Semi-Supervised Learning Using Sparsely Labelled Sip Events for Online Hydration Tracking Systems
Avirup Roy, Hrishikesh Dutta, Amit Kumar Bhuyan, Subir Kumar Biswas · 2023
This paper presents a lightweight on-device liquid consumption tracking system based on a semi-supervised learning paradigm. The online learning framework caters to scenarios where a hydration tracking bottle/device has no prior knowledge of a user's consumption gesture patterns. The proposed iterative semi-supervised learning (ISSL) framework uses sparsely labelled user gesture events acquired from the IMU sensors installed on a bottle, such that it can learn to differentiate between sip and non-sip gestures by specific individuals. Two different strategies, namely, population-based, and distance-based, are employed to achieve the desired clustering performance. A comparative study between these strategies has been presented in terms of clustering accuracies for classifying sip and non-sip gestures. The proposed architecture is shown to be lightweight in terms of computation complexity and memory usage of the bottle-embedded hardware. The trade-off between classification accuracy and computation complexity is analyzed for different algorithmic hyper-parameters and it is shown how to manage this trade-off. Extensive experimentation and simulation study has been conducted for multiple users' drinking patterns to validate the proposed learning paradigm.