ABSTRAT:

The posture detection received lots of attention in the fields of human sensing and artificial intelligence. Posture detection can be used for the monitoring health status of elderly remotely by identifying their postures such as standing, sitting and walking. Most of the current studies used traditional machine learning classifiers to identify the posture. However, these methods do not perform well to detect the postures accurately. Therefore, in this study, we proposed a novel hybrid approach based on machine learning classifiers (i. e., support vector machine (SVM), logistic regression (KNN), decision tree, Naive Bayes, random forest, Linear discrete analysis and Quadratic discrete analysis) and deep learning classifiers (i. e., 1D-convolutional neural network (1D- CNN), 2D-convolutional neural network (2D-CNN), LSTM and bidirectional LSTM) to identify posture detection. The proposed hybrid approach uses prediction of machine learning (ML) and deep learning (DL) to improve the performance of ML and DL algorithms. The experimental results on widely benchmark dataset are shown and results achieved an accuracy of more than 98%

EXISTING SYSTEM :

The sensor chair with pressure sensor tries to avoid wrong sitting position which may cause disease. In this posture detection, analysis is compared with decision tree and random forest. The classifier which gives better performance is random forest classifier [12]. For the improvement of sitting posture, sitting posture monitoring systems (SPMSs) is used. It has mounted sensors on backrest and seat plate of a chair. For this experiment 6 sitting postures are considered. Then various machine learning algorithms (SVM with RBF kernel, SVM linear, random forest, QDA, LDA, NB and DT) are applied on body weight ratio which is measured by SPMS. Result from SVM with RBF kernel gives better accuracy as compare to others [13]. There is also an intelligent systems design for the posture detection of sitting person on wheel chair. A network of sensors is used for data collection using neighbourhood rule (CNN), then data balancing is done with Kennard-stone algorithm and reduction in dimensions via principal component analysis. Finally k-nearest algorithm is applied to pre-processed and balanced data. In this amount of data is significantly reduced but result is remarkable

EXISTING SYSTEM DISADVANTAGES:

1.LESS ACCURACY

2. LOW EFFICIENCY

PROPOSED SYSTEM :

The hybrid methods consists of different classifiers and combining their prediction to train meta-learning model. The hybrid is used to enhance the performance of specific system. In this study, the prediction of ML classifiers (logistic regression, random forest, KNN, Naïve Bayes, decision tree, linear discriminant analysis, quadratic discriminant analysis and SVM) and DL classifiers (CNN, LSTM) are used as input of CNN, LSTM architecture. Fig 2 shows the architecture of proposed hybrid of ML and DL for posture detection. It is to be noted that, the parameters of each classifier has been set-up empirically after several simulation experiments

PROPOSED SYSTEM ADVANTAGES:

1.HIGH ACCURACY

2.HIGH EFFICIENCY

SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10

HARDWARE REQUIREMENTS:

 Processor : Core I3
 RAM Capacity : 2 GB
 Hard Disk : 250 GB
 Monitor : 15″ Color
 Mouse : 2 or 3 Button Mouse
 Key Board : Windows 08/10