Name
Abstract | ||
|---|---|---|
Wearable devices have paved the way for several contextaware applications in the field of health-care, sports and entertainment to improve the well-being of users. During rehabilitation patients need accurate feedback on their physiotherapy and preferably in near real-time. This feedback to users can empower and improve the speed of recovery. We present here a system that analyzes activities of patients to provide real-time feedback. Specifically, we analyze exercises performed during knee rehabilitation where patients undergoing therapy often have to visit doctors for feedback. Moreover, they receive little or no feedback when performing these exercises away from the clinic. To overcome this, we propose a novel two-stage methodology that provides accurate feedback on the exercises performed. We collected data from six patients during their rehabilitation to evaluate our models. Furthermore, the proposed technique can be applied in wide-variety of exercises and also in sports. |
Year | Venue | Field |
|---|---|---|
2017 | EWSN | Rehabilitation,Activity recognition,Simulation,Wearable computer,Computer science,Real-time computing,Human–computer interaction,Wearable technology |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akshay Uttama Nambi S.n | 1 | 68 | 10.00 |
| Luis Gonzalez | 2 | 0 | 0.34 |
| R. Venkatesha Prasad | 3 | 649 | 77.98 |