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Monrada Sirimongkol.. Abnormal gait pattern recognition of stroke patient in initial stage using smartphone and hybrid classification methods. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2565.
Abnormal gait pattern recognition of stroke patient in initial stage using smartphone and hybrid classification methods
Name: Orasa Patsadu.
Organization :
Rajamangala University of Technology Krungthep. Faculty of Science and Technology
Email :
orasa.p@mail.rmutk.ac.th
Name: Kittipat Thepmee.
Organization :
Rajamangala University of Technology Krungthep. Faculty of Science and Technology
Name: Kantapong Phuengliam.
Organization :
Rajamangala University of Technology Krungthep. Faculty of Science and Technology
Name: Monrada Sirimongkol.
Organization :
Rajamangala University of Technology Krungthep. Faculty of Science and Technology
keyword:
Hybrid classification methods.
LCSH:
Gait disorders.
Abstract:
This paper proposes abnormal gait pattern recognition of Stroke patient in the initial stage using smartphone and hybrid classification methods. Our proposed research is divided into 2 parts: model building for abnormal gait pattern detection and system development. For a model of abnormal gait pattern detection, sensors (accelerometer and gyroscope sensor) on a smartphone are used to collect accelerometer and gyroscope data of gait pattern. Then, data of gait pattern are transformed and selected appropriated attributes to build a model using hybrid classification methods (Multilayer Perceptron, Decision Tree, and Support Vector Machine). From our experiments, the result shows that our proposed method achieved high accuracy of 99.40 percent for abnormal gait pattern recognition of Stroke patient in the initial stage and compared with previous research using accelerometer and gyroscope. Finally, our research developed a gait stroke detection system for abnormal gait pattern detection. When the system detects abnormal gait pattern, the system will send notification to caregiver and physician to reduce the risk of fall and accidence, especially, in the case of patient living alone at home. In addition, the system can help the physician to diagnose and train the gait pattern of post-stroke patients for rehabilitation.""
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2565
Modified:
2024-09-03
Issued:
2023-02-12
บทความ/Article
application/pdf
BibliograpyCitation :
วารสารเทคโนโลยีสารสนเทศ. ปีที่ 18, ฉบับที่ 2 (ก.ค.-ธ.ค. 65), หน้า 21-33.
eng
©copyrights King Mongkut's University of Technology North Bangkok
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ใช้เวลา
0.036009 วินาที
Orasa Patsadu.
| Title | Contributor | Type |
|---|---|---|
| The development of game to develop the cognitive skill for autistic children via virtual reality
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Orasa Patsadu.;Yanee Muchchimwong.;Nattaburud Narudkun. | บทความ/Article | |
| Abnormal gait pattern recognition of stroke patient in initial stage using smartphone and hybrid classification methods
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Orasa Patsadu.;Kittipat Thepmee.;Kantapong Phuengliam.;Monrada Sirimongkol. | บทความ/Article | |
| A comparison of classification methods of hypothyroid disease prediction
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Kulchaya Pongsawaeng.;Ausron Binmaduereh.;Panuphong Jenrotphondet.;Orasa Patsadu. | บทความ/Article |
Kittipat Thepmee.
| Title | Contributor | Type |
|---|---|---|
| Abnormal gait pattern recognition of stroke patient in initial stage using smartphone and hybrid classification methods
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Orasa Patsadu.;Kittipat Thepmee.;Kantapong Phuengliam.;Monrada Sirimongkol. | บทความ/Article |
Kantapong Phuengliam.
| Title | Contributor | Type |
|---|---|---|
| Abnormal gait pattern recognition of stroke patient in initial stage using smartphone and hybrid classification methods
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Orasa Patsadu.;Kittipat Thepmee.;Kantapong Phuengliam.;Monrada Sirimongkol. | บทความ/Article |
Monrada Sirimongkol.
| Title | Contributor | Type |
|---|---|---|
| Abnormal gait pattern recognition of stroke patient in initial stage using smartphone and hybrid classification methods
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Orasa Patsadu.;Kittipat Thepmee.;Kantapong Phuengliam.;Monrada Sirimongkol. | บทความ/Article |