model predicts the missing activity labels by leveraging the known
ones. HAR-GCCN shows superior performance relative to previously used baseline
methods, improving classification accuracy by about 25% and up to 68% on
different datasets. Code is available at
https://github.com/abduallahmohamed/HAR-GCNN.

提出了一种基于深度图卷积神经网络的人体活动识别方法（HAR-GCCN），该方法利用传感器测量之间的时间相邻性来预测未分类活动的正确标签。通过新的训练策略和数据集，该方法在多个数据集上展现了比现有基线方法优越约 25％和高达 68％的分类准确性。