Roy, AvirupDutta, HrishikeshGriffith, HenryBiswas, Subir2022-04-112022-04-112022-03-25Sensors 22 (7): 2514 (2022)https://hdl.handle.net/20.500.12588/819A lightweight on-device liquid consumption estimation system involving an energy-aware machine learning algorithm is developed in this work. This system consists of two separate on-device neural network models that carry out liquid consumption estimation with the result of two tasks: the detection of sip from gestures with which the bottle is handled by its user and the detection of first sips after a bottle refill. This predictive volume estimation framework incorporates a self-correction mechanism that can minimize the error after each bottle fill-up cycle, which makes the system robust to errors from the sip classification module. In this paper, a detailed characterization of sip detection is performed to understand the accuracy-complexity tradeoffs by developing and implementing a variety of different ML models with varying complexities. The maximum energy consumed by the entire framework is around 119 mJ during a maximum computation time of 300 µs. The energy consumption and computation times of the proposed framework is suitable for implementation in low-power embedded hardware that can be incorporated in consumer grade water bottles.Attribution 4.0 United Stateshttps://creativecommons.org/licenses/by/4.0/drink volume estimationsip detectionembedded machine learningon-device classificationneural networksTinyMLArticle2022-04-11