Name
Title | ||
|---|---|---|
A Feasible and Terrain-Insensitive Approach for Analyzing Power Wheelchair Users' Mobility |
Abstract | ||
|---|---|---|
Understanding a power wheelchair users mobility characteristics is critical because mobility is an important factor for social participation and quality of life of an individual. Although power wheelchairs can improve the mobility for people with disabilities, research has shown that power wheelchair users tend to live an inactive lifestyle. A sedentary lifestyle exposes wheelchair users to a greater risk of secondary health issues, such as cardiovascular diseases, obesity, diabetes, etc. Therefore, it is critical to assess wheelchair users mobility to ensure that they maintain an active lifestyle. However, existing health tracking applications are not suitable for power wheelchair users. They either require sensors to be installed on the wheels of a wheelchair (hence bringing installation and maintenance burdens) or are designed for able individuals by detecting the users steps, whose characteristics are significantly different from the dynamics of a power wheelchair. Furthermore, data captured by the inertial sensors (e.g., accelerometer or gyroscope) demonstrates a wide variety of patterns owing to different terrains on which the wheelchair travels. In this study, we propose to use the accelerometer in a smartphone for data collection, and employ mathematics and physics techniques to process and transform the raw data so that patterns intrinsic to wheelchair maneuvers are revealed. Based on the processed data, we developed a learning-based approach to analyze wheelchair users mobility by leveraging such patterns. We have conducted a sequence of experiments to evaluate the proposed approach. Experimental results showed that our approach correctly recognized all the bouts (segments of continuous movement), and achieved accurate measurements on bout maneuvering time and maximum period of continuous movement, which are critical indicators of a wheelchair users mobility. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICTAI.2017.00094 | 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI) |
Keywords | Field | DocType |
artificial neural network,bout,machine learning,mobility,power wheelchair | Wheelchair,Data collection,Gyroscope,Sedentary lifestyle,Computer science,Accelerometer,Terrain,Raw data,Human–computer interaction,Artificial intelligence,Inertial measurement unit,Machine learning | Conference |
ISSN | ISBN | Citations |
1082-3409 | 978-1-5386-3877-4 | 0 |
PageRank | References | Authors |
0.34 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fang Li | 1 | 21 | 13.89 |
| Marcus Eng Hock Ong | 2 | 0 | 0.68 |
| Yan Daniel Zhao | 3 | 0 | 0.34 |
| Gang Qian | 4 | 784 | 63.77 |
| Jicheng Fu | 5 | 74 | 15.92 |