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Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network

Department of Patient Care & Measurements, Philips Research Laboratories, 5656AE Eindhoven, The Netherlands
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Sensors 2020, 20(22), 6424; https://doi.org/10.3390/s20226424
Received: 21 September 2020 / Revised: 5 November 2020 / Accepted: 8 November 2020 / Published: 10 November 2020
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. View Full-Text
Keywords: deep learning; human activity recognition (HAR); multiclass classification; patient monitoring; wearable sensors deep learning; human activity recognition (HAR); multiclass classification; patient monitoring; wearable sensors
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MDPI and ACS Style

Fridriksdottir, E.; Bonomi, A.G. Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network. Sensors 2020, 20, 6424. https://doi.org/10.3390/s20226424

AMA Style

Fridriksdottir E, Bonomi AG. Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network. Sensors. 2020; 20(22):6424. https://doi.org/10.3390/s20226424

Chicago/Turabian Style

Fridriksdottir, Esther; Bonomi, Alberto G. 2020. "Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network" Sensors 20, no. 22: 6424. https://doi.org/10.3390/s20226424

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