**8. Future trends**

The computational methods reviewed in this study have evolved over several decades and continue to do so. For example, ANOVA test's inability to detect visually observable waveform due to abnormal gait behavior had been improved with wfANOVA test [20]. Apart from factorization algorithms and PCA, artificial neural


**33**

**Author details**

Rajat Emanuel Singh1

Little Rock, Arkansas, USA

provided the original work is properly cited.

Arkansas at Little Rock, Arkansas, USA

\*Address all correspondence to: kxiqbal@ualr.edu

, Kamran Iqbal1

*A Review of EMG Techniques for Detection of Gait Disorders*

network were implemented for synergy extraction [5]. New time and frequency domain features and hybrid methods for feature selection have been developed and introduced over the years [67]. In these examples, the conventional techniques were enhanced or detection of gait disorders. There is a consistent effort to augment current computational techniques and improve the EMG based detection methods for motor behavior abnormalities. Optimization algorithms, feature level fusion, and advances in computational methodology point to a future for detecting intricate EMG patterns EMG associated with abnormal gait behavior in machine learning. Recently, application of deep learning algorithms to detect abnormal EMG patterns appears more promising [85], and performs well with EMG acquired directly from the muscles. The main issue in clinical application of deep learning is its real-time implementation. The development of powerful graphics processing unit (GPU) and

faster training algorithms will likely resolve such issues in near future.

In conclusion, in this article we reviewed the existing literature on EMG processing techniques from simple thresholding to complex computation algorithms and their application in detecting gait disorders. The pros and cons of the techniques discussed are summarized in **Table 3**. Besides discussing these techniques in detail, our study cites pertinent literature where these techniques were successfully used to detect gait abnormalities. This study clearly points towards the recent trend in assessing gait disorders from EMG data using an intelligent system. Examples of such systems using supervised and unsupervised learning were also reviewed.

*DOI: http://dx.doi.org/10.5772/intechopen.84403*

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

1 College of Engineering and Information Technology, University of Arkansas at

2 School of Counseling, Human Performance, and Rehabilitation, University of

\*, Gannon White2

and Jennifer K. Holtz<sup>2</sup>
