Pavlok-Nudge: A Feedback Mechanism for Atomic Behaviour Modification with Snoring Usecase
This provides an automated solution for individuals with snoring issues to potentially improve health through behavior change, but it is incremental as it applies existing methods to a new use case.
The paper tackles the problem of automating behavior modification by detecting snoring with a lightweight deep learning model and using a wearable device to nudge users, achieving state-of-the-art test accuracy of 0.99 with 99.8% fewer parameters than prior methods.
This paper proposes an atomic behaviour intervention strategy using the Pavlok wearable 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, it 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. 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 lightweight 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 lightweight model (99.8% fewer parameters over SOTA; 790,273$\rightarrow$1,337) achieves SOTA test accuracy of 0.99 on a public benchmark. The code and model are publicly available at https://github.com/hasan-rakibul/pavlok-nudge-snore.