# Hybrid InceptionV3-SVM-Based Approach for Human Posture Detection in Health Monitoring Systems
## 2 Related Works  
Numerous studies have been conducted in the literature to develop various postural models. Here, the authors provide an overview of the most recent methods for detecting human postures.  
Using smart technology and portable systems to anticipate and monitor human health is a crucial component of smart cities. As a result, multisensory and LoRa (long-range) technologies are used in this work to decide posture recognition in this work. Low cost and extended communication range are two benefits of LoRa WAN technology. Wearable clothing is created with the help of these two technologies, multisensory and LoRa so that it is comfortable in any position. Due to LoRa's low transmitting frequency and short data transfer size, multiprocessing was employed in this research. For multiprocessing, sliding windows are used, and Random Forest (RF) is used for feature extraction, data processing, and feature selection. Three testers from a 500-group data set are used to improve performance and accuracy [32]. Along with body language, gestures, and postures are nonverbal ways of communicating. This study uses cutting-edge body tracking technologies and augmented reality to detect static posture. Furthermore, group collaboration and learning are detected using unsupervised machine learning using Kinect body position sensors [33]. Accurate yoga practice has been made possible by posture detection. The real-time basis and limited data sets make posture identification a difficult task. Therefore, a sizable data set containing at least 5500 photographs of various yoga positions have been produced to address this problem. The tf-pose estimation technique, which depicts the human body's skeleton in real-time, has been utilized for posture identification. The tf-pose skeleton is used as a feature to create multiple ML techniques and it is used to extract the positions of the joints in the human body (SVM, KNN, logistic regression, DT, NB, and RF). The highest precision of all is provided by the RF model [34; 35; 36]. Because people spend most of their time sitting, there is also another posture issue that affects them.  
Physical and mental health are affected by inadequate and prolonged sitting. Data collection for sitting posture and stretch posture is done with the help of a posture training system. Subsequently, a smart cushion that combines pressure sensors and artificial intelligence (AI) to identify posture. Supervised machine learning models that produce higher results are taught for more than 13 different postures [37]. The pressure sensor on the chair works to prevent unhealthy sitting positions. The analysis is in contrast to DTand RF in this posture detection. The RF classifier [38] is the one that performs best. Sitting posture monitoring systems (SPMSs) are utilized to improve sitting posture. Sensors have been installed. Six different sitting positions are taken into account for this experiment. Then, several ML techniques (such as SVM with RBF kernel, SVM linear, RF, QDA, LDA, NB, and DT) are employed for the body weight ratio, which is determined by SPMS. The results of SVM using the RBF kernel are more accurate than those of other methods [39]. The posture of a person sitting in a wheelchair may also be detected using sophisticated devices. Data are collected from a network of sensors using the neighborhood rule (CNN), balanced using the Kennard-stone technique, and then the dimensions are reduced using principal component analysis. Finally, preprocessed and balanced data are subjected to the KNN algorithm. The amount of data in this study is substantially less, but the results are astonishing [40].