Name
Abstract | ||
|---|---|---|
Automatically estimating a person's energy expenditure has numerous uses, including ensuring sufficient daily activity by an elderly live-alone person, such activity shown to have numerous benefits. Most previous work requires a person to wear a sensor device. We introduce a video-based activity level estimation technique to take advantage of increasingly-common in-home camera systems. We consider several features of a motion bounding rectangle for such estimation, including changes in height and width, and vertical and horizontal velocities and accelerations. Experiments involved 36 recordings of normal household activity, such as reading while seated, sweeping, and light exercising, involving 4 different actors. Results show, somewhat surprisingly, that the feature horizontal acceleration leads to an activity level estimation fidelity of 0.994 correlation with a commercial BodyBugg body-worn energy measurement device. Furthermore, the approach yielded 90.9% average accuracy of energy expenditure. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICHI.2013.28 | ICHI |
Keywords | Field | DocType |
geriatrics,patient monitoring,video signal processing,BodyBugg body-worn energy measurement device,assistive monitoring,daily energy expenditure,elderly live-alone person,sensor device,video,Patient monitoring,assistive monitoring,embedded systems,energy expenditure,smart homes,telehealth,ubiquitous systems,video processing | Minimum bounding rectangle,Fidelity,Video processing,Horizontal and vertical,Simulation,Remote patient monitoring,Computer science,Energy expenditure,Real-time computing,Acceleration,Telehealth | Conference |
Citations | PageRank | References |
4 | 0.43 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alex Edgcomb | 1 | 37 | 6.13 |
| Frank Vahid | 2 | 2688 | 218.00 |