Physical​ and physiological information is essential for indoor human monitoring applications. In order to capture such information non-intrusively, we utilize/design sensors that can capture the vibration induced by people/body parts to infer a variance of activities (over different scales, both infrastructure and wearable) in the physical world. We explore new opportunities to utilize complementary information over different scales (on-body, room, city, etc.) to achieve accurate inference.

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Data acquisition quality directly affects the information representative and the model accuracy in applications of real-world deployed cyber-physical systems. We combine physical and data-driven knowledge to design metrics and methods to assess the signal and dataset quality for particular sensing tasks.

Papers:
Zhang, Yue, Abdias Tellez Benitez, Reza Ehsani, and Shijia Pan. "DaQual: Data Quality Assessment for Tree Trunk Relative Water Content Sensors in a Pomegranate Orchard." In Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems, pp. 1096-1101. 2022.

Hu, Zhizhang, Yue Zhang, and Shijia Pan. "Footstep-Induced Floor Vibration Dataset: Reusability and Transferability Analysis." In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, pp. 546-551. 2021.

Zhang, Yue, Zhizhang Hu, Susu Xu, and Shijia Pan. "AutoQual: task-oriented structural vibration sensing quality assessment leveraging co-located mobile sensing context." CCF Transactions on Pervasive Computing and Interaction 3 (2021): 378-396.

Yu, Tong, Yue Zhang, Zhizhang Hu, Susu Xu, and Shijia Pan. "Vibration-based indoor human sensing quality reinforcement via Thompson sampling." In Proceedings of the First International Workshop on Cyber-Physical-Human System Design and Implementation, pp. 33-38. 2021.

The heterogeneity of cyber-physical systems brings challenges and opportunities for various applications. Our goal is to explore new methods to combine the complementary characteristics of multiple sensing modalities for accurate fine-grained learning with limited (labeled) data.

Papers:
Zhang, Yue, Zhizhang Hu, Uri Berger, and Shijia Pan. "CMA: Cross-Modal Association Between Wearable and Structural Vibration Signal Segments for Indoor Occupant Sensing." In The 22nd International Conference on Information Processing in Sensor Networks, pp. 96-109. 2023.

Hu, Zhizhang, Yue Zhang, Tong Yu, and Shijia Pan. "VMA: Domain Variance-and Modality-Aware Model Transfer for Fine-Grained Occupant Activity Recognition." In 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), pp. 259-270. IEEE, 2022.

Pan, Shijia, Mario Berges, Juleen Rodakowski, Pei Zhang, and Hae Young Noh. "Fine-Grained Activity of Daily Living (ADL) Recognition Through Heterogeneous Sensing Systems With Complementary Spatiotemporal Characteristics." Frontiers in Built Environment 6 (2020): 560497.