Name
Title | ||
|---|---|---|
Rehab-Net: Deep Learning framework for Arm Movement Classification using Wearable Sensors for Stroke Rehabilitation. |
Abstract | ||
|---|---|---|
In this paper, we present a deep learning framework “Rehab-Net” for effectively classifying three upper limb movements of the human arm, involving extension, flexion, and rotation of the forearm, which, over the time, could provide a measure of rehabilitation progress. The proposed framework, Rehab-Net is formulated with a personalized, light weight and low-complex, customized convolutional neural... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TBME.2019.2899927 | IEEE Transactions on Biomedical Engineering |
Keywords | Field | DocType |
Task analysis,Stroke (medical condition),Monitoring,Accelerometers,Feature extraction,Wrist,Deep learning | Computer vision,Accelerometer,Convolutional neural network,Computer science,Wearable computer,Support vector machine,Feature extraction,Artificial intelligence,Linear discriminant analysis,Deep learning,Cluster analysis | Journal |
Volume | Issue | ISSN |
66 | 11 | 0018-9294 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Madhuri Panwar | 1 | 7 | 3.23 |
| Dwaipayan Biswas | 2 | 67 | 10.49 |
| Harsh Bajaj | 3 | 1 | 0.36 |
| Michael Jobges | 4 | 3 | 1.46 |
| Ruth Turk | 5 | 1 | 0.36 |
| Koushik Maharatna | 6 | 267 | 32.33 |
| Amit Acharyya | 7 | 139 | 31.20 |