Performance Comparisson Human Activity Recognition Using Simple Linear Method

Abstract

Human activity recognition (HAR) with daily activities have become leading problems in human physical analysis. HAR with wide application in several areas of human physical analysis were increased along with several machine learning methods. This topic such as fall detection, medical rehabilitation or other smart appliance in physical analysis application has increase degree of life. Smart wearable devices with inertial sensor accelerometer and gyroscope were popular sensor for physical analysis. The previous research used this sensor with a various position in the human body part. Activities can classify in three class, static activity (SA), transition activity (TA), and dynamic activity (DA). Activity from complexity in activities can be separated in low and high complexity based on daily activity. Daily activity pattern has the same shape and patterns with gathering sensor. Dataset used in this paper have acquired from 30 volunteers. Seven basic machine learning algorithm Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosted and K-Nearest Neighbor. Confusion activities were solved with a simple linear method. The purposed method Logistic Regression achieves 98% accuracy same as SVM with linear kernel, with same result hyperparameter tuning for both methods have the same accuracy. LR and SVC its better used in SA and DA without TA in each recognizing.

Human activity recognition for static and dynamic activity using convolutional neural network

“…Hyperparameter is a method in the neural network that allows users to obtain a combination of parameters that have the best accuracy value from a number of previous neural network computing steps [23]. The combination of parameters obtained by using hyperparameter includes the number of layers used, the mapping feature, size convolution filter, size pooling dataset [24]- [30]. The parameters used on the proposed CNN model before tuning were seen in Table 3.…”