More Data for People with Disabilities! Comparing Data Collection Efforts for Wheelchair Transportation Mode Detection

Abstract

Transportation mode detection (TMD) for wheelchair users is essential for applications that facilitate enhancing accessibility and quality of life. Yet, the lack of extensive datasets from disabled individuals hinders the development of tailored TMD systems. Our study assesses two data collection methods in TMD for disability research: using non-wheelchair users to simulate wheelchair activities (Simulation Real IMU) and generating synthetic sensor data from videos (Virtual IMU). Results show that, when using a larger dataset and multiple sensor modalities, models trained on Simulation Real IMU perform better. However, models trained on both Simulation Real IMU and Virtual IMU exhibited similar performances when sensors were restricted to accelerometer and gyroscope only. This finding guides future researchers toward the use of Simulation Real IMU for comprehensive, multimodal sensor studies, provided they have sufficient budget and time. However, the more cost and time-efficient Virtual IMU can be a viable alternative in scenarios using basic sensors.