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

Abstract

Background: Naturalistic 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.

Objective: We 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).

Methods: The 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.

Results: The 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 r_lng=0.71 and r_lat=0.83, respectively, while the corresponding acceleration measurements between Android and DAS were r_lng=0.95 and r_lat=0.97. The correlation coefficients between lateral accelerations on all three devices were higher than with the corresponding longitudinal accelerations for most maneuvers.

Conclusions: The 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.

Keywords: elevated g-force; iPhone; kinematic risky driving behavior; lateral acceleration; longitudinal acceleration; naturalistic driving studies.

Figure 1

Smartphones used for the Virginia Tech Transportation Institute road test: iPhone devices are #2 and 3; Android devices are #1, 4, and 5; Virginia Tech Transportation Institute permanently vehicle-installed instrumentation #6.

Figure 2

Virginia Smart Road: a) roundabout used for cornering; b) straight portion of the road used for braking and acceleration; c) turnout lane used for turning left and right.

Figure 3

The street-driving phase of the test in Christiansburg, VA. Driving over speedbumps, potholes and U-turns was performed on the loop around New River Valley Mall (black box).

Figure 4

Comparison of gravity-corrected and uncorrected Android acceleration measurements with data acquisition system (DAS; gravity-uncorrected).

Figure 5

Comparison of gravity-corrected Android acceleration measurements with iPhone (inherently gravity-corrected).

Figure 6

The effects of in-vehicle weight distribution on baseline acceleration measurements. The acceleration measurements offset from the baseline varied (arrows) based on the number of passengers/weight distribution inside the vehicle. Comparison with the Android and iPhone devices within the black box is show in Figure 7. DAS: data acquisition system.

Figure 7

The effects of in-vehicle weight distribution on baseline acceleration measurements. While vehicle is stationary, the acceleration measurements should be zero. DAS: data acquisition system.

Figure 8

Acceleration measurements for hard left cornering maneuvers. The horizontal line along lateral acceleration represents the threshold of 0.45 g. Shaded stripes identify the approximate time of the cornering maneuvers. DAS: data acquisition system.

Figure 9

Acceleration measurements for hard braking maneuvers. The horizontal line along longitudinal acceleration represents the threshold of -0.45 g. Shaded stripes identify the approximate time of the braking maneuvers. DAS: data acquisition system.

Figure 10

Acceleration measurements for hard left turning maneuvers. The horizontal line along longitudinal and lateral accelerations represents thresholds of –0.45 g and 0.45 g, respectively. Shaded stripes identify the approximate time of the turning maneuvers. DAS: data acquisition system.

Figure 11

Acceleration measurements acquired during street driving in Christiansburg, VA. Highlighted are maneuvers that exceeded 0.45 g threshold: hard brake (1), hard right turn (2), hard left U-turn (3), and pothole (4). Details during first 3.4 minutes of driving within the black box are annotated in Figure 12. DAS: data acquisition system; UTC: Coordinated Universal Time.

Figure 12

Acceleration measurements acquired during first 3.4 minutes of street driving in Christiansburg, VA (black box in Figure 11): brake (1), acceleration (2), and right turn (3). DAS: data acquisition system; UTC: Coordinated Universal Time.