*A Review of EMG Techniques for Detection of Gait Disorders DOI: http://dx.doi.org/10.5772/intechopen.84403*

*Artificial Intelligence - Applications in Medicine and Biology*

Nair et al. 2010 Rheumatoid

abnormal physiological condition is shown in **Table 2**.

**EMG method Pros Cons**

Parkinson's)

domain

EMG

*Pros and cons of EMG processing techniques discussed.*

1.Lower computational burden 2.Takes advantage of experience

1.EMG onset can reveal altered muscle activity (e.g., freezing episodes in

1.Provides quantitative information in frequency and time-frequency

1.An abnormality in MUAP's shape reveals altered motor behavior 2.Requires less processing for Needle

1.Recovers dominant spatio-temporal

2.Useful in certain disorder diagnosis (Cerebral Palsy, stroke, SCI, etc.) 3.Computational cost is dependent on the type of factorization algorithm

profiles in EMG signal

2.Specific Gait abnormalities can be distinguished (suitable for SCI patients) 3.Provides additional features like MdPF, IMNF for further classification 4.Provides algorithmic options that sidestep stationarity issues

**8. Future trends**

Visual inspection of raw EMG

EMG envelope/ onset detection

Frequency and time-frequency analysis

MUAP decomposition

Muscle synergy decomposition

*EMG classification methods.*

Least square Kernel Algorithm

**Table 2.**

daily life activities including falling. The accuracy for detecting trip fall improved with weighted genetic algorithm [73]. A wide variety of time domain, frequency domain, and time-frequency domain features, and optimization techniques provide multiple options to enhance the classification accuracy of gait diagnosis. The performance of each algorithmic class discussed in this review with respect to the

**Classifier Authors Year Conditions Classification Performance**

EMG of healthy and rheumatoid arthritis

and osteoarthritis

Accuracy = 91%

Accuracy = 97%

1.Relies on experience only, hence

1.Impacted by a number of parameters, hence may not be reliable

2.Assumption of stationarity is made

1.Harder to decompose sEMG signal 2.Computational cost is high for

1.Preprocessing of EMG signal impacts the dimensional space for

synergy extraction 2.Choice of algorithm alters the results, i.e., assumption on the type of synergies need to be made

1.Added processing time and computational burden

for some FFT tools

sEMG

chances of error 2.Limited theoretical basis

arthritis

Nair et al. 2010 Osteoarthritis EMG of healthy

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

**32**

**Table 3.**

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.
