Decoding of Multiple Wrist and Hand Movements Using a Transient EMG Classifier

: The design of prosthetic controllers by means of neurophysiological signals still poses a crucial challenge to bioengineers. State of the art of electromyographic (EMG) continuous pattern recognition controllers rely on the questionable assumption that repeated muscular contractions produce repeatable patterns of steady-state EMG signals. Conversely, we propose an algorithm that decodes wrist and hand movements by processing the signals that immediately follow the onset of contraction (i.e., the transient EMG). We collected EMG data from the forearms of 14 non-amputee and 5 transradial amputee participants while they performed wrist flexion/extension, pronation/supination, and four hand grasps (power, lateral, bi-digital, open). We firstly identified the combination of wrist and hand movements that yielded the best control performance for the same participant (intra-subject classification). Then, we assessed the ability of our algorithm to classify participant data that were not included in the training set (cross-subject classification). Our controller achieved a median accuracy of ~96% with non-amputees, while it achieved heterogeneous outcomes with amputees, with a median accuracy of ~89%. Importantly, for each amputee, it produced at least one acceptable combination of wrist-hand movements (i.e., with accuracy >85%). Regarding the cross-subject classifier, while our algorithm obtained promising results with non-amputees (accuracy up to ~80%), they were not as good with amputees (accuracy up to ~35%), possibly suggesting further assessments with domain-adaptation strategies. In general, our offline outcomes, together with a preliminary online assessment, support the hypothesis that the transient EMG decoding could represent a viable pattern recognition strategy, encouraging further online assessments

Decoding of Multiple Wrist and Hand Movements Using a Transient EMG Classifier

The design of prosthetic controllers by means of neurophysiological signals still poses a crucial challenge to bioengineers. State of the art of electromyographic (EMG) continuous pattern recognition controllers rely on the questionable assumption that repeated muscular contractions produce repeatable patterns of steady-state EMG signals. Conversely, we propose an algorithm that decodes wrist and hand movements by processing the signals that immediately follow the onset of contraction (i.e., the \textit {transient} EMG). We collected EMG data from the forearms of 14 non-amputee and 5 transradial amputee participants while they performed wrist flexion/extension, pronation/supination, and four hand grasps (power, lateral, bi-digital, open). We firstly identified the combination of wrist and hand movements that yielded the best control performance for the same participant (intra-subject classification). Then, we assessed the ability of our algorithm to classify participant data that were not included in the training set (cross-subject classification). Our controller achieved a median accuracy of 96% with non-amputees, while it achieved heterogeneous outcomes with amputees, with a median accuracy of 89%. Importantly, for each amputee, it produced at least one \textit {acceptable} combination of wrist-hand movements (i.e., with accuracy >85%). Regarding the cross-subject classifier, while our algorithm obtained promising results with non-amputees (accuracy up to 80%), they were not as good with amputees (accuracy up to 35%), possibly suggesting further assessments with domain-adaptation strategies. In general, our offline outcomes, together with a preliminary online assessment, support the hypothesis that the transient EMG decoding could represent a viable pattern recognition strategy, encouraging further online assessments

Disulfiram moderately restores impaired hepatic redox status of rats subchronically exposed to cadmium

Examination of cadmium (Cd) toxicity and disulfiram (DSF) effect on liver was focused on oxidative stress (OS), bioelements status, morphological and functional changes. Male Wistar rats were intraperitoneally treated with 1mg CdCl2/kg BW/day; orally with 178.5 mg DSF/kg BW/day for 1, 3, 10 and 21 days; and co-exposed from 22nd to 42nd day. The co-exposure nearly restored previously suppressed total superoxide dismutase (SOD), catalase (CAT) and increased glutathione peroxidase (GPx) activities; increased previously reduced glutathione reductase (GR) and total glutathione-S-transferase (GST) activities; reduced previously increased superoxide anion radical (O-2(center dot-)) and malondialdehyde (MDA) levels; increased zinc (Zn) and iron (Fe), and decreased copper (Cu) (yet above control value), while magnesium (Mg) was not affected; and decreased serum alanine aminotransferases (ALT) levels. Histopathological examination showed signs of inflammation process as previously demonstrated by exposure to Cd. Overall, we ascertained partial liver redox status improvement, compared with the formerly Cd-induced impact

Design and development of a multi-subject transfer learning algorithm based on EMG transient

Research pattern recognition myoelectric controls that include several movements require too much extensive algorithm training sessions to be clinically suitable. The patient has to reproduce different movements several times to build a data set for training the algorithm and the length of the process is proportional to the number of movements to train. In order to reduce the training time, we introduced transfer learning to the already existing machine learning algorithm to build a general common model from different subjects that can be tailored to a new user. The chosen transfer learning algorithm is Canonical Correlation Analysis (CCA) for its simplicity and effectiveness. This technique makes two views of the same set of objects and projects them onto a different space in which they are maximally correlated. It is an attempt to create a unified-style-space, i.e., attempt to make all training users' style similar to that of the expert. The novel user requires minimal training effort to obtain the projection matrices which will transform the user's data into a space where it is maximally correlated to the expert user. The data points are extracted at the moment of the EMG transient. This way there is no continuous classification, movements are classified only when an Onset Detection Algorithm (ODA) recognizes a transient, which makes the whole system less prone to errors. Additionally, since the contraction precedes the actual movement, the response time of the transient classifier is faster than that of a conventional continuous classifier. In this work, two different data sets were explored, surface and intramuscular EMG data set. Intramuscular EMG data set had signals recorded from 5 channels and divided into 5 classes, with 4 repetitions per each class. After processing the data, several subjects had to be removed due to noise and inconsistent contractions, leaving only 4 subjects that were considered for training and testing. On the other side, surface EMG data set had 8 channels and 8 classes with 20 repetitions per each class. The number of subjects which were included into training and testing is 15. Firstly, the chosen machine learning algorithm, Support Vector Machine (SVM) was trained on each subject individually from both data sets in a form of crossvalidation, leaving one repetition per each movement for testing (CV LORO). Intramuscular EMG data set had an average accuracy across all subjects around 88\%, while surface EMG data set had more than 90\% average accuracy. It is expected that the surface EMG data set has greater accuracy due to more data available, for each class surface data set had 20 data points compared to 4 for intramuscular data set. Subsequently, SVM was trained on data from all subjects combined. For finding the threshold for ODA, four different methods were observed: finding one threshold for all training data, finding individual threshold for each subject in the training set and choosing the minimum/median threshold for the test subject and choosing the optimal threshold for each subject whether it be in training or testing set. Here crossvalidation was implemented by leaving one subject for testing and training the SVM on the rest of the subjects (CV LOSO). Intramuscular data set under-performed with the average accuracy of 45\%. Class "Three digit pinch" had an accuracy of less then 10\%. This is due to low number of data and subjects, hence this data set was no longer considered for future testing. Surface EMG data set had similar average accuracy for all four different methods of finding the threshold for the ODA and was around 57\%. The maximum accuracy achieved was 59.7\% when median threshold was used for ODA. This was still much lower compared to the accuracy obtained from SVM trained on each individual subjects, which was over 90\%, therefore transfer learning algorithm was introduced. Canonical Correlation Analysis was used on the feature set before the training of the SVM. Due to the use of the EMG transient there is less data compared to when EMG steady state is used, which means less data to adequately represent the feature space of the new user. To this end data was oversampled by extracting random points in the range of each feature. Without oversampling, the surface EMG data set had very low performances, with average accuracy of 29\% for when 5 repetitions per movement was used for calibration. The more the data was oversampled, the greater the performance was, with 76.5\% of average accuracy for when 5 repetitions per movement were oversampled to 100 repetitions per movement. This is grater than the accuracy obtained with SVM without CCA. However, the accuracy obtained from SVM trained on each individual subject with CV LORO when only 5 repetitions per movement was used for training and testing is slightly over 80\%, which means that the CCA does not increase the performance of the SVM enough. Upon observing the accuracies per class, it was noticed that some classes have very low performances, like the "Side grip" class with around 20\% class accuracy. Therefore 3 classes were removed: "Pronation", "Supination" and "Side grip". CV LORO was implemented again on the surface EMG data set with the reduced number of classes, as well as the CV LOSO with CCA and the performances were compared. It has been found that for low number of calibration data for CV LOSO and training data for CV LORO (3, 4, 5 samples per class) and large oversampling (up to 100 samples per class) the CV LOSO with CCA outperforms the CV LORO by few percentages. To further try to increase the performance, the SMOTE algorithm was introduced as a data augmentation method. The calibration data was oversampled from 5 to 100 samples per class and performance of the SVM was compared to that of the SVM with data oversampling with random extraction. Both methods reached to around 90\% average accuracy, meaning that the oversampling method does not contribute to the performance of the CCA or the SVM. Finally, an SVM adaptation was implemented. The SVM was retrained with the support vectors obtained from the SVM trained on subjects in the training set and the oversampled calibration data from the test subject. Calibration data was oversampled from 5 to 100 samples per class with SMOTE algorithm. The adapted SVM with and without CCA was compared, with former outperforming the later with more than 5\%, showing the benefits of the CCA. Maximum average accuracy obtained with 5 repetitions per movement for calibration was achieved when SMOTE algorithm was used to oversample data to 100 samples per class, using CCA as transfer learning algorithm and SVM adaptation, reaching almost 92\%. This is by 7\% greater than the accuracy obtained with CV LORO, proving the applicability of these algorithms. The CCA and SVM adaptation was performed again to surface EMG data set when only class "Side grip" was removed. The highest performance is for adapted SVM with CCA when calibration data was oversampled from 5 to 100 samples per class with average accuracy of 87.4\%. Considering the large number of classes (in this case 7), and that the data from different subjects was used for training, this accuracy quite sufficient. In conclusion, transfer learning algorithm CCA and SVM adaptation contribute greatly in increasing the performance of the machine learning algorithm SVM when it comes to training data from different sources. Using CCA will significantly reduce the training time of the novel prosthetic user, increasing its applicability

Möjligheter och utmaningar med surfplatta i matematikundervisning

Elevers användning av surfplatta i matematikundervisningen har ökat, men matematikämnet är det ämnet där elever använder digitala verktyg som minst. Syftet med studien är att utifrån lärarens perspektiv undersöka vilken betydelse surfplattan har som ett läromedel i matematikundervisningen för lågstadieelever. För att få svar på studiens frågeställning användes en kvalitativ metod i form av intervjuer. Till denna studie var valet att använda SAMR-modellen som teoristöd och modellen användes för analys av den data som framkom av intervjuerna. Resultatet av denna studie synliggör vilka möjligheter respektive utmaningar det kan finnas för elever när de använder surfplattan. Det framgick även att lärarna såg fler möjligheter än utmaningar med surfplattan

Möjligheter och utmaningar med surfplatta i matematikundervisning

Elevers användning av surfplatta i matematikundervisningen har ökat, men matematikämnet är det ämnet där elever använder digitala verktyg som minst. Syftet med studien är att utifrån lärarens perspektiv undersöka vilken betydelse surfplattan har som ett läromedel i matematikundervisningen för lågstadieelever. För att få svar på studiens frågeställning användes en kvalitativ metod i form av intervjuer. Till denna studie var valet att använda SAMR-modellen som teoristöd och modellen användes för analys av den data som framkom av intervjuerna. Resultatet av denna studie synliggör vilka möjligheter respektive utmaningar det kan finnas för elever när de använder surfplattan. Det framgick även att lärarna såg fler möjligheter än utmaningar med surfplattan

Cannabis as a possible treatment for spasticity in multiple sclerosis

© 2016, University of Kragujevac, Faculty of Science. All Rights Reserved. The therapeutic potential of cannabis has been known for centuries. Cannabinoids express their effects throughtwotypes of receptors, cannabinoid receptor 1 (CB1) and cannabinoid receptor 2 (CB2). Present studies indicate that cannabis-based drugs can make a positive impact in the treatment of different diseases. For many years, multiple sclerosis patients have self-medicated with illegal street cannabis to alleviate spasticity, a common and debilitating symptom that impairs quality of life. Nabiximols is the cannabis-based medicine approved in many countries as an add-on therapy for symptom improvement in patients with spasticity who have not responded adequately to other medications. Adverse events such as dizziness, diarrhoea, fatigue, nausea, headache and somnolence occur quite frequently with nabiximols, but they are generally of mild-to-moderate intensity and their incidence can be markedly reduced by gradual uptitration. The prerequisite for the therapeutic use of cannabis in Serbia arerequires legal clarifi cation for the use of the drug in a clinical environment

Cannabis as a Possible Treatment for Spasticity in Multiple Sclerosis / Kanabis Kao Moguci Tretman U Lecenju Spasticnosti Kod Multiple Skleroze

The therapeutic potential of cannabis has been known for centuries. Cannabinoids express their effects through two types of receptors, cannabinoid receptor 1 (CB1) and cannabinoid receptor 2 (CB2). Present studies indicate that cannabis-based drugs can make a positive impact in the treatment of different diseases. For many years, multiple sclerosis patients have self-medicated with illegal street cannabis to alleviate spasticity, a common and debilitating symptom that impairs quality of life