Abstract
The classification of human activity is becoming one of the most important areas of human health monitoring and physical fitness. With the use of physical activity recognition applications, people suffering from various diseases can be efficiently monitored and medical treatment can be administered in a timely fashion. These applications could improve remote services for health care monitoring and delivery. However, the fixed health monitoring devices provided in hospitals limits the subjects’ movement. In particular, our work reports on wearable sensors that provide remote monitoring that periodically checks human health through different postures and activities to give people timely and effective treatment. In this paper, we propose a novel human activity recognition (HAR) system with multiple combined features to monitor human physical movements from continuous sequences via tri-axial inertial sensors. The proposed HAR system filters 1D signals using a notch filter that examines the lower/upper cutoff frequencies to calculate the optimal wearable sensor data. Then, it calculates multiple combined features, i.e., statistical features, Mel Frequency Cepstral Coefficients, and Gaussian Mixture Model features. For the classification and recognition engine, a Decision Tree classifier optimized by the Binary Grey Wolf Optimization algorithm is proposed. The proposed system is applied and tested on three challenging benchmark datasets to assess the feasibility of the model. The experimental results show that our proposed system attained an exceptional level of performance compared to conventional solutions. We achieved accuracy rates of 88.25%, 93.95%, and 96.83% over MOTIONSENSE, MHEALTH, and the proposed self-annotated IM-AccGyro human-machine dataset, respectively.
Highlights
IntroductionChronic and physical fitness-related diseases are rapidly increasing as the population increases
Chronic and physical fitness-related diseases are rapidly increasing as the population increases.Physical activities are directly associated with human health benefits
Three basic classifiers, along with optimization algorithms, were used to evaluate the proposed preprocessing and feature extraction methodology. These are the Decision Tree classifier optimized by Binary Grey Wolf Optimization (BGWO), the Support Vector Machine (SVM) optimized by particle PSO, and the Genetic Algorithm optimized by Ant Colony Optimization (ACO)
Summary
Chronic and physical fitness-related diseases are rapidly increasing as the population increases. Discriminative approaches model the boundaries between data events [7,8,9] These machine learning algorithms operate with little prior information, they provide good classification results [10,11,12,13,14,15]. We propose new robust ‘multi-combined’ features to represent human body movements and to classify human activity patterns using the time-series data from tri-axial inertial signals via wearable sensors These new features are combinations of 14 different kinds of features, including statistical features, the Mel Frequency Cepstral Coefficients (MFCC), Electrocardiogram (ECG) features, and Gaussian Mixture Model (GMM) features, which efficiently reduce discriminant errors and improve the performance of the activity recognition system.
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