A hybrid posture detection framework: integrating machine learning and deep neural networks

Sidrah Liaqat, Kia Dashtipour, Kamran Arshad, Khaled Assaleh, Naeem Ramzan

Research output: Contribution to journalArticlepeer-review

65 Citations (Scopus)
95 Downloads (Pure)


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%.
Original languageEnglish
Pages (from-to)9515-9522
Number of pages8
JournalIEEE Sensors Journal
Issue number7
Early online date1 Feb 2021
Publication statusPublished - 1 Apr 2021


  • posture detection
  • hybrid approach
  • deep learning
  • machine learning


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