RisQ: Recognizing Smoking Gestures with Inertial Sensors on a Wristband
By Abhinav Parate, Meng-Chieh Chiu, Chaniel Chadowitz, Deepak Ganesan, Evangelos Kalogerakis. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2014
302 citations recorded by the source corpus.
Smoking-induced diseases are known to be the leading cause of death in the United States. In this work, we design RisQ, a mobile solution that leverages a wristband containing a 9-axis inertial measurement unit to capture changes in the orientation of a person's arm, and a machine learning pipeline that processes this data to accurately detect smoking gestures and sessions in real-time. Our key innovations are four-fold: a) an arm trajectory-based method that extracts candidate hand-to-mouth gestures, b) a set of trajectory-based features to distinguish smoking gestures from confounding gestures including eating and drinking, c) a probabilistic model that analyzes sequences of hand-to-mouth gestures and infers which gestures are part of individual smoking sessions, and d) a method that leverages multiple IMUs placed on a person's body together with 3D animation of a person's arm to reduce burden of self-reports for labeled data collection. Our experiments show that our gesture recognition algorithm can detect smoking gestures with high accuracy (95.7%), precision (91%) and recall (81%). We also report a user study that demonstrates that we can accurately detect the number of smoking sessions with very few false positives over the period of a day, and that we can reliably extract the beginning and end of smoking session periods.
Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/804a88adde8ce240404a49a9dfa7f1aa18d6d6f7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
Citations in the corpus
- A Survey of Wearable Devices and Challenges (2017, methodology): “[41] propose a machine learning algorithm to detect smoking gestures using a wrist-worn 9-axis IMU consisting of an accelerometer, a gyroscope and a magnetometer.”