## 1 Introduction  
Chronic and physical fitness-related diseases are rapidly increasing as the population increases. Physical activities are directly associated with human health benefits. Therefore, many researchers strongly recommend 30 to 40 minutes of physical activity regularly for a healthier life, since it can reduce the risk of many diseases, such as heart attacks, diabetes, cancer, cardiovascular disease, and so on [1]. In hospitals, many patients need continuous monitoring, which is quite expensive and inconvenient,especially for children and elderly people [2]. Instead of relying on expensive treatments and delayed intervention, healthcare sensing technology can inform doctors of escalated incidents beforehand. Among the primary health-care sensing categories, wearable inertial sensors show promising potential for human locomotion tracking [3]. These sensors can monitor human activities conveniently and effectively in a free living environment, and their use is rapidly increasing, representing up to 97% of the market volume by 2020 [4].  
Human activity recognition (HAR) can be broadly classified into template matching, generative and discriminative categories [5; 6]. Firstly, template matching algorithms, such as the K-Nearest Neighbors classifier, compute the distances between the event data. Secondly, generative algorithms, e.g., the Bayes network, use probabilistic graphs to classify human activities. Finally, discriminative approaches model the boundaries between data events [7; 8; 9]. Although these machine learning algorithms operate with little prior information, they nevertheless provide good classification results [10; 11; 12; 13; 14; 15]. However, their feature engineering requires deep expertise that can significantly reduce discriminant errors and improve the performance of a recognition system.  
In this paper, 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. For HAR, novel combined classifiers such as Binary Grey Wolf Optimization (BGWO) and Decision Trees (DTs) are applied. To examine the performance, a new continuous wearable human activity dataset (i.e., the IM-AccGyro dataset) that contains 1D segmented signal sequences is provided; this can be used for the training and testing data of different physical exercises. This will become a benchmark dataset in the field of the wearable activity recognition of physical exercises based on inertial sensors. Additionally, we apply the proposed method to public datasets such as the MOTIONSENSE and MHEALTH datasets. For comparison studies, we consider state-of-the-art methods such as a Genetic algorithm optimized by Ant Colony Optimization (ACO) and a Support Vector Machine (SVM) optimized by Particle Swarm Optimization (PSO). We obtained remarkable improvements in the recognition rates over current state-of-the-art methods.  
The rest of this paper is organized as follows. Section 2 presents related works. Section 3 presents the complete system methodology, which is comprised of physical activity detection, preprocessing, feature extraction, optimization, and classification methodologies. Section 4 explains the complete experimental setting and describes the datasets. Section 5 describes the results and evaluation. Finally, Section 6 presents the conclusion.