1. |
- Abtahi Fahliani, Azra, et al.
(författare)
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DP-ACT: Decentralized Privacy-Preserving Asymmetric Digital Contact Tracing
- 2023
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Ingår i: The 24th Privacy Enhancing Technologies Symposium (PETS).
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Konferensbidrag (refereegranskat)abstract
- Digital contact tracing substantially improves the identification of high-risk contacts during pandemics. Despite several attempts to encourage people to use digital contact-tracing applications by developing and rolling out decentralized privacy-preserving protocols (broadcasting pseudo-random IDs over Bluetooth Low Energy-BLE), the adoption of digital contact tracing mobile applications has been limited, with privacy being one of the main concerns.In this paper, we propose a decentralized privacy-preserving contact tracing protocol, called DP-ACT, with both active and passive participants. Active participants broadcast BLE beacons with pseudo-random IDs, while passive participants model conservative users who do not broadcast BLE beacons but still listen to the broadcasted BLE beacons. We analyze the proposed protocol and discuss a set of interesting properties. The proposed protocol is evaluated using both a face-to-face individual interaction dataset and five real-world BLE datasets. Our simulation results demonstrate that the proposed DP-ACT protocol outperforms the state-of-the-art protocols in the presence of passive users.
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2. |
- Huang, Baichuan, et al.
(författare)
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EpilepsyNet: Interpretable Self-Supervised Seizure Detection for Low-Power Wearable Systems
- 2023
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Ingår i: IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS). - 9798350332674
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Konferensbidrag (refereegranskat)abstract
- Epilepsy is one of the most common neurological disorders that is characterized by recurrent and unpredictable seizures. Wearable systems can be used to detect the onset of a seizure and notify family members and emergency units for rescue. The majority of state-of-the-art studies in the epilepsy domain currently explore modern machine learning techniques, e.g., deep neural networks, to accurately detect epileptic seizures. However, training deep learning networks requires a large amount of data and computing resources, which is a major challenge for resource-constrained wearable systems. In this paper, we propose EpilepsyNet, the first interpretable self-supervised network tailored to resource-constrained devices without using any seizure data in its initial offline training. At runtime, however, once a seizure is detected, it can be incorporated into our self-supervised technique to improve seizure detection performance, without the need to retrain our learning model, hence incurring no energy overheads. Our self-supervised approach can reach a detection performance of 79.2%, which is on par with the state-of-the-art fully-supervised deep neural networks trained on seizure data. At the same time, our proposed approach can be deployed in resource-constrained wearable devices, reaching up to 1.3 days of battery life on a single charge.
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3. |
- Huang, Baichuan, et al.
(författare)
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Lightweight Machine Learning for Seizure Detection on Wearable Devices
- 2023
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Ingår i: ICASSP, the International Conference on Acoustics, Speech, and Signal Processing 2023.
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Konferensbidrag (refereegranskat)abstract
- For patients with epilepsy, automatic epilepsy monitoring, i.e., the process of direct observation of the patient’s health status in real time, is crucial. Wearable systems provide the possibility of real-time epilepsy monitoring and alerting caregivers upon the occurrence of a seizure. In the context of the ICASSP 2023 Seizure Detection Challenge, we pro- pose a lightweight machine-learning framework for real-time epilepsy monitoring on wearable devices. We evaluate our proposed framework on the SeizeIT2 dataset from the wear- able SensorDot (SD) of Byteflies. The experimental results show that our proposed framework achieves a sensitivity of 73.6% and a specificity of 96.7% in seizure detection.
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