Nowadays, wearable technology can enhance physical human life-log routines by shifting goals from merely counting steps to tackling significant healthcare challenges. Such wearable technology modules have presented opportunities to acquire important information about human activities in real-life environments. The purpose of this paper is to report on recent developments and to project future advances regarding wearable sensor systems for the sustainable monitoring and recording of human life-logs. On the basis of this survey, we propose a model that is designed to retrieve better information during physical activities in indoor and outdoor environments in order to improve the quality of life and to reduce risks. This model uses a fusion of both statistical and non-statistical features for the recognition of different activity patterns using wearable inertial sensors, i.e., triaxial accelerometers, gyroscopes and magnetometers. These features include signal magnitude, positive/negative peaks and position direction to explore signal orientation changes, position differentiation, temporal variation and optimal changes among coordinates. These features are processed by a genetic algorithm for the selection and classification of inertial signals to learn and recognize abnormal human movement. Our model was experimentally evaluated on four benchmark datasets: Intelligent Media Wearable Smart Home Activities (IM-WSHA), a self-annotated physical activities dataset, Wireless Sensor Data Mining (WISDM) with different sporting patterns from an IM-SB dataset and an SMotion dataset with different physical activities. Experimental results show that the proposed feature extraction strategy outperformed others, achieving an improved recognition accuracy of 81.92%, 95.37%, 90.17%, 94.58%, respectively, when IM-WSHA, WISDM, IM-SB and SMotion datasets were applied.

Advancements in wearable sensors technologies provide prominent effects in the daily life activities of humans. These wearable sensors are gaining more awareness in healthcare for the elderly to ensure their independent living and to improve their comfort. In this paper, we present a human activity recognition model that acquires signal data from motion node sensors including inertial sensors, i.e., gyroscopes and accelerometers. First, the inertial data is processed via multiple filters such as Savitzky–Golay, median and hampel filters to examine lower/upper cutoff frequency behaviors. Second, it extracts a multifused model for statistical, wavelet and binary features to maximize the occurrence of optimal feature values. Then, adaptive moment estimation (Adam) and AdaDelta are introduced in a feature optimization phase to adopt learning rate patterns. These optimized patterns are further processed by the maximum entropy Markov model (MEMM) for empirical expectation and highest entropy, which measure signal variances for outperformed accuracy results. Our model was experimentally evaluated on University of Southern California Human Activity Dataset (USC-HAD) as a benchmark dataset and on an Intelligent Mediasporting behavior (IMSB), which is a new self-annotated sports dataset. For evaluation, we used the “leave-one-out” cross validation scheme and the results outperformed existing well-known statistical state-of-the-art methods by achieving an improved recognition accuracy of 91.25%, 93.66% and 90.91% when compared with USC-HAD, IMSB, and Mhealth datasets, respectively. The proposed system should be applicable to man–machine interface domains, such as health exercises, robot learning, interactive games and pattern-based surveillance.

The monitoring of human physical activities using wearable sensors, such as inertial-based sensors, plays a significant role in various current and potential applications. These applications include physical health tracking, surveillance systems, and robotic assistive technologies. Despite the wide range of applications, classification and recognition of human activities remains imprecise and this may contribute to unfavorable reactions and responses. To improve the recognition of human activities, we designed a dataset in which ten participants (five male and five female) performed 11 different activities wearing three body-worn inertial sensors in different locations on the body. Our model extracts data via a hierarchical feature-based technique. These features include time, wavelet, and time-frequency domains, respectively. Stochastic gradient descent (SGD) is then introduced to optimize selective features. The selected features with optimized patterns are further processed by multi-layered kernel sliding perceptron to develop adaptive learning for the classification of physical human activities. Our proposed model was experimentally evaluated and applied on three benchmark datasets: IM-WSHA, a self-annotated dataset, PAMAP2 dataset which is comprised of daily living activities, and an HuGaDB, a dataset which contains physical activities for aging people. The experimental results show that the proposed method achieves better results and outperforms others in terms of recognition accuracy, achieving an accuracy rate of 83.18%, 94.16%, and 92.50% respectively, when IM-WSHA, PAMAP2, and HuGaDB datasets are applied.

Human activity recognition using MotionNode sensors is getting prominence effect in our daily life logs. Providing accurate information on human's activities and behaviors is one of the most challenging tasks in ubiquitous computing and human-Computer interaction. In this paper, we proposed an efficient model for having statistical features along SMO-based random forest. Initially, we processed a 1-D Hadamard transform wavelet and 1-D LBP based extraction algorithm to extract valuable features. For activity classification, we used sequential minimal optimization along with Random Forest over two benchmarks USC-HAD dataset and IMSB datasets. Experimental results show that our proposed model can compete with other state-of-the-art methods and can be effectively used to recognize robust human activities in terms of efficiency and accuracy.

Human physical activity recognition from inertial sensors is shown to be a successful approach for monitoring elderly individuals and children in indoor and outdoor environments. As a result, researchers have shown significant interest in developing state-of-the-art machine learning methods capable of utilizing inertial sensor data and providing key decision support in different scenarios. This paper analyzes data-driven techniques for recognizing human daily living activities. Therefore, to improve the recognition and classification of human physical activities (for example, walking, drinking, and running), we introduced a model that integrates data preprocessing methods (such as denoising) along with major domain features (such as time, frequency, wavelet, and time–frequency features). Following that, stochastic gradient descent (SGD) is used to improve the performance of the extracted features. The selected features are catered to the random forest classifier to detect and monitor human physical activities. Additionally, the proposed HPAR system was evaluated on five benchmark datasets, namely the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE databases. The experimental results show that the HPAR system outperformed the present state-of-the-art methods with recognition rates of 90.18%, 91.25%, 91.83%, 90.46%, and 92.16% from the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE datasets, respectively. The proposed HPAR model has potential applications in healthcare, gaming, smart homes, security, and surveillance.

Physical activity monitoring using wireless sensors is highly demanded in medical fields such as heart attack monitoring, Parkinson's disease, sleep problems and disrupted circadian rhythms. For the productivity of these areas, many researchers are actively involved in physical activity monitoring. However, they still lack potency in term of recognition accuracy. This paper proposed a novel system that attempts to improve recognition accuracy by employing Electrocardiogram and Gaussian Mixture Model feature extraction strategy along with Ant Colony Optimization and Genetic Algorithm as a classifier. Unlike conventional results, our proposed model has shown a high validity results of 83.33% and 92.70% over the two-public benchmark IMSB and USC-HAD datasets, respectively. The proposed system can be used to monitor physical activities of human that can be helpful in daily life activities especially in medical field.