Loading...
Please wait, while we are loading the content...
Similar Documents
Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network
| Content Provider | MDPI |
|---|---|
| Author | Fridriksdottir, Esther Bonomi, Alberto G. |
| Copyright Year | 2020 |
| Description | 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. |
| Starting Page | 6424 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s20226424 |
| Journal | Sensors |
| Issue Number | 22 |
| Volume Number | 20 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-11-10 |
| Access Restriction | Open |
| Subject Keyword | Sensors Medical Informatics Deep Learning Human Activity Recognition (har) Multiclass Classification Patient Monitoring Wearable Sensors |
| Content Type | Text |
| Resource Type | Article |
