Recognizing Skateboard and Kickboard Commuting Behaviors Using Activity Trackers: Feasibility Study Using Machine Learning Approaches.

Background: Active commuting, such as skateboarding and kickboarding, is gaining popularity as an alternative to traditional modes of transportation such as walking and cycling. However, current activity trackers and smartphones, which rely on accelerometer data, are primarily designed to recognize symmetrical locomotive activities (eg, walking and running) and may struggle to accurately identify the unique push-push-glide motion patterns of skateboarding and kickboarding.

Objective: The primary objective of this study was to evaluate the feasibility of classifying skateboard and kickboard commuting behaviors using data from wearable sensors and smartphones. A secondary objective was to identify the most important sensor-derived features for accurate activity recognition.

Methods: Ten participants (4 women and 6 men; aged 12-55 y) performed 9 activities, including skateboarding, kickboarding, walking, running, bicycling, ascending stairs, descending stairs, sitting, and standing. Data were collected using wearable sensors (accelerometer, gyroscope, and barometer) placed on the wrist and the hip, as well as in the pocket to replicate the sensing characteristics of commercial activity trackers and smartphones. The signal processing approach included the extraction of 211 features from 10- and 20-second sliding windows. Random forest classifiers were trained to perform multiclass and binary classifications, including distinguishing skateboarding and kickboarding from other activities.

Results: Wrist-worn sensor configurations achieved the highest balanced accuracies for multiclass classification (range 84%-88%). Skateboarding and kickboarding were identified with high sensitivity, ranging from 93% to 99% and 97% to 99%, respectively. Hip and pocket sensor configurations showed lower performance, particularly in distinguishing skateboarding (range 49%-58% sensitivity) from kickboarding (78% sensitivity). Binary classification models grouping skateboarding and kickboarding into a push-push-glide superclass achieved high accuracies (range 91%-95%). Key features for classification included low- and high-frequency accelerometer signals, as well as roll-pitch-yaw angles.

Conclusions: This study demonstrates the feasibility of recognizing skateboard and kickboard commuting behaviors using wearable sensors, particularly wrist-worn devices. While hip and pocket sensors showed limitations in differentiating these activities, the broader push-push-glide classification achieved acceptable accuracy, suggesting its potential for integration into activity tracker software. Future research should explore sensor fusion approaches to further enhance recognition performance and address the question of energy expenditure estimation.