Health monitoring catalogue based on human activity classification using machine learning

Abstract

In recent times, fitness trackers and smartphones equipped with different sensors like gyroscopes, accelerometers, global positioning system sensors and programs are used for recognizing human activities. In this paper, the results collected from these devices are used to design a system that can assist an application in monitoring a person’s health. The proposed system takes the raw sensor signals as input, preprocesses it and using machine learning techniques outputs the state of the user with minimum error. The objective of this paper is to compare the performance of different algorithms logistic regression (LR), support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The algorithms are trained and tested with an original number of features as well as with transformed number of features (using linear discriminant analysis). The data with a smaller number of features is then used to visualize the high dimensional data. In this paper, each data point is mapped in the high dimensional data to two-dimensional data using t-distributed stochastic neighbor embedding technique. Overall, the first high dimensional data is visualized and compared with model’s performance with different algorithms and different number of coordinates.

Machine Learning in Molecular Communication and Applications for Health Monitoring Networks

“…In MHM activities, sensor data is usually a multi-channel time series with spatial and temporal dependencies. A CNN as well as LSTM combo has outperformed a CNN and LSTM alone on MHM tasks [20]. The name of a deep neural network structure developed in study is convolutional bi-directional long short-term memory networks(CBLSTM).…”

Deep Learning Based Hybrid Classifier for Analyzing Hepatitis C in Ultrasound Images

“…Based on these classifications, it is clear that two-class classifiers are superior to three-class classifiers, which do not provide enough classification power. To solve this problem, a hybrid classifier was developed [37][38][39] that would combine the findings of all trained classifiers by assigning different weights to the probability of the classifications that each classifier would provide. This was accomplished by using a soft -max layer in each classifier to distribute input probabilities over all classes.…”

Integrating random forest model and internet of things-based sensor for smart poultry farm monitoring system

“…Machine learning-based prediction models have found application in various domains, including their use in identifying human loitering through vision sensor technology within surveillance systems [26], to predict gas-fired boiler flue gas oxygen content [27] and human activity recognition [28], [29]. Moreover, the use of IoT sensor data as input for machine learning models has been previously utilized and shown favorable results in predicting the environmental conditions within poultry houses.…”