Wavelet Based Machine Learning Technique to Classify the Different Shoulder Movement of Upper Limb Amputee

Abstract

The wavelet transform is an accurate, efficient and efficacious method to improve the quality of the myoelectric signal. Classification of the signal from upper limb using Surface Electromyogram (SEMG) signal has been the matter of extensive research. Number of methods and algorithms have been described by researchers to classify biomedical signals. The main aim of this paper to extract the different coefficient values from the given SEMG data by using Discrete Wavelet Transform (DWT). Afterward, random forest machine learning algorithm was used to identify the different shoulder movement of an upper limb amputee. The combination of wavelet coefficients and random forest exhibited the best performance with 99.2% accuracy for the classification of different shoulder motions. It was found that the different motion can be identified accurately and provide the fundamental information to develop an efficient prosthetic device.

Shoulder girdle recognition using electrophysiological and low frequency anatomical contraction signals for prosthesis control

“…Absorbing the scalar a T µ into the term a by an appending process, we have Equation (11), with a 0 and x i 0 being (n + 1) dimensional vectors, which ensures that computational complexity is minimised from not centring the data matrix [33,34]. µ is the sample mean vector and x I is the ith data point for a specific class.…”

“…As with Kaur et al. [11], Sharba et al. [4] extracted features from the time‐frequency domain through the wavelet transform and obtained an accuracy of 87% for the case of amputees across seven shoulder motions.…”

“…In the literature, key works by Gonzalez et al [7], Rivela et al [8], Soma et al [9], and Horiuchi et al [10] showed that shoulder motions could be identified for a group of nonamputees, whereas Kaur et al [11] managed to differentiate shoulder motions for a group of amputees across four shoulder motions, and using the wavelet transform, obtained an accuracy of 98%. More recently, Sharba et al [4], were able to differentiate between various shoulder motions for a group of non-amputees and amputees, where they showed that high classification accuracies (CAs) were obtainable with a slight degradation in the accuracy for the case of the amputee participants.…”

“…This was combined with an accelerometer (Acc), which has the ability to monitor low frequency displacements emanating from both anatomical contractions and skeletal movements that come from designated shoulder gesture motions [4]. As with Kaur et al [11], Sharba et al [4] extracted features from the time-frequency domain through the wavelet transform and obtained an accuracy of 87% for the case of amputees across seven shoulder motions. The wavelet has been said to be computationally intense in the scope of real-time performance, and as a result, it is mainly suited to research-based exercises and case studies at this point [4].…”

Real-time classification of shoulder girdle motions for multifunctional prosthetic hand control: A preliminary study

“…Their study demonstrated the ability to use muscles nearby the shoulder to classify different grips of the arm or hand. Kaur et al 11 carried out a study of shoulder motion classification by using wavelet as a feature extraction with the random forest as a classifier. Their study classified shoulder motions by considering three muscles, namely, pectoralis, trapezius, and teres muscles.…”