This paper proposes an atomic behaviour intervention strategy using Pavlok device. Pavlok utilises beeps, vibration and shocks as a mode of aversion technique to help individuals with behaviour modification. While the device can be useful in certain periodic daily life situations, like alarms and exercise notifications, the device relies on manual operations that limit its usage. To automate behaviour modification, we propose a framework that first detects targeted behaviours through a lightweight deep learning model and subsequently nudges the user through Pavlok. Our proposed solution is implemented and verified in the context of snoring, which captures audio from the environment following a prediction of whether the audio content is a snore or not using a 1D convolutional neural network. Based on the prediction, we use Pavlok to nudge users for preventive measures, such as a change in sleeping posture. We believe that this simple solution can help people change their atomic habits, which may lead to long-term health benefits. Our proposed real-time, lightweight model (99.8% fewer parameters over SOTA; 1,278,049 --> 1337) achieves SOTA performance (test accuracy of 0.99) on a public benchmark. The code and model are publicly available atthis https URL.
View on arXiv@article{hasan2025_2305.06110,
title={ Pavlok-Nudge: A Feedback Mechanism for Atomic Behaviour Modification with Snoring Usecase },
author={ Md Rakibul Hasan and Shreya Ghosh and Pradyumna Agrawal and Zhixi Cai and Abhinav Dhall and Tom Gedeon },
journal={arXiv preprint arXiv:2305.06110},
year={ 2025 }
}