A survey on bio-signal analysis for human-robot interaction

The use of bio-signals analysis in human-robot interaction is rapidly increasing. There is an urgent demand for it in various applications, including health care, rehabilitation, research, technology, and manufacturing. Despite several state-of-the-art bio-signals analyses in human-robot interaction (HRI) research, it is unclear which one is the best. In this paper, the following topics will be discussed: robotic systems should be given priority in the rehabilitation and aid of amputees and disabled people; second, domains of feature extraction approaches now in use, which are divided into three main sections (time, frequency, and time-frequency). The various domains will be discussed, then a discussion of each domain's benefits and drawbacks, and finally, a recommendation for a new strategy for robotic systems

Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition

Title on author’s file: Classification of mechanomyogram signal using wavelet packet transform and singular value decomposition for multifunction prosthesis control2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Effectiveness of surface electromyography in pattern classification for upper limb amputees

This study was undertaken to explore 18 time domain (TD) and time-frequency domain (TFD) feature configurations to determine the most discriminative feature sets for classification. Features were extracted from the surface electromyography (sEMG) signal of 17 hand and wrist movements and used to perform a series of classification trials with the random forest classifier. Movement datasets for 11 intact subjects and 9 amputees from the NinaPro online database repository were used. The aim was to identify any optimum configurations that combined features from both domains and whether there was consistency across subject type for any standout features. This work built on our previous research to incorporate the TFD, using a Discrete Wavelet Transform with a Daubechies wavelet. Findings report configurations containing the same features combined from both domains perform best across subject type (TD: root mean square (RMS), waveform length, and slope sign changes; TFD: RMS, standard deviation, and energy). These mixed-domain configurations can yield optimal performance (intact subjects: 90.98%; amputee subjects: 75.16%), but with only limited improvement on single-domain configurations. This suggests there is limited scope in attempting to build a single absolute feature configuration and more focus should be put on enhancing the classification methodology for adaptivity and robustness under actual operating conditions

Real-time EMG based pattern recognition control for hand prostheses : a review on existing methods, challenges and future implementation

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations

Towards electrodeless EMG linear envelope signal recording for myo-activated prostheses control

After amputation, the residual muscles of the limb may function in a normal way, enabling the electromyogram (EMG) signals recorded from them to be used to drive a replacement limb. These replacement limbs are called myoelectric prosthesis. The prostheses that use EMG have always been the first choice for both clinicians and engineers. Unfortunately, due to the many drawbacks of EMG (e.g. skin preparation, electromagnetic interferences, high sample rate, etc.); researchers have aspired to find suitable alternatives. One proposes the dry-contact, low-cost sensor based on a force-sensitive resistor (FSR) as a valid alternative which instead of detecting electrical events, detects mechanical events of muscle. FSR sensor is placed on the skin through a hard, circular base to sense the muscle contraction and to acquire the signal. Similarly, to reduce the output drift (resistance) caused by FSR edges (creep) and to maintain the FSR sensitivity over a wide input force range, signal conditioning (Voltage output proportional to force) is implemented. This FSR signal acquired using FSR sensor can be used directly to replace the EMG linear envelope (an important control signal in prosthetics applications). To find the best FSR position(s) to replace a single EMG lead, the simultaneous recording of EMG and FSR output is performed. Three FSRs are placed directly over the EMG electrodes, in the middle of the targeted muscle and then the individual (FSR1, FSR2 and FSR3) and combination of FSR (e.g. FSR1+FSR2, FSR2-FSR3) is evaluated. The experiment is performed on a small sample of five volunteer subjects. The result shows a high correlation (up to 0.94) between FSR output and EMG linear envelope. Consequently, the usage of the best FSR sensor position shows the ability of electrode less FSR-LE to proportionally control the prosthesis (3-D claw). Furthermore, FSR can be used to develop a universal programmable muscle signal sensor that can be suitable to control the myo-activated prosthesis

BCI applications based on artificial intelligence oriented to deep learning techniques

A Brain-Computer Interface, BCI, can decode the brain signals corresponding to the intentions of individuals who have lost neuromuscular connection, to reestablish communication to control external devices. To this aim, BCI acquires brain signals as Electroencephalography (EEG) or Electrocorticography (ECoG), uses signal processing techniques and extracts features to train classifiers for providing proper control instructions. BCI development has increased in the last decades, improving its performance through the use of different signal processing techniques for feature extraction and artificial intelligence approaches for classification, such as deep learning-oriented classifiers. All of these can assure more accurate assistive systems but also can enable an analysis of the learning process of signal characteristics for the classification task. Initially, this work proposes the use of a priori knowledge and a correlation measure to select the most discriminative ECoG signal electrodes. Then, signals are processed using spatial filtering and three different types of temporal filtering, followed by a classifier made of stacked autoencoders and a softmax layer to discriminate between ECoG signals from two types of visual stimuli. Results show that the average accuracy obtained is 97% (+/- 0.02%), which is similar to state-of-the-art techniques, nevertheless, this method uses minimal prior physiological and an automated statistical technique to select some electrodes to train the classifier. Also, this work presents classifier analysis, figuring out which are the most relevant signal features useful for visual stimuli classification. The features and physiological information such as the brain areas involved are compared. Finally, this research uses Convolutional Neural Networks (CNN) or Convnets to classify 5 categories of motor tasks EEG signals. Movement-related cortical potentials (MRCPs) are used as a priori information to improve the processing of time-frequency representation of EEG signals. Results show an increase of more than 25% in average accuracy compared to a state-of-the-art method that uses the same database. In addition, an analysis of CNN or ConvNets filters and feature maps is done to and the most relevant signal characteristics that can help classify the five types of motor tasks.DoctoradoDoctor en Ingeniería Eléctrica y Electrónic

Kohti yläraaja-proteesien ohjausta pintaelektromyografialla

The loss of an upper limb is a life-altering accident which makes everyday life more difficult.A multifunctional prosthetic hand with an user-friendly control interface may significantlyimprove the life quality of amputees. However, many amputees do not use their prosthetichand regularly because of its low functionality, and low controllability. This situation callsfor the development of versatile prosthetic limbs that allow amputees to perform tasks thatare necessary for activities of daily living. The non-pattern based control scheme of the commercial state-of art prosthesis is rather poorand non-natural. Usually, a pair of muscles is used to control one degree of freedom. Apromising alternative to the conventional control methods is the pattern-recognition-basedcontrol that identifies different intended hand postures of the prosthesis by utilizing theinformation of the surface electromyography (sEMG) signals. Therefore, the control of theprosthesis becomes natural and easy. The objective of this thesis was to find the features that yield the highest classificationaccuracy in identifying 7 classes of hand postures in the context of Linear DiscriminantClassifier. The sEMG signals were measured on the skin surface of the forearm of the 8 ablebodiedsubjects. The following features were investigated: 16 time-domain features, twotime-serial-domain features, the Fast Fourier Transform (FFT), and the Discrete WaveletTransform (DWT). The second objective of this thesis was to study the effect of the samplingrate to the classification accuracy. A preprocessing technique, Independent ComponentAnalysis (ICA), was also shortly examined. The classification was based on the steady statesignal. The signal processing, features, and classification were implemented with Matlab. The results of this study suggest that DWT and FFT did not outperform the simple andcomputationally efficient time domain features in the classification accuracy. Thus, at least innoise free environment, the high classification accuracy (> 90 %) can be achieved with asmall number of simple TD features. A more reliable control may be achieved if the featuresare selected individually of a subset of the effective features. Using the sampling rate of 400Hz instead of commonly used 1 kHz may not only save the data processing time and thememory of the prosthesis controller but also slightly improve the classification accuracy.ICA was not found to improve the classification accuracy, which may be because themeasurement channels were placed relatively far from each other.Yläraaja-amputaatio vaikuttaa suuresti päivittäiseen elämään. Helposti ohjattavalla toiminnallisillaproteeseilla amputoitujen henkilöiden elämänlaatua voitaisiin parantaa merkittävästi.Suurin osa amputoiduista henkilöistä ei kuitenkaan käytä proteesiaan säännöllisesti proteesinvähäisten toimintojen ja vaikean ohjattavuuden vuoksi. Olisikin tärkeää kehittää helpostiohjattava ja riittävästi toimintoja sisältävä proteesi, joka mahdollistaisi päivittäisessäelämässä välttämättömien tehtävien suorittamisen. Markkinoilla olevat lihassähköiset yläraajaproteesit perustuvat yksinkertaiseen hahmontunnistustahyödyntämättömään ohjaukseen, jossa lihasparilla ohjataan yleensä yhtä proteesinvapausastetta. Lupaava vaihtoehto perinteisille ohjausmenetelmille on hahmontunnistukseenpohjautuva ohjaus. Se tunnistaa käyttäjän käden asennot käsivarren iholta mitatun lihassähkösignaalinsisältämän informaation avulla mahdollistaen helpon ja luonnollisen ohjauksen. Tämän diplomityön tavoitteena oli löytää piirteet, jolla seitsemän erilaista käden asentoa pystytäänluokittelemaan mahdollisimman tarkasti lineaarisella diskriminantti luokittelijalla.Lihassähkösignaalit mitattiin kahdeksan ei-amputoidun koehenkilön käsivarresta ihon pinnallekiinnitetyillä elektrodeilla. Työssä vertailtiin seuraavia piirteitä: 16 aika-alueen piirrettä,kaksi aikasarja-alueen piirrettä, nopea Fourier-muunnos (FFT), diskreetti Aallokemuunnos(DWT). Työn toinen tavoite oli tutkia näytteenottotaajuuden vaikutusta luokittelutarkkuuteen.Myös esiprosessointia riippumattomien komponenttien analyysillä tutkittiinlyhyesti. Luokittelu tehtiin staattisen lihassupistuksen aikana mitatun signaalin perusteella.Signaalin prosessointi, piirteet ja luokittelu toteutettiin Matlabilla. Tämän tutkimuksen tulokset osoittivat, etteivät diskreetti Aalloke-muunnos ja nopea Fouriermuunnosyllä laskennallisesti tehokkaampia aika-alueen piirteitä parempaan luokittelutarkkuuteen.Pienellä määrällä yksinkertaisia aika-alueen piirteitä voidaan saavuttaa hyvä luokittelutarkkuus(>90 %). Luokittelutarkkuutta voitaneen edelleen parantaa valitsemalla optimaalisetpiirteet yksilöllisesti pienestä joukosta hyviksi havaittuja piirteitä. Käyttämällä 400Hz:n näytteenottotaajuutta yleisesti käytetyn 1 kHz:n sijasta, voidaan sekä säästää prosessointiaikaaja proteesin prosessorin muistia että myös parantaa hieman luokittelutarkkuutta.Esiprosessointi riippumattomien komponenttien analyysillä ei parantanut luokittelutarkkuutta,mikä johtunee siitä, että mittauskanavat olivat suhteellisen kaukana toisistaan

Control of multifunctional prosthetic hands by processing the electromyographic signal

The human hand is a complex system, with a large number of degrees of freedom (DoFs), sensors embedded in its structure, actuators and tendons, and a complex hierarchical control. Despite this complexity, the efforts required to the user to carry out the different movements is quite small (albeit after an appropriate and lengthy training). On the contrary, prosthetic hands are just a pale replication of the natural hand, with significantly reduced grasping capabilities and no sensory information delivered back to the user. Several attempts have been carried out to develop multifunctional prosthetic devices controlled by electromyographic (EMG) signals (myoelectric hands), harness (kinematic hands), dimensional changes in residual muscles, and so forth, but none of these methods permits the "natural" control of more than two DoFs. This article presents a review of the traditional methods used to control artificial hands by means of EMG signal, in both the clinical and research contexts, and introduces what could be the future developments in the control strategy of these devices

Longitudinal tracking of physiological state with electromyographic signals.

Electrophysiological measurements have been used in recent history to classify instantaneous physiological configurations, e.g., hand gestures. This work investigates the feasibility of working with changes in physiological configurations over time (i.e., longitudinally) using a variety of algorithms from the machine learning domain. We demonstrate a high degree of classification accuracy for a binary classification problem derived from electromyography measurements before and after a 35-day bedrest. The problem difficulty is increased with a more dynamic experiment testing for changes in astronaut sensorimotor performance by taking electromyography and force plate measurements before, during, and after a jump from a small platform. A LASSO regularization is performed to observe changes in relationship between electromyography features and force plate outcomes. SVM classifiers are employed to correctly identify the times at which these experiments are performed, which is important as these indicate a trajectory of adaptation

Characterization of neurological disorders using evolutionary algorithms

The life expectancy increasing, in the last few decades, leads to a large diffusion of neurodegenerative age-related diseases such as Parkinson’s disease. Neurodegenerative diseases are part of the huge category of neurological disorders, which comprises all the disorders affecting the central nervous system. These conditions have a terrible impact on life quality of both patients and their families, but also on the costs associated to the society for their diagnosis and management. In order to reduce their impact on individuals and society, new better strategies for the diagnosis and monitoring of neurological disorders need to be considered. The main aim of this study is investigating the use of artificial intelligence techniques as a tool to help the doctors in the diagnosis and the monitoring of two specific neurological disorders (Parkinson’s disease and dystonia), for which no objective clinical assessments exist. The evolutionary algorithms are chosen as the artificial intelligence technique to evolve the best classifiers. The classifiers evolved by the chosen technique are then compared with those evolved by two popular well-known techniques: artificial neural network and support vector machine. All the evolved classifiers are not only able to distinguish among patients and healthy subjects but also among different subgroups of patients. For Parkinson’s disease: two different cognitive impairment subgroups of patients are considered, with the aim of an early diagnosis and a better monitoring. For dystonia: two kinds of dystonia patients are considered (organic and functional) to have a better insight in the division of the two groups. The results obtained for Parkinson’s disease are encouraging and evidenced some differences among the cognitive impairment subgroups. Dystonia results are not satisfactory at this stage, but the study presents some limitations that could be overcome in future work