Si Zuo

@inproceedings{10.1145/3594739.3610797,
  author = {Zuo, Si and Fortes, Vitor and Suh, Sungho and Sigg, Stephan and Lukowicz, Paul},
  title = {Unsupervised Diffusion Model for Sensor-based Human Activity Recognition},
  year = {2023},
  isbn = {9798400702006},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3594739.3610797},
  doi = {10.1145/3594739.3610797},
  abstract = {Recognizing human activities from sensor data is a vital task in various domains, but obtaining diverse and labeled sensor data remains challenging and costly. In this paper, we propose an unsupervised statistical feature-guided diffusion model for sensor-based human activity recognition. The proposed method aims to generate synthetic time-series sensor data without relying on labeled data, addressing the scarcity and annotation difficulties associated with real-world sensor data. By conditioning the diffusion model on statistical information such as mean, standard deviation, Z-score, and skewness, we generate diverse and representative synthetic sensor data. We conducted experiments on public human activity recognition datasets and compared the proposed method to conventional oversampling methods and state-of-the-art generative adversarial network methods. The experimental results demonstrate that the proposed method can improve the performance of human activity recognition and outperform existing techniques.},
  booktitle = {Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing \& the 2023 ACM International Symposium on Wearable Computing},
  pages = {205},
  numpages = {1},
  keywords = {Human activity recognition, Sensor data generation, Statistical feature-guided diffusion model, Unsupervised learning},
  location = {Cancun, Quintana Roo, Mexico},
  series = {UbiComp/ISWC '23 Adjunct}
  }

Recognizing human activities from sensor data is a vital task in various domains, but obtaining diverse and labeled sensor data remains challenging and costly. In this paper, we propose an unsupervised statistical feature-guided diffusion model for sensor-based human activity recognition. The proposed method aims to generate synthetic time-series sensor data without relying on labeled data, addressing the scarcity and annotation difficulties associated with real-world sensor data. By conditioning the diffusion model on statistical information such as mean, standard deviation, Z-score, and skewness, we generate diverse and representative synthetic sensor data. We conducted experiments on public human activity recognition datasets and compared the proposed method to conventional oversampling methods and state-of-the-art generative adversarial network methods. The experimental results demonstrate that the proposed method can improve the performance of human activity recognition and outperform existing techniques.

@inproceedings{Beck2020BCGECG,
  title={BCG and ECG-based secure communication for medical devices in Body Area Networks},
  author={Nils Beck and Si Zuo and Stephan Sigg},
  booktitle={The 19th International Conference on Pervasive Computing and Communications (PerCom 2021), adjunct},
  year={2021},
  abstract={An increasing amount of medical devices, such as pace makers or insulin pumps, is able to communicate in wireless Body Area Networks (BANs). While this facilitates interaction between users and medical devices, something that was previously more complicated or - in the case of implanted devices - often impossible, it also raises security and privacy questions. We exploit the wide availability of ballistocardiographs (BCG) and electocardiographs (ECG) in consumer wearables and propose MEDISCOM, an ad-hoc, implicit and secure communication protocol for medical devices in local BANs. Deriving common secret keys from a body’s BCG or ECG signal. MEDISCOM ensures confidentiality and integrity of sensitive medical data and also continuously authenticates devices, requiring no explicit user interaction and maintaining a low computational overhead. We consider relevant attack vectors and show how MEDISCOM is resilient towards them. Furthermore, we validate the security of the secret keys that our protocol derives on BCG and ECG data from 29 subjects.
},
  group = {ambience},
  project = {abacus}
}

An increasing amount of medical devices, such as pace makers or insulin pumps, is able to communicate in wireless Body Area Networks (BANs). While this facilitates interaction between users and medical devices, something that was previously more complicated or - in the case of implanted devices - often impossible, it also raises security and privacy questions. We exploit the wide availability of ballistocardiographs (BCG) and electocardiographs (ECG) in consumer wearables and propose MEDISCOM, an ad-hoc, implicit and secure communication protocol for medical devices in local BANs. Deriving common secret keys from a body’s BCG or ECG signal. MEDISCOM ensures confidentiality and integrity of sensitive medical data and also continuously authenticates devices, requiring no explicit user interaction and maintaining a low computational overhead. We consider relevant attack vectors and show how MEDISCOM is resilient towards them. Furthermore, we validate the security of the secret keys that our protocol derives on BCG and ECG data from 29 subjects.