# Human Activity Classification Based on Angle Variance Analysis Utilizing the Poincare Plot
## 4 Discussion  
In this study, a Poincare plot-based variance analysis was applied to angles derived from raw accelerometer data to classify static and dynamic activities along with their variants (e.g., laying, sitting, and standing for static activities).  
In terms of classification between static and dynamic activities, both count- and angle-based classification is evidently accurate. The acceleration variation is reflected significantly when the user is in motion, which thereby increases the variability in accelerometer counts and the angle.  
In the case of laying and sitting-standing classification, the results for the mean count of the X-axis show accuracy (98.26%), sensitivity (98.14%), and specificity (98.31%), whereas the combined feature for the mean count of three axes together shows the same results because of standard scaling or normalization. However, the other two axes (Y and Z) do not perform well individually compared with the mean count of the X-axis. However, the results were found without the initial orientation of the senor.  
In the case of count-based calculation for static activities, many subjects' mean count was zero (as the count method calculates the value after minimum vibration). After the initial orientation of the device, the mean angle was found to be a good marker for laying and sitting-standing classification, which is very much obvious from Figure 6.  
In the case of classification between sitting and standing, we hypothesize that standing would induce more postural variability in the accelerometer owing to the balance retraining. Following this hypothesis, the classification between sitting and standing with the angle variance-based method yielded an accuracy of 68.08% with SD1. Eventually, we realized that the mean angle performed better in accuracy (88.11%) with sensitivity (97.39%) and specificity (82.53%). In contrast, the count-based method was performed with accuracy (57.62%), sensitivity (67.85%), and specificity (54.44%).  
In terms of vigorous activity classification with walk and run data from the mHealth dataset, count (X, Y, Z) and SD (SD1, SD2, SDRR) were considered individually and jointly for the walking-running classification. The overall count- and variance-based classification accuracy was 100% and 100%, respectively, with five-fold cross-validation for SVM classifiers.  
In this paper, we have shown a robust classification of activity using variance features from a body-mounted accelerometer. In comparison with some deep learning architectures in [42; 43; 44], the overall accuracy was 92.71%, 92.13%, and 92.67% with the 1D convolutional neural network (CNN), CNN-LSTM, and bidirectional LSTM, respectively. In [45], Bayes classifier is used where the time, frequency, and spatial domain totals of 19 features are extracted. Principal component analysis is applied to reduce the dimension, whereas the overall accuracy for the proposed method is around 96% for all activities without any dimensionality reduction with SD1, SD2, and SDRR. To the best of our knowledge, this  
Figure 12: ROC curve for walking and running classification. The left panel denotes the ROC curve for the Actigraph count-based walking and running classification. The right panel denotes the Poincare plot-based walking and running activities’ classification.  
is the first study of such kind. Activity classification based on the accelerometer-only feature will enable cheaper and robust applications in telemedicine applications, as well as continuous monitoring of stress signature on humans. Such a classification method will have manifold advantages ranging from health applications to continuous patient monitoring, tracking the stress markers of industry workers. Hence, human activity classification with significant accuracy is warranted.