Human Activity Recognition using a Deep Learning Algorithm for Patient Monitoring

Abstract

Physical activity and mobility are important indicators of the recovery process of patients in the general ward of the hospital. Currently, monitoring mobility of hospitalized patients relies largely on direct observation from the caregivers. Accelerometers have the potential to quantify physical activity of patients objectively and without obstructing their daily routines. Human Activity Recognition (HAR) is a technique used to assess the type of activity an individual subject is carrying out based on sensor readings and has been extensively studied. However, the literature shows that HAR methodologies have been largely developed to recognize activities typical for healthy subjects. This means that activities performed at a slow and irregular pace, such as by a symptomatic patient or an elder, are scarcely considered to design HAR methods. Using HAR for patient monitoring would allow clinically meaningful metrics, such as time spent ambulating or in sedentary behaviour each day, to be obtained automatically. This may offer a convenient solution to enable caregivers automatically monitoring the recovery process of patients.

The aim of this work was to develop an accurate HAR model to recognize activities typical for hospitalized patients. A data collection study was conducted with 20 healthy volunteers in a simulated hospital environment. A single triaxial accelerometer placed on the trunk was used to measure body movement and recognize seven activity types: Lying in bed, upright posture, walking, walking with walking aid, wheelchair transport, stair ascent and stair descent. Features from both time and frequency domain were extracted and used to train three machine learning (ML) classifiers (Naïve Bayes, Random Forest, Support Vector Machine). Additionally, a deep neural network (DNN) consisting of a three convolutional layers and a Long Short-Term Memory layer was developed.

The performance of the DNN model was evaluated on holdout data and compared to the performance of the feature-based ML classifiers. The DNN model reached a higher classification accuracy than the latter approaches (F1-score= 0.902 vs. 0.821). All the models showed a large number of misclassification between the walking with or without walking aid class. By combining these two classes the DNN model reached an F1-score 0.946, compared to F1-score 0.856 of the best feature-based ML approach represented by a support vector machine classifier.

This work shows for the first time the value of applying deep-learning techniques to improve the accuracy of feature-based ML classifiers for addressing the problem of HAR using a single triaxial accelerometer in simulated hospital conditions.