# Hybrid InceptionV3-SVM-Based Approach for Human Posture Detection in Health Monitoring Systems
## 1 Introduction  
It is crucial to maintain an upright posture if you want to live a healthy life. The location of your limbs and how you hold your body make up your posture. With the development of new technologies, human employment has become more sedentary, resulting in a decrease in mobility and physical activity [1]. Long periods of sitting while working or studying cause muscular weakness and make maintenance extremely difficult. People have experienced a variety of problems as a result of not taking care of or not maintaining a proper posture. Musculoskeletal complications are more common to affect the spine, neck, back, and shoulder. Today, health problems caused by poor posture are becoming more widespread in all age groups. Some of the variables that contribute to posture-related bad situations include sedentary work habits, lack of exercise, and poor or uneven sitting positions [1]. For example, [PERSON] et al. [2] examined electromyogram data from 12 patients to assess how the neck and upper extremities were affected by the height of the computerdesk. When 18 participants used a smartphone for a variety of tasks, [PERSON], [PERSON], and [PERSON] [3] used a motion capture device to measure the frequency with which their heads flexed, finding that this was the main source of neck pain. According to a study conducted on 126 students and published in [4], there is a link between a poor forward posture of the neck, that is, a frontward head posture, and neck discomfort, including neck impairment. According to [PERSON] et al. [5], sitting positions have an impact on people's breathing and electrocardiograms. In research on bad head positions, [PERSON] et al. [6] reported that, compared to neutral head posture, respiratory performance decreased. According to [7], research with 88 school students found that slumped seating severely reduces children's ability to breathe. To live in good health and an uninfected life, it is important to maintain proper posture when sitting and standing. Different technologies can be employed to detect posture, with wearable [2,8] and non-wearable pieces of machinery being two of the most popular methods [6,9]. For posture monitoring, non-wearable equipment has been utilized, including the Kinect [9] depth camera and the Vicon MX motion analyzer system [6]. The non-wearable technique for posture monitoring is quite efficient, but its use is limited by the viewing angle of the camera. Furthermore, installing depth sensors for posture monitoring in a smart home context would involve a substantial cost outlay. Wearable posture detection systems enable movement flexibility while enabling posture monitoring. An electromyogram, for example, is used in Kang [2] to check posture. In [8], a smart shirt with components of dual inertial dimensions having 9 degrees of freedom is described as being used to monitor back posture. With ML approaches such as decision trees (DT), K nearest neighbors (KNN), and support vector machines (SVM) [5,9,10], the data collected from various sensor technologies could be processed to classify various postures. For example, in [4], the authors classified two postures with up to 99.5% accuracy using DT, KNN, and SVM techniques using ECG and respirational movement. In [9], a max-margin classifier with up to 88.67% accuracy was used to detect position from the video data set. It was based on the traditional SVM technique. According to [10], the classification precision for five sitting positions was greater than 98% using data from 13 piezoresistive devices mounted on a chair. These classifiers included DT, KNN, and SVM. Based on time series data, deep learning has also been used to categorize human activities [11, 12, 13, 14, 15]. Many robust and systematic classifiers are available for use with deep learning; some of these are covered later in the article.