Recent trends and challenges of surface electromyography in prosthetic applications.

Surface electromyography (sEMG) meets extensive applications in the field of prosthesis in the current period. The effectiveness of sEMG in prosthesis applications has been verified by numerous revolutionary developments and extensive research attempts. A large volume of research and literature works have explored and validated the vast use of these signals in prostheses as an assistive technology. The objective of this paper is to conduct a systematic review and offer a detailed overview of the work record in the prosthesis and myoelectric interfaces framework. This review utilized a systematic search strategy to identify published articles discussing the state-of-the-art applications of sEMG in prostheses (including upper limb prosthesis and lower limb prostheses). Relevant studies were identified using electronic databases such as PubMed, IEEE Explore, SCOPUS, ScienceDirect, Google Scholar and Web of Science. Out of 3791 studies retrieved from the databases, 188 articles were found to be potentially relevant (after screening of abstracts and application of inclusion-exclusion criteria) and included in this review. This review presents an investigative analysis of sEMG-based prosthetic applications to assist the readers in making further advancements in this field. It also discusses the fundamental advantages and disadvantages of using sEMG in prosthetic applications. It also includes some important guidelines to follow in order to improve the performance of sEMG-based prosthesis. The findings of this study support the widespread use of sEMG in prosthetics. It is concluded that sEMG-based prosthesis technology, still in its sprouting phase, requires significant explorations for further development. Supplementary investigations are necessary in the direction of making a seamless mechanism of biomechatronics for sEMG-based prosthesis by cohesive efforts of robotic researchers and biomedical engineers.

Current Trends in Feature Extraction and Classification Methodologies of Biomedical Signals.

Biomedical signal and image processing is the study of the dynamic behavior of various bio-signals, which benefits academics and research. Signal processing is used to assess the behavior of analogue and digital signals for the assessment, reconfiguration, improved efficiency, extraction of features, and reorganization of patterns. This paper unveils hidden characteristic information about input signals using feature extraction methods. The main feature extraction methods used in signal processing are based on studying time, frequency, and frequency domain. Feature exaction methods are used for data reduction, comparison, and reducing dimensions, producing the original signal with sufficient accuracy with a structure of an efficient and robust pattern for the classifier system. Therefore, an attempt has been made to study the various feature extraction methods, feature transformation methods, classifiers, and datasets for biomedical signals.

A Systematic Review of Machine Learning Based Gait Characteristics in Parkinson's Disease.

OBJECTIVE: Parkinson's disease is a pervasive neuro disorder that affects people's quality of life throughout the world. The unsatisfactory results of clinical rating scales open the door for more research. PD treatment using current biomarkers seems a difficult task. So automatic evaluation at an early stage may enhance the quality and time period of life. METHODS: Grading of Recommendations Assessment, Development, and Evaluation (GRADE) and population, Intervention, Comparison, and Outcome (PICO) search methodology schemes are followed to search the data and eligible studies for this survey. Approximate 1500 articles were extracted using related search strings. After the stepwise mapping and elimination of studies, 94 papers are found suitable for the present review. RESULTS: After the quality assessment of extracted studies, nine inhibitors are identified to analyze people's gait with Parkinson's disease, where four are critical. This review also differentiates the various machine learning classification techniques with their PD analysis characteristics in previous studies. The extracted research gaps are described as future perspectives. Results can help practitioners understand the PD gait as a valuable biomarker for detection, quantification, and classification. CONCLUSION: Due to less cost and easy recording of gait, gait-based techniques are becoming popular in PD detection. By encapsulating the gait-based studies, it gives an in-depth knowledge of PD, different measures that affect gait detection and classification.

A comprehensive assessment of Brain Computer Interfaces: Recent trends and challenges.

BACKGROUND: An uninterrupted channel of communication and control between the human brain and electronic processing units has led to an increased use of Brain Computer Interfaces (BCIs). This article attempts to present an all-encompassing review on BCI and the scientific advancements associated with it. The ultimate goal of this review is to provide a general overview of the BCI technology and to shed light on different aspects of BCIs. This review also underscores the applications, practical challenges and opportunities associated with BCI technology, which can be used to accelerate future developments in this field. METHODS: This review is based on a systematic literature search for tracking down the relevant research annals and proceedings. Using a methodical search strategy, the search was carried out across major technical databases. The retrieved records were screened for their relevance and a total of 369 research chronicles were engulfed in this review based on the inclusion criteria. RESULTS: This review describes the present scenario and recent advancements in BCI technology. It also identifies several application areas of BCI technology. This comprehensive review provides evidence that, while we are getting ever closer, significant challenges still exist for the development of BCIs that can seamlessly integrate with the user's biological system. CONCLUSION: The findings of this review confirm the importance of BCI technology in various applications. It is concluded that BCI technology, still in its sprouting phase, requires significant explorations for further development.

Identification and classification of upper limb motions using PCA.

This paper describes the utility of principal component analysis (PCA) in classifying upper limb signals. PCA is a powerful tool for analyzing data of high dimension. Here, two different input strategies were explored. The first method uses upper arm dual-position-based myoelectric signal acquisition and the other solely uses PCA for classifying surface electromyogram (SEMG) signals. SEMG data from the biceps and the triceps brachii muscles and four independent muscle activities of the upper arm were measured in seven subjects (total dataset=56). The datasets used for the analysis are rotated by class-specific principal component matrices to decorrelate the measured data prior to feature extraction.

Electromyographic classification of effort in muscle strength assessment.

Dual-channel evaluation of surface electromyogram (SEMG) signals acquired from amputee subjects using computational techniques for classification of arm motions is presented in this study. SEMG signals were classified by the neural network (NN) and interpretation was done using statistical techniques to extract the effectiveness of the recorded signals. From the results, it was observed that there exists a calculative difference in amplitude gain across different motions and that SEMG signals have great potential to classify arm motions. The outcomes indicated that the NN algorithm performs significantly better than other algorithms, with a classification rate (CR) of 96.40%. Analysis of variance (ANOVA) presents the results to validate the effectiveness of the recorded data to discriminate SEMG signals. The results are of significant thrust in identifying the operations that can be implemented for classifying upper-limb movements suitable for prostheses' design.

EMG classification using wavelet functions to determine muscle contraction.

Surface electromyogram (SEMG) is a complex signal and is influenced by several external factors/artifacts. The electromyogram signal from the stump of the subject is picked up through surface electrodes. It is amplified and artifacts are removed before digitising it in a controlled manner so that minimum signal loss occurs due to processing. As removing these artifacts is not easy, feature extraction to obtain useful information hidden inside the signal becomes a different process. This paper presents methods of analysing SEMG signals using discrete wavelet Transform (DWT) for extracting accurate patterns of the signals and the performance of the used algorithms is being analysed rigorously. The obtained results suggest a root mean square difference (RMSD) value for the denoising and quality of reconstruction of the SEMG signal. The result shows that the best mother wavelets for tolerance of noise are second order of symmlets and bior6.8. Results inferred that bior6.8 suitable for the classification and analysis of SEMG signals of different arm motions results in a classification accuracy of 88.90%.

A novel feature extraction for robust EMG pattern recognition.

This paper presents the detailed evaluation and classification of Surface Electromyogram (SEMG) signals at different upper arm muscles for different operations. After acquiring the data from selected locations, interpretation of signals was done for the estimation of parameters using simulated algorithm. First, different types of arm operations were analysed; then statistical techniques were implemented for investigating muscle force relationships in terms of amplitude estimation. The classification (Artificial Neural Network) based results have been presented for detecting different pre-defined arm motions in order to discriminate SEMG signals. The outcome of research indicates that a neural network classifier performs best with an average classification rate of 92.50%. Finally, the result also inferred the operations which were observed to be easy for arm recognition and the study is a step forward to develop powerful, flexible and efficient prosthetic designs.

Comparative study of wavelet denoising in myoelectric control applications.

Here, the wavelet analysis has been investigated to improve the quality of myoelectric signal before use in prosthetic design. Effective Surface Electromyogram (SEMG) signals were estimated by first decomposing the obtained signal using wavelet transform and then analysing the decomposed coefficients by threshold methods. With the appropriate choice of wavelet, it is possible to reduce interference noise effectively in the SEMG signal. However, the most effective wavelet for SEMG denoising is chosen by calculating the root mean square value and signal power values. The combined results of root mean square value and signal power shows that wavelet db4 performs the best denoising among the wavelets. Furthermore, time domain and frequency domain methods were applied for SEMG signal analysis to investigate the effect of muscle-force contraction on the signal. It was found that, during sustained contractions, the mean frequency (MNF) and median frequency (MDF) increase as muscle force levels increase.

An analytical approach to test and design upper limb prosthesis.

In this work the signal acquiring technique, the analysis models and the design protocols of the prosthesis are discussed. The different methods to estimate the motion intended by the amputee from surface electromyogram (SEMG) signals based on time and frequency domain parameters are presented. The experiment proposed that the used techniques can help significantly in discriminating the amputee's motions among four independent activities using dual channel set-up. Further, based on experimental results, the design and working of an artificial arm have been covered under two constituents--the electronics design and the mechanical assembly. Finally, the developed hand prosthesis allows the amputated persons to perform daily routine activities easily.