Paper Info
Title
SyncWISE: Window Induced Shift Estimation for Synchronization ofVideo and Accelerometry from Wearable Sensors
Abstract
The development and validation of computational models to detect daily human behaviors (e.g., eating, smoking, brushing) using wearable devices requires labeled data collected from the natural field environment, with tight time synchronization of the micro-behaviors (e.g., start/end times of hand-to-mouth gestures during a smoking puff or an eating gesture) and the associated labels. Video data is increasingly being used for such label collection. Unfortunately, wearable devices and video cameras with independent (and drifting) clocks make tight time synchronization challenging. To address this issue, we present the Window Induced Shift Estimation method for Synchronization (SyncWISE) approach. We demonstrate the feasibility and effectiveness of our method by synchronizing the timestamps of a wearable camera and wearable accelerometer from 163 videos representing 45.2 hours of data from 21 participants enrolled in a real-world smoking cessation study. Our approach shows significant improvement over the state-of-the-art, even in the presence of high data loss, achieving 90% synchronization accuracy given a synchronization tolerance of 700 milliseconds. Our method also achieves state-of-the-art synchronization performance on the CMU-MMAC dataset.
Year
DOI
Venue
2020
10.1145/3411824
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Keywords
DocType
Volume
Accelerometry,Automatic Synchronization,Temporal Drift,Time Synchronization,Video,Wearable Camera,Wearable Sensor
Journal
4
Issue
ISSN
Citations 
3
2474-9567
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Yun Zhang101.01
Shibo Zhang296.00
Miao Liu302.70
Elyse Daly400.34
Samuel Battalio500.34
Santosh Kumar62048122.99
Bonnie Spring700.34
James M. Rehg85259474.66
Nabil Alshurafa913419.65