Automatic physical activity and in‐vehicle status classification based on GPS and accelerometer data: A hierarchical classification approach using machine learning techniques

Abstract

Due to the advancement of tracking technology, a large quantity of movement data has been collected and analyzed in various research domains. In human mobility and physical activity (PA) research, GPS trajectories and the capabilities of geographic information systems (GIS) facilitate a better understanding of the associations between PA and various environmental factors taking individuals' daily travels into account. PA research, however, needs to widen its focus from the intensity of PA to types of PA, which may provide useful clues for understanding specific health behaviors in particular geographic contexts. This study proposes and develops an algorithm to automatically classify PA types and in-vehicle status using GPS and accelerometer data. Walking, standing, jogging, biking and sedentary/in-vehicle statuses are identified through hierarchical classification processes based on machine learning and geospatial techniques. The proposed algorithm achieved high predictive accuracy on real-world GPS and accelerometer data. It can greatly reduce participants' and researchers' burdens by automatically identifying PA types and in-vehicle status for human mobility research, which is also known as travel mode imputation in transportation research. | 1523 LEE and KWAN

Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection

“…These links enhance our contextual knowledge of the relationship between objectively measured PA and physical and social environments [17][18][19][20][21]. The second application uses features such as time, distance, altitude and speed derived from GPS data to inform classifiers in PA detection [5,[22][23][24][25]. However, few studies in the PA domain attempted to assess the potential benefit of using GPS data as additional input to PA type detection.…”

The Effects of GPS-Based Buffer Size on the Association between Travel Modes and Environmental Contexts

“…Because GPS points at 5-s intervals are too numerous to process for buffer analysis, we decided to reduce the number of points by half by sampling them at a 10-s interval. Before data analysis, Kalman filtering [28], based on linear quadratic estimation, was performed to increase the accuracy the GPS data. Further, short trips with less than a 3-min duration were excluded because GPS tracks that seem like short trips of under 3 min are most likely not real trips (e.g., due to drifting GPS points) and can be wrongly identified as trips.…”

“…To obtain the travel modes of the trips recorded in the survey, the travel mode classification algorithm developed by Lee and Kwan [28] was adopted. The algorithm identifies travel modes like walking and biking using machine learning.…”

“…The overall predictive accuracy of the optimized classification algorithm was 97.00% (walking: 97.58%, in-vehicle: 97.24%, biking: 89.33%, and running: 99.01% in Figure 2) with 10 to 300 s time windows and 10 to 500 m distance windows when the Geolife GPS data described in Section 3.1 in [28] were used as training and test datasets. When 10 to 180 s time windows and 10 to 200 m distance windows-which were the minimum ranges of window sizes in the test-were used, the algorithm achieved the lowest predictive accuracy (95.04%).…”

“…When 10 to 180 s time windows and 10 to 200 m distance windows-which were the minimum ranges of window sizes in the test-were used, the algorithm achieved the lowest predictive accuracy (95.04%). Next, the optimized classification algorithm was also tested using real-world GPS trajectories collected from three subjects described in Lee and Kwan [28]. The three subjects were young adults and all males with no health issues.…”