Intake gestures and movement
The characteristic movement patterns during intake were first discussed in our 2005 paper on detection of eating and drinking gestures. The approach in most follow-up work has been to measure movements using inertial sensors attached to the wrist. Our work focused on developing spotting methods to identify different intake motion in continuous data streams from wearable sensors. The pattern spotting methods are based on hidden Markov models (HMM) and feature search strategies. The methods are generically applicable to different event detection and gesture spotting tasks in data streams. We further developed segmentation methods to leverage the intake-specific arm rotations in roll and pitch axes. The work on segmentation and spotting was published first in 2005 and incorporated in more elaborated form later. Results showed that different intake movements can be discriminated, including drinking, eating with fork and knife, with spoon, and using hands only. Subsequently, we investigated self-adaptation and sparse coding techniques to personalise and maximise intake movement spotting performance.
Our investigations continued to explore magnetic proximity measurement methods between hand and chest/head to support gesture segmentation and identification of different drinking movements related to the fluid container used.
"Sparse Natural Gesture Spotting in Free Living to Monitor Drinking with Wrist-worn Inertial Sensors", Proceedings of the 2018 ACM International Symposium on Wearable Computers, ACM, 2018.,
"Self-Taught Learning for Activity Spotting in On-body Motion Sensor Data", Proceedings of the 15th Annual International Symposium on Wearable Computers (ISWC '11), IEEE, June 2011.,
"Adaptive Activity Spotting Based on Event Rates", SUTC 2010: Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, IEEE, 2010.,
"Towards Wearable Sensing Based Assessment of Fluid Intake", PerHealth 2010: Proceedings of the First IEEE PerCom Workshop on Pervasive Healthcare, IEEE, 2010.,
"On-body sensing solutions for automatic dietary monitoring", Pervasive Computing, IEEE, 2009.,
"Gesture spotting with body-worn inertial sensors to detect user activities", Pattern Recognition, Google Scholar, 2008.,
"Detection of eating and drinking arm gestures using inertial body-worn sensors", ISWC 2005: IEEE Proceedings of the Ninth International Symposium on Wearable Computers, IEEE Press, October 2005.,