| Autor: |
Esther Fridriksdottir, Alberto G. Bonomi |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Sensors, Vol 20, Iss 22, p 6424 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
1424-8220 |
| DOI: |
10.3390/s20226424 |
| Popis: |
The objective of this study was to investigate the accuracy of a Deep Neural Network (DNN) in recognizing activities typical for hospitalized patients. A data collection study was conducted with 20 healthy volunteers (10 males and 10 females, age = 43 ± 13 years) in a simulated hospital environment. A single triaxial accelerometer mounted on the trunk was used to measure body movement and recognize six activity types: lying in bed, upright posture, walking, wheelchair transport, stair ascent and stair descent. A DNN consisting of a three-layer convolutional neural network followed by a long short-term memory layer was developed for this classification problem. Additionally, features were extracted from the accelerometer data to train a support vector machine (SVM) classifier for comparison. The DNN reached 94.52% overall accuracy on the holdout dataset compared to 83.35% of the SVM classifier. In conclusion, a DNN is capable of recognizing types of physical activity in simulated hospital conditions using data captured by a single tri-axial accelerometer. The method described may be used for continuous monitoring of patient activities during hospitalization to provide additional insights into the recovery process. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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