*Article* **Walking Speed Classification from Marker-Free Video Images in Two-Dimension Using Optimum Data and a Deep Learning Method**

**Tasriva Sikandar 1 , Sam Matiur Rahman 2 , Dilshad Islam <sup>3</sup> , Md. Asraf Ali 4 , Md. Abdullah Al Mamun 5 , Mohammad Fazle Rabbi 6 , Kamarul H. Ghazali 1 , Omar Altwijri 7 , Mohammed Almijalli 7 and Nizam U. Ahamed 8, \***

	- <sup>4</sup> Department of Computer Science, American International University-Bangladesh (AIUB), Dhaka 1229, Bangladesh
	- <sup>5</sup> Electronics Division, Atomic Energy Centre, Dhaka 1000, Bangladesh
	- <sup>6</sup> School of Health Sciences and Social Work, Griffith University, Gold Coast, QLD 4222, Australia <sup>7</sup> Biomedical Technology Department, College of Applied Medical Sciences, King Saud University,
	- Riyadh 11451, Saudi Arabia
	- <sup>8</sup> Department of Radiation Oncology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15232, USA
	- **\*** Correspondence: nizamahamed@pitt.edu

**Abstract:** Walking speed is considered a reliable assessment tool for any movement-related functional activities of an individual (i.e., patients and healthy controls) by caregivers and clinicians. Traditional video surveillance gait monitoring in clinics and aged care homes may employ modern artificial intelligence techniques to utilize walking speed as a screening indicator of various physical outcomes or accidents in individuals. Specifically, ratio-based body measurements of walking individuals are extracted from marker-free and two-dimensional video images to create a walk pattern suitable for walking speed classification using deep learning based artificial intelligence techniques. However, the development of successful and highly predictive deep learning architecture depends on the optimal use of extracted data because redundant data may overburden the deep learning architecture and hinder the classification performance. The aim of this study was to investigate the optimal combination of ratio-based body measurements needed for presenting potential information to define and predict a walk pattern in terms of speed with high classification accuracy using a deep learningbased walking speed classification model. To this end, the performance of different combinations of five ratio-based body measurements was evaluated through a correlation analysis and a deep learning-based walking speed classification test. The results show that a combination of three ratiobased body measurements can potentially define and predict a walk pattern in terms of speed with classification accuracies greater than 92% using a bidirectional long short-term memory deep learning method.

**Keywords:** two-dimensional (2D) image; marker-free video; walking speed; walking speed classification; bi-LSTM; deep learning; redundant feature; ratio-based body measurement; optimal feature
