Structure as Sensors is a new class of sensing systems we enable by our research. We use the structure (e.g., buildings) to acts as the physical sensor element, and use the structural responses (e.g., vibrational movements) to understand the details of physical events (e.g., persons moving in a building, or events around the building). This approach reduces the deployment difficulties of a sensing system but significantly increases the dimensionality of the problem space due to numerous influence factors that change the structural response (e.g., wave propagation in the building, building material, walking speed, occupant activity, operation of machinery, etc.). 

Using the structure as a sensor, we have a number of deployments measuring: 

Select papers:

• Elderly/children's gait monitor

  • Dong, Y., Iammarino, M., Liu, J., Codling, J., Fagert, J., Mirshekari, M., ... & Noh, H. (2023). Ambient Floor Vibration Sensing Advances Accessibility of Functional Gait Assessment for Children with Muscular Dystrophies.

  • Pan, S., Yu, T., Mirshekari, M., Fagert, J., Bonde, A., Mengshoel, O., Noh, H., & Zhang, P. (2017). FootprintID: Indoor Pedestrian Identification through Ambient Structural Vibration Sensing. Journal Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(3), 89:1-31.

  • Fagert, J., Mirshekari, M., Pan, S., Lowes, L., Iammarino, M., Zhang, P., & Noh, H. Y. (2021). Structure-and sampling-adaptive gait balance symmetry estimation using footstep-induced structural floor vibrations. Journal of Engineering Mechanics, 147(2), 04020151.

• Health Monitoring

  • Codling, J. R., Shulkin, J. D., Chang, Y. C., Zhang, J., Latapie, H., Noh, H. Y., ... & Dong, Y. (2024, October). FloHR: Ubiquitous Heart Rate Measurement using Indirect Floor Vibration Sensing. In Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (pp. 44-54).

  • Dong, Y., Wu, Y., Chang, Y. C., Aggarwal, J., Codling, J. R., Latapie, H., ... & Noh, H. Y. (2024). Context-aware crowd monitoring for sports games using crowd-induced floor vibrations. Data-Centric Engineering, 5, e25.

  • Ruiz, C., Pan, S., Bannis, A., Chang, M. P., Noh, H. Y., & Zhang, P. (2020, April). IDIoT: Towards Ubiquitous Identification of IoT Devices through Visual and Inertial Orientation Matching During Human Activity. In 2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI) (pp. 40-52). IEEE.

• Pig health monitoring

  • Dong, Y., Bonde, A., Codling, J. R., Bannis, A., Cao, J., Macon, A., ... & Noh, H. Y. (2023). PigSense: Structural vibration-based activity and health monitoring system for pigs. ACM Transactions on Sensor Networks, 20(1), 1-43.

  • Bonde, A., Codling, J. R., Naruethep, K., Dong, Y., Siripaktanakon, W., Ariyadech, S., ... & Zhang, P. (2021, May). PigNet: Failure-Tolerant Pig Activity Monitoring System Using Structural Vibration. In Proceedings of the 20th International Conference on Information Processing in Sensor Networks (co-located with CPS-IoT Week 2021) (pp. 328-340).

• Item identification and association in cashier-less supermarkets

  • Falcão, J., Ruiz, C., Pan, S., Noh, H. Y., & Zhang, P. (2020). FAIM: Vision and Weight Sensing Fusion Framework for Autonomous Inventory Monitoring in Convenience Stores. Frontiers in Built Environment, 175.

  • Ruiz, C., Falcao, J., Pan, S., Noh, H., & Zhang, P. (2019, November). AIM3S: Autonomous Inventory Monitoring through Multi-Modal Sensing for Cashier-Less Convenience Stores. In The 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys 2019).

Using mobile carrier as Sensors, we utilize the movement of sensors to increase the efficiency of measuring phenomenons that covers a wide area (e.g. cities). Although mobile sensors can allow for this greater coverage, their mobility can also create areas with no coverage and unbalanced coverage for the sensing goals.  This requires system coordination and collaboration in order to produce optimal outcomes. As part of this work,  we 1) actuate the sensors to optimize sensor placement, movement, and data gathering; and 2) fuse with physical models to generate missing data (e.g. physical model of air flow to model pollution).

Using the mobile carrier as sensors, we developed and deployed a number of applications using a different hardware platforms :

Select papers:

• Taxi-fleet enabled air pollution monitoring

  • Chen, X., Xu, S., Liu, X., Xu, X., Noh, H. Y., Zhang, L., & Zhang, P. (2020). Adaptive hybrid model-enabled sensing system (hmss) for mobile fine-grained air pollution estimation. IEEE Transactions on Mobile Computing.

  • Chen, X., Wang, Y., He, J., Pan, S., Li, Y., & Zhang, P. (2019). CAP: Context-aware App Usage Prediction with Heterogeneous Graph Embedding. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 3(1), 4.

  • Xu, S., Chen, X., Pi, X., Joe-Wong, C., Zhang, P., & Noh, H. Y. (2019). iLOCuS: Incentivizing Vehicle Mobility to Optimize Sensing Distribution in Crowd Sensing. IEEE Transactions on Mobile Computing.

• Animal tracking

  • Zhang, P., Sadler, C. M., Lyon, S. A., & Martonosi, M. (2004, November). Hardware design experiences in ZebraNet. In Proceedings of the 2nd international conference on Embedded networked sensor systems (pp. 227-238). ACM. 

• Muscular activity and status tracking

  • Frank Mokaya, Roland Lucas, Hae Young Noh and Pei Zhang. Burnout: A Wearable System for Unobtrusive Skeletal Muscle Fatigue Estimation, In Proceedings of the 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN '16).

• Micro UAV swarms

  • Ruiz, C., Pan, S., Bannis, A., Chen, X., Joe-Wong, C., Noh, H., & Zhang, P. (2018). IDrone: Robust Drone Identification through Motion Actuation Feedback. Journal Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(2), 80:1-22.

  • Chen, X., Purohit, A., Dominguez, C. R., Carpin, S., & Zhang, P. (2015, November). DrunkWalk: Collaborative and Adaptive Planning for Navigation of Micro-Aerial Sensor Swarms. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems (pp. 295-308). ACM.

  • Purohit, A., Sun, Z., Mokaya, F., & Zhang, P. (2011, April). SensorFly: Controlled-mobile sensing platform for indoor emergency response applications. In Information Processing in Sensor Networks (IPSN), 2011 10th International Conference on (pp. 223-234). IEEE.