Quantification of User Driving Behavior in Motorized Mobility Scooters using CNN-based Head Pose Estimation Algorithm.

Abstract

There is a growing concern regarding the driving safety of Motorized Mobility Scooters (MMSs) for the elderly and mobility-impaired individuals. Although various research has made progress in sensor-based driving assistance systems to identify environmental hazards, few studies focus on investigating the impact of user behavior on MMS driving. In this paper, a driving status logging (DSL) system is developed to measure the user's behavior while driving. A cross-correlation analysis is implemented to quantify the temporal relationship between the head movement and steering operation in the driving of MMSs. The preliminary results suggest that the head movement can be used as an appropriate index to predict the intended steering operation in the driving of MMSs. Moreover, the quantified head-steering lag time provides the possibility to identify the hazardous driving pattern of MMS users.Clinical Relevance- The investigation of user behavior has the potential to improve the safety of MMSs. In this study, the user behavior in the driving of MMSs was quantitatively measured using the developed DSL system. Consequently, the temporal relationship between head movement and steering operation was first quantified in MMS-related research. These outcomes provide valuable insights into developing behavioral interventions to address the user's risky behavior patterns, thereby promoting the driving safety of MMSs.