Measuring Risky Driving Behavior Using an mHealth Smartphone App: Development and Evaluation of gForce

• Main Authors: Freidlin, Raisa Z (Author), Dave, Amisha D (Author), Espey, Benjamin G (Author), Stanley, Sean T (Author), Garmendia, Marcial A (Author), Pursley, Randall (Author), Ehsani, Johnathon P (Author), Simons-Morton, Bruce G (Author), Pohida, Thomas J (Author)
• Format: Book
• Published: JMIR Publications, 2018-04-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2291-5222; 10.2196/mhealth.9290
• Keywords: Information technology; T58.5-58.64; Public aspects of medicine; RA1-1270
• Source: JMIR mHealth and uHealth, Vol 6, Iss 4, p e69 (2018)

BackgroundNaturalistic driving studies, designed to objectively assess driving behavior and outcomes, are conducted by equipping vehicles with dedicated instrumentation (eg, accelerometers, gyroscopes, Global Positioning System, and cameras) that provide continuous recording of acceleration, location, videos, and still images for eventual retrieval and analyses. However, this research is limited by several factors: the cost of equipment installation; management and storage of the large amounts of data collected; and data reduction, coding, and analyses. Modern smartphone technology includes accelerometers built into phones, and the vast, global proliferation of smartphones could provide a possible low-cost alternative for assessing kinematic risky driving. ObjectiveWe evaluated an in-house developed iPhone app (gForce) for detecting elevated g-force events by comparing the iPhone linear acceleration measurements with corresponding acceleration measurements obtained with both a custom Android app and the in-vehicle miniDAS data acquisition system (DAS; Virginia Tech Transportation Institute). MethodsThe iPhone and Android devices were dashboard-mounted in a vehicle equipped with the DAS instrumentation. The experimental protocol consisted of driving maneuvers on a test track, such as cornering, braking, and turning that were performed at different acceleration levels (ie, mild, moderate, or hard). The iPhone gForce app recorded linear acceleration (ie, gravity-corrected). The Android app recorded gravity-corrected and uncorrected acceleration measurements, and the DAS device recorded gravity-uncorrected acceleration measurements. Lateral and longitudinal acceleration measures were compared. ResultsThe correlation coefficients between the iPhone and DAS acceleration measurements were slightly lower compared to the correlation coefficients between the Android and DAS, possibly due to the gravity correction on the iPhone. Averaging the correlation coefficients for all maneuvers, the longitudinal and lateral acceleration measurements between iPhone and DAS were rlng=0.71 and rlat=0.83, respectively, while the corresponding acceleration measurements between Android and DAS were rlng=0.95 and rlat=0.97. The correlation coefficients between lateral accelerations on all three devices were higher than with the corresponding longitudinal accelerations for most maneuvers. ConclusionsThe gForce iPhone app reliably assessed elevated g-force events compared to the DAS. Collectively, the gForce app and iPhone platform have the potential to serve as feature-rich, inexpensive, scalable, and open-source tool for assessment of kinematic risky driving events, with potential for research and feedback forms of intervention.