Work

mORAL

Year: 2019

Type: article

Abstract: We address the open problem of reliably detecting oral health behaviors passively from wrist-worn inertial sensors. We present our model named mORAL (pronounced em oral) for detecting brushing and flossing behaviors, without the use of instrumented toothbrushes so that the model is applicable to brushing with still prevalent manual toothbrushes. We show that for detecting rare daily events such as toothbrushing, adopting a model that is based on identifying candidate windows based on events, rather than fixed-length timeblocks, leads to significantly higher performance. Trained and tested on 2,797 hours of sensor data collected over 192 days on 25 participants (using video annotations for ground truth labels), our brushing model achieves 100% median recall with a false positive rate of one event in every nine days of sensor wearing. The average error in estimating the start/end times of the detected event is 4.1% of the interval of the actual toothbrushing event.

Source: Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

DOI: 10.1145/3314388

Authors: Sayma Akther, Nazir Saleheen, Shahin Alan Samiei, Vivek Shetty, Emre Ertin, Santosh Kumar

Institutions: University of Memphis, University of California, Los Angeles, The Ohio State University

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid