A Dynamic Partitioning Algorithm for Sip Detection using a Bottle-Attachable IMU Sensor

Hydration tracking technologies are a promising tool for improving health outcomes across a variety of populations. As a non-wearable solution that is reconfigurable across containers, bottle-attachable inertial measurement unit (IMU) sensors offer numerous advantages versus alternative tracking approaches. This paper proposes a novel dynamic temporal partitioning and classification algorithm for spotting drinks within the streaming data generated by such sensors. By exploiting the distinguishing characteristics of the container’s estimated inclination during drinking, the algorithm identifies candidate drink intervals for subsequent classification using a Threshold-Merge-Discard framework. The proposed approach is benchmarked against a slight variation of a previously introduced sliding window classifier for a series of experiments replicating the intended use case of the device. The new algorithm is shown to increase the true-positive detection rate by 23.7%, while reducing the number of required classification operations by more than an order of magnitude.

@article{Griffith2019,
  title     = {A Dynamic Partitioning Algorithm for Sip Detection using a Bottle-Attachable IMU Sensor},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {1},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Henry Griffith and Yan Shi and Subir Biswas},
  doi       = {10.14569/IJACSA.2019.0100101},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100101}
}