The field of sensor-based human activity recognition (HAR) mainly uses posture, motion and context data of Inertial Measurement Units (IMUs) to identify daily activities. Despite the advancements in learning-based methods, it is challenging to perform information fusion from the temporal perspective due to the complexities in fusing heterogeneous sensor data and establishing long-term context correlations. This paper proposes a novel triple spectral fusion framework tailored for HAR. First, we develop an adaptive complementary filtering technique for noise suppression and organize each IMU's sensors into posture and motion modality nodes. Given that IMU nodes form a dynamic heterogeneous graph, we then apply adaptive filtering within the graph Fourier domain to merge both homogeneous and heterogeneous node information. Furthermore, an adaptive wavelet frequency selection approach is implemented to suppress context redundancy and shorten the length of features. This approach enhances both timestamp-based graph aggregation and the correlation of long-term contexts. Our framework uses adaptive filtering in the Fourier, graph Fourier, and wavelet domains, enabling effective multi-sensor fusion and context correlation. Extensive experiments on ten benchmark datasets demonstrate the superior performance of our framework. Project page: https://github.com/crocodilegogogo/TSF-TPAMI2026.

翻译：基于传感器的人体活动识别（HAR）领域主要利用惯性测量单元（IMU）的姿态、运动和上下文数据来识别日常活动。尽管基于学习的方法取得了进展，但由于异构传感器数据融合以及建立长期上下文关联的复杂性，从时间视角进行信息融合仍然具有挑战性。本文提出了一种专为HAR设计的新型三重频谱融合框架。首先，我们开发了一种自适应互补滤波技术用于噪声抑制，并将每个IMU的传感器组织成姿态和运动模态节点。鉴于IMU节点构成动态异构图，我们随后在图傅里叶域中应用自适应滤波，以合并同构和异构节点信息。此外，我们实现了一种自适应小波频率选择方法，以抑制上下文冗余并缩短特征长度。该方法增强了基于时间戳的图聚合以及长期上下文的关联性。我们的框架在傅里叶、图傅里叶和小波域中使用自适应滤波，实现了有效的多传感器融合和上下文关联。在十个基准数据集上的广泛实验证明了我们框架的优越性能。项目页面：https://github.com/crocodilegogogo/TSF-TPAMI2026。