Next Article in Journal
Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network
Next Article in Special Issue
Measuring Spatial and Temporal PM2.5 Variations in Sacramento, California, Communities Using a Network of Low-Cost Sensors
Previous Article in Journal
Lock-in Amplifier-Based Impedance Detection of Tissue Type Using a Monopolar Injection Needle
Open AccessArticle

Identification of Bicycling Periods Using the MicroPEM Personal Exposure Monitor

1
RTI International, Research Triangle Park, NC 27709, USA
2
Mailman School of Public Health, Columbia University, New York, NY 10032, USA
3
Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(21), 4613; https://doi.org/10.3390/s19214613
Received: 23 September 2019 / Revised: 18 October 2019 / Accepted: 21 October 2019 / Published: 23 October 2019
(This article belongs to the Special Issue Sensors for Particulate Matter and Air Pollution)
Exposure assessment studies are the primary means for understanding links between exposure to chemical and physical agents and adverse health effects. Recently, researchers have proposed using wearable monitors during exposure assessment studies to obtain higher fidelity readings of exposures actually experienced by subjects. However, limited research has been conducted to link a wearer’s actions to periods of exposure, a necessary step for estimating inhaled dosage. To aid researchers in these settings, we developed a machine learning model for identifying periods of bicycling activity using passively collected data from the RTI MicroPEM wearable exposure monitor, a lightweight device capable of continuously sampling both air pollution levels and accelerometry parameters. Our best performing model identifies biking activity with a mean leave-one-session-out (LOSO) cross-validation F1 score of 0.832 (unweighted) and 0.979 (weighted). Accelerometer derived features contributed greatly to the model performance, as well as temporal smoothing of the predicted activities. Additionally, we found competitive activity recognition can occur with even relatively low sampling rates, suggesting suitability for exposure assessment studies where continuous data collection for long periods (without recharge) are needed to capture realistic daily routines and exposures. View Full-Text
Keywords: human activity recognition; machine learning; wearable sensors; exposure assessment; air pollution human activity recognition; machine learning; wearable sensors; exposure assessment; air pollution
Show Figures

Figure 1

MDPI and ACS Style

Chew, R.; Thornburg, J.; Jack, D.; Smith, C.; Yang, Q.; Chillrud, S. Identification of Bicycling Periods Using the MicroPEM Personal Exposure Monitor. Sensors 2019, 19, 4613.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop