Abstract

Multimodal sensors in healthcare applications have been increasingly researched because it facilitates automatic and comprehensive monitoring of human behaviors, high-intensity sports management, energy expenditure estimation, and postural detection. Recent studies have shown the importance of multi-sensor fusion to achieve robustness, high-performance generalization, provide diversity and tackle challenging issue that maybe difficult with single sensor values. The aim of this study is to propose an innovative multi-sensor fusion framework to improve human activity detection performances and reduce misrecognition rate. The study proposes a multi-view ensemble algorithm to integrate predicted values of different motion sensors. To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. Furthermore, we utilized Synthetic Over-sampling minority Techniques (SMOTE) algorithm to reduce the impact of class imbalance and improve performance results. With the above methods, this paper provides unified framework to resolve major challenges in human activity identification. The performance results obtained using two publicly available datasets showed significant improvement over baseline methods in the detection of specific activity details and reduced error rate. The performance results of our evaluation showed 3% to 24% improvement in accuracy, recall, precision, F-measure and detection ability (AUC) compared to single sensors and feature-level fusion. The benefit of the proposed multi-sensor fusion is the ability to utilize distinct feature characteristics of individual sensor and multiple classifier systems to improve recognition accuracy. In addition, the study suggests a promising potential of hybrid feature selection approach, diversity-based multiple classifier systems to improve mobile and wearable sensor-based human activity detection and health monitoring system.

Highlights

  • In recent times, sensor technologies for health monitoring have advanced greatly due to the decrease in the cost and availabilities of sensor-embedded devices

  • We conducted an experimental evaluation of four classification algorithms on the original feature vectors extracted from accelerometer, gyroscope, and magnetometer respectively, to assess the impact of each sensor modality for human activity detection and health monitoring

  • The impact of increasing the minority activity classes using sampling minority Techniques (SMOTE) algorithms was evaluated for both feature-level fusion and multi-view stacking ensemble algorithms

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Summary

Introduction

Sensor technologies for health monitoring have advanced greatly due to the decrease in the cost and availabilities of sensor-embedded devices. Human activity identification has been explored in various sensors types. These include wearable, video, ambient and smartphone-based methods [1, 3]. The use of wearable and smartphone embedded sensors for human activity identification have attracted high interest among researchers. For consistent monitoring of physiological signals, comprehensive health check, indoor localizations and pedestrian navigations [3, 4]. Applications of these devices for identification of various activity details are as results of their pervasiveness, continuous tracking of human activity details and provision of continuous monitoring through cyber-physical systems. Wearable and smartphone devices provide a better alternative for ubiquitous and continuous monitoring of activity details

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