Fridriksdottir Esther, Bonomi Alberto G
Department of Patient Care & Measurements, Philips Research Laboratories, 5656AE Eindhoven, The Netherlands.
Sensors (Basel). 2020 Nov 10;20(22):6424. doi: 10.3390/s20226424.
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.
本研究的目的是调查深度神经网络（DNN）识别住院患者典型活动的准确性。在模拟医院环境中对20名健康志愿者（10名男性和10名女性，年龄 = 43 ± 13岁）进行了数据收集研究。使用安装在躯干上的单个三轴加速度计来测量身体运动并识别六种活动类型：卧床、直立姿势、行走、轮椅运输、上楼梯和下楼梯。针对此分类问题开发了一个由三层卷积神经网络和一个长短期记忆层组成的DNN。此外，从加速度计数据中提取特征以训练支持向量机（SVM）分类器进行比较。与SVM分类器的83.35%相比，DNN在留出数据集上的总体准确率达到了94.52%。总之，DNN能够使用单个三轴加速度计捕获的数据在模拟医院条件下识别身体活动类型。所描述的方法可用于住院期间对患者活动的持续监测，以提供对康复过程的更多见解。
