In pattern recognition-based myoelectric control, high accuracy for multiple discriminated motions is presented in most of related literature. However, there is a gap between the classification accuracy and the usability of practical applications of myoelectric control, especially the effect of long-term usage. This paper proposes and investigates the behavior of fifty time-domain and frequency-domain features to classify ten upper limb motions using electromyographic data recorded during 21days. The most stable single feature and multiple feature sets are presented with the optimum configuration of myoelectric control, i.e. data segmentation and classifier. The result shows that sample entropy (SampEn) outperforms other features when compared using linear discriminant analysis (LDA), a robust classifier. The averaged test classification accuracy is 93.37%, when trained in only initial first day. It brings only 2.45% decrease compared with retraining schemes. Increasing number of features to four, which consists of SampEn, the fourth order cepstrum coefficients, root mean square and waveform length, increase the classification accuracy to 98.87%. The proposed techniques achieve to maintain the high accuracy without the retraining scheme. Additionally, this continuous classification allows the real-time operation.

http://iihm.imag.fr/publs/2014/ESWA-Draft.pdf

EMG feature evaluation for improving myoelectric pattern recognition robustness

Falls, especially in elderly persons, are an important health problem worldwide. Reliable fall detection systems can mitigate negative consequences of falls. Among the important challenges and issues reported in literature is the difficulty of fair comparison between fall detection systems and machine learning techniques for detection. In this paper, we present UP-Fall Detection Dataset. The dataset comprises raw and feature sets retrieved from 17 healthy young individuals without any impairment that performed 11 activities and falls, with three attempts each. The dataset also summarizes more than 850 GB of information from wearable sensors, ambient sensors and vision devices. Two experimental use cases were shown. The aim of our dataset is to help human activity recognition and machine learning research communities to fairly compare their fall detection solutions. It also provides many experimental possibilities for the signal recognition, vision, and machine learning community.

https://www.mdpi.com/1424-8220/19/9/1988/pdf

UP-Fall Detection Dataset: A Multimodal Approach.

https://research.aalto.fi/files/11701996/1_s2.0_S174680941500021X_main.pdf

Current state of digital signal processing in myoelectric interfaces and related applications

Rich useful information can be obtained from the muscles and researchers can use such information in a wide class of clinical and engineering applications by measuring surface electromyography (EMG) signals (Merletti & Parker, 2004). Normally, EMG signals are acquired by surface electrodes that are placed on the skin superimposed on the targeted muscle. In order to use the EMG signal as a diagnosis signal or a control signal, a feature is often extracted before performing analysis or classification stage (Phinyomark et al., 2012a) because a lot of information, both useful information and noise (Phinyomark et al., 2012b), is contained in the raw EMG data. An EMG feature is a distinct characteristic of the signal that can be described or observed quantitatively, such as being large or small, spiky or smooth, and fast or slow. Generally, EMG features can be computed in numerical form from a finite length time interval and can change as a function of time, i.e. a voltage or a frequency. They can be computed in several domains, such as time domain, frequency domain, timefrequency and time-scale representations (Boostani & Moradi, 2003). However, frequencydomain features show the better performance than other-domain features in case of the assessing muscle fatigue (Al-Mulla et al., 2012). Mean frequency (MNF) and median frequency (MDF) are the most useful and popular frequency-domain features (Phinyomark et al., 2009) and frequently used for the assessment of muscle fatigue in surface EMG signals (Cifrek et al., 2009).

/pdf/the-usefulness-of-mean-and-median-frequencies-in-1uo0ew4a4j.pdf

The Usefulness of Mean and Median Frequencies in Electromyography Analysis

Freezing of gait (FOG) is a serious gait disturbance, common in mid- and late-stage Parkinson’s disease, that affects mobility and increases fall risk. Wearable sensors have been used to detect and predict FOG with the ultimate aim of preventing freezes or reducing their effect using gait monitoring and assistive devices. This review presents and assesses the state of the art of FOG detection and prediction using wearable sensors, with the intention of providing guidance on current knowledge, and identifying knowledge gaps that need to be filled and challenges to be considered in future studies. This review searched the Scopus, PubMed, and Web of Science databases to identify studies that used wearable sensors to detect or predict FOG episodes in Parkinson’s disease. Following screening, 74 publications were included, comprising 68 publications detecting FOG, seven predicting FOG, and one in both categories. Details were extracted regarding participants, walking task, sensor type and body location, detection or prediction approach, feature extraction and selection, classification method, and detection and prediction performance. The results showed that increasingly complex machine-learning algorithms combined with diverse feature sets improved FOG detection. The lack of large FOG datasets and highly person-specific FOG manifestation were common challenges. Transfer learning and semi-supervised learning were promising for FOG detection and prediction since they provided person-specific tuning while preserving model generalization.

https://www.mdpi.com/1424-8220/19/23/5141/pdf

Wearable-Sensor-based Detection and Prediction of Freezing of Gait in Parkinson's Disease: A Review.

Recently, wavelet analysis has proved to be one of the most powerful signal processing tools for the analysis of surface electromyography (sEMG) signals. It has been widely used in sEMG pattern classification for both clinical and engineering applications. This study investigated the usefulness of extracting sEMG features from multiple-level wavelet decomposition and reconstruction. A suitable wavelet based function was used to yield useful resolution components from the sEMG signal. The optimal sEMG resolution component was selected and then its reconstruction carried out. Throughout this process, noise and unwanted sEMG components were removed. Effective sEMG components were extracted with twenty-five state-of-the-art features in both the time domain and the frequency domain. Two criteria were deployed in the evaluation, scatter graphs and a class separation index. The experimental results show that most sEMG features extracted from the reconstructed sEMG signal of the first and second-level wavelet detail coefficients yield improved class separability in feature space. Some features extracted from the sub-signals are recommended such as the myopulse percentage rate, zero crossing, Willison amplitude and the mean absolute value. The proposed method will ensure that the classification accuracy will be as high as possible while the computational time will be as low as possible. Ill. 3, bibl. 24, tabl. 2 (in English; abstracts in English and Lithuanian). DOI: http://dx.doi.org/10.5755/j01.eee.122.6.1816

/pdf/feature-extraction-and-reduction-of-wavelet-transform-d82htzj2d9.pdf

Feature Extraction and Reduction of Wavelet Transform Coefficients for EMG Pattern Classification

To control of the upper-limb prostheses based on surface electromyography (EMG) movement actions, the first and the most significant step is an extraction of the efficient features. In this paper, an evaluation of various existed EMG features based on time and frequency domains is proposed by using a statistical criterion method, namely RES index, the ratio of the Euclidean distance to the standard deviation. The RES index can response the distance between movement scatter groups and directly address the variation of feature in the same group. Moreover, the evaluation of EMG features based on the statistical index does not depend on the classifier types. The EMG signals recorded from ten subjects were employed with seven upper-limb movements and eight muscle positions. Fifteen features that have been widely used to classify the EMG signals were tested with three real-time window size functions including 256, 128, and 64 samples. From the experimental results, Willison amplitude (WAMP) with threshold value 0.025 volts shows the best performance in class separation compared to the other features. Waveform length (WL) and root mean square are useful augmenting features. Two efficient features, i.e., WAMP and WL, are suggested to use as a feature vector for the EMG recognition system. It will be obtained the high classification accuracy and can be reaching for the real-time control system. Moreover, the effect of window-size functions is dependent on the type of features.

Evaluation of EMG Feature Extraction for Movement Control of Upper Limb Prostheses Based on Class Separation Index

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Feature Extraction and Reduction of Wavelet Transform Coefficients for EMG Pattern Classification

Evaluation of EMG Feature Extraction for Movement Control of Upper Limb Prostheses Based on Class Separation Index