Name
Abstract | ||
|---|---|---|
The number of accidents involving elderly individuals has been increasing with the increase of the aging population, posing increasingly serious challenges. Most accidents are caused by reduced judgment and physical abilities, which lead to severe consequences. Therefore, studies on support systems for elderly and visually impaired people to improve the safety and quality of daily life are attracting considerable attention. In this study, a road surface condition distinction method using reflection intensities obtained by an ultrasonic sensor was proposed. The proposed method was applied to movement support systems for elderly and visually impaired individuals to detect dangerous road surfaces and give an alarm. The method did not perform well in previous studies of puddle detection, because the alert provided by the method did not enable users to avoid puddles. This study extended the method proposed by previous studies with respect to puddle detection ability. The findings indicate the effectiveness of the proposed method by considering four road surface conditions. The proposed method could detect puddle conditions. The effectiveness of the proposed method was verified in all four conditions, since users could differentiate between road surface conditions and classify the conditions as either safe or dangerous. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3390/s16101678 | SENSORS |
Keywords | Field | DocType |
surface condition distinction,ultrasonic sensor,reflection intensity,movement support system,accidents involving falls,road surface condition,puddle condition,elderly individuals,visually impaired individuals | Puddle,Ultrasonic sensor,Support system,ALARM,Transport engineering,Electronic engineering,Road surface,Artificial intelligence,Engineering,Accident prevention,Machine learning | Journal |
Volume | Issue | Citations |
16 | 10.0 | 0 |
PageRank | References | Authors |
0.34 | 1 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shota Nakashima | 1 | 54 | 11.05 |
| Shingo Aramaki | 2 | 1 | 1.05 |
| Yuhki Kitazono | 3 | 5 | 5.13 |
| Shenglin Mu | 4 | 45 | 6.74 |
| Kanya Tanaka | 5 | 18 | 12.75 |
| Seiichi Serikawa | 6 | 540 | 38.54 |