Name
Abstract | ||
|---|---|---|
Objectively assessing gait function in lower limb rehabilitation remains a challenge in healthcare. This paper proposed the framework of AI gait analysis and assessment system eZiGait, which is based on seamless smart insoles. The preliminary study of activity recognition using eZiGait is presented. Walking data for five types of activities including slow walking, normal walking, fast walking, climbing upstairs, and walking down stairs have been investigated. Three classifiers were used, including artificial neural network (ANN), k-nearest neighbour (KNN) and random forest, to classify the five exercises. Results shows that a classification accuracy of 80% can be achieved with the ANN or 70% with KNN and randomforest. This demonstrates that simple features extracted from smart insoles can be used to classify different types of exercise. This provides for potential development of an AI gait analysis and assistant system to support lower limb rehabilitation at hospital, community or at home using state-of-the-art smart insoles and mobile technologies. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BIBM.2018.8621176 | PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) |
Keywords | Field | DocType |
Gait analysis, Gait assessment, smart insoles, lower limb rehabilitation, wearable sensors | Rehabilitation,Activity recognition,Gait,Computer science,Gait analysis,Artificial intelligence,Random forest,Artificial neural network,Climbing,Machine learning,Stairs | Conference |
ISSN | Citations | PageRank |
2156-1125 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Graham McCalmont | 1 | 0 | 0.34 |
| Philip J. Morrow | 2 | 384 | 53.29 |
| Huiru Zheng | 3 | 458 | 74.87 |
| Anas Samara | 4 | 13 | 2.29 |
| Sara Yasaei | 5 | 0 | 0.34 |
| Haiying Wang | 6 | 77 | 15.77 |
| Sally Mcclean | 7 | 1029 | 132.29 |