Background E-scooter injuries have risen in recent years and riders report a relatively high prevalence of accidents. Collisions with motor vehicles pose a high risk to e-scooter users. E-scooter riders move fast relative to runners but lack movement of limbs that present aspects of biological motion to drivers, which may diminish conspicuity. Method Two experiments measured participants’ detection of point light representations beneath masking visual noise. Study 1 presented a runner, e-scooter rider, and rectangular object. Study 2 modified the e-scooter stimuli to remove motion sway and added alternative e-scooter presentations, one with moving lights consistent with biological motion and the other with the same motion in reverse, inconsistent with biological motion. Results Study 1 found a main effect of figure type, with the runner resulting in superior detection, recognition, and response time compared to the e-scooter rider, which performed better than the object. Study 2 found better perception performance for the runner, including better detection compared to the reverse motion e-scooter. Conclusion Findings suggest that reduced biological motion produced by e-scooter users slightly worsens and slows their detection by other road users and indicates an advantage for the perception of human body configurations. Any inclusion of apparent motion to improve detection, especially near the ground, should be consistent with biological motion. Application Visual display alterations (e.g., lighting) to introduce apparent motion that mimics biological movements or is consistent with biological motion may potentially confer a detection advantage over other movement patterns.
Examining Biological Motion as a Potential Factor in E-Scooter Conspicuity and Safety
Marshall L. Mabry,Curtis M. Craig,P. Easterlund,Nichole L. Morris
Published 2025 in Hum. Factors
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- Publication year
2025
- Venue
Hum. Factors
- Publication date
2025-05-22
- Fields of study
Biology, Medicine, Computer Science, Environmental Science
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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