Name
Abstract | ||
|---|---|---|
Nocturnal agitation is one of the symptoms exhibited by dementia patients. Diagnosing and monitoring the evolution of agitation is difficult because patient monitoring requires a doctor, nurse or caregiver observing patients for extended periods of time. In this work, we propose to use an automatic monitoring system based on wearable technology that complements the caregiverâs work. The proposed system uses a wrist wearable device to record agitation data, which are subsequently classified through machine learning techniques as quantifiable indexes of nocturnal agitation. Preliminary tests performed with volunteers showed that the classification of recorded movements between nocturnal agitation or quiet periods was successful in 78.86% of the cases. This proof of concept demonstrates the feasibility of using wearable technology to monitor nocturnal agitation. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.5220/0005938500630069 | PECCS |
Keywords | Field | DocType |
Wearable Computers, Pervasive Health, Support Vector Machines, Nocturnal Agitation, Accelerometry | Nocturnal,Monitoring system,Remote patient monitoring,Computer science,Wearable computer,Simulation,Real-time computing,Physical medicine and rehabilitation,Wearable technology,Dementia | Conference |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ana Cristina Marcén | 1 | 1 | 0.36 |
| Jesus Carro | 2 | 1 | 1.04 |
| Violeta Monasterio | 3 | 17 | 3.73 |