# Human Activity Classification Based on Angle Variance Analysis Utilizing the Poincare Plot
###### Abstract  
We propose a single sensor-based activity classification method where the Poincare plot was introduced to analyze the variance of the angle between acceleration vector with gravity calculated from the raw accelerometer data for human activity classification. Two datasets named 'Human Activity Recognition' and 'MHealth dataset' were used to develop the model to classify activity from low to vigorous intensity activities and posture estimation. Short-term and long-term variability analyzing the property of the Poincare plot was used to classify activities according to the vibrational intensity of body movement. Commercially available Actigraphy's activity classification metric 'count' resembled value was used to compare the feasibility of the proposed classification algorithm. In the case of the HAR dataset, laying, sitting, standing, and walking activities were classified. Poincare plot parameters SD1, SD2, and SDRR of angle in the case of angle variance analysis and the mean count of 'Y-', 'Z-' axis were fitted to a support vector machine (SVM) classifier individually and jointly. The variance- and count-based methods have 100% accuracy in the static-dynamic classification. Laying activity classification has 100% accuracy from other static conditions in the proposed method, whereas the count-based method has 98.08% accuracy with 10-fold cross-validation. In the sitting-standing classification, the proposed angle-based algorithm shows 88% accuracy, whereas the count-based approach has 58% accuracy with a support vector machine classifier with 10-fold cross-validation. In the classification of the variants of dynamic activities with the MHealth dataset, the accuracy for angle variance-based and count-based methods is 100%, in both cases, for fivefold cross validation with SVM classifiers. P ocommonly used solution to synchronize physiological data (e.g., ECG and PPG) with activity monitoring sensor data [6]. Continuous assessment of stress can avoid unexpected accidents, especially for the workers' life in many heavy industries. Understanding the normal and abnormal ambulatory physiological condition is dependent on the prior and present status of PA [7; 8; 9]. Many deep learning-based methods, which are complex systems for wearable devices, have been proposed for human activity classification, especially with the University of California, Irvine Human Activity Recognition dataset [10; 11]. As deep learning has automatic feature extraction capabilities, those methods do not require any manual feature extraction techniques. In most cases, a 1D convolutional neural network and temporal sequence-based long-short term memory neural network had been used for feature extraction and classification for human activity [12; 13; 14], despite having some constraints for wearable devices in terms of computational complexity [15]. There are many studies using the conventional machine learning approach where fast feature extraction methods have been proposed to optimize an algorithm with multiple features [16]. Along with machine learning development, many industries have grown worldwide to develop hardware [17] and software products to monitor human activity, and Actigraph is one of them. It is a leading provider of wearable physical activity and sleep monitoring solutions for the global scientific community [18]. Actigraph's research-grade accelerometry monitors are the most widely used and extensively validated devices of their kind [19]. Its monitoring solutions have been used in thousands of academic studies worldwide to objectively measure the physical activity, mobility, and sedentary behavior of study participants in real-world and laboratory settings. A flexible and robust software technology platform provides the most comprehensive data monitoring, analysis, and management support in the industry [20; 21; 22].