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Mendeley readers
| Title |
Hand contour detection in wearable camera video using an adaptive histogram region of interest
|
|---|---|
| Published in |
Journal of NeuroEngineering and Rehabilitation, December 2013
|
| DOI | 10.1186/1743-0003-10-114 |
| Pubmed ID | |
| Authors |
José Zariffa, Milos R Popovic |
| Abstract |
Monitoring hand function at home is needed to better evaluate the effectiveness of rehabilitation interventions. Our objective is to develop wearable computer vision systems for hand function monitoring. The specific aim of this study is to develop an algorithm that can identify hand contours in video from a wearable camera that records the user's point of view, without the need for markers. |
Mendeley readers
The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Colombia | 1 | 2% |
| Germany | 1 | 2% |
| Switzerland | 1 | 2% |
| Unknown | 41 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 25% |
| Researcher | 9 | 20% |
| Student > Doctoral Student | 3 | 7% |
| Other | 3 | 7% |
| Student > Bachelor | 3 | 7% |
| Other | 8 | 18% |
| Unknown | 7 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 16 | 36% |
| Medicine and Dentistry | 4 | 9% |
| Computer Science | 4 | 9% |
| Neuroscience | 4 | 9% |
| Nursing and Health Professions | 2 | 5% |
| Other | 6 | 14% |
| Unknown | 8 | 18% |