Name
Title | ||
|---|---|---|
Prediction of RSSI by Scikit-Learn for Improving Position Detecting System of Omnidirectional Wheelchair Tennis |
Abstract | ||
|---|---|---|
The wheelchair with good performance for the aged and disabled people is attracting attention from the society. Also, the wheelchair can provide the user with many benefits, such as maintaining mobility, continuing or broadening community social activities, conserving energy and enhancing quality of life. The wheelchair body must be compact enough and should be able to make different movements in order to have many applications. In our previous work, we presented the design and implementation of an omnidirectional wheelchair. In this paper, we propose a position detecting system for improving the performance of omnidirectional wheelchair tennis. This is achieved by predicting RSSI value using Scikit-learn. The proposed system can find correctly the wheelchair position for avoiding the collision. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-33506-9_66 | Lecture Notes in Networks and Systems |
Field | DocType | Volume |
Wheelchair,Omnidirectional antenna,Computer science,Collision,Human–computer interaction,Distributed computing | Conference | 97 |
ISSN | Citations | PageRank |
2367-3370 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keita Matsuo | 1 | 212 | 55.01 |
| Leonard Barolli | 2 | 1179 | 144.22 |