Asim Waris

The effect of time on EMG classification of hand motions in able-bodied and transradial amputees

While several studies have demonstrated the short-term performance of pattern recognition systems, long-term investigations are very limited. In this study, we investigated changes in classification performance over time. Ten able-bodied individuals and six amputees took part in this study. EMG signals were recorded concurrently from surface and intramuscular electrodes, with intramuscular electrodes kept in the muscles for seven days. Seven hand motions were evaluated daily using linear discriminant analysis and the classification error quantified within (WCE) and between (BCE) days. BCE was computed for all possible combinations between the days. For all subjects, surface sEMG (7.2 ± 7.6%), iEMG (11.9 ± 9.1%) and cEMG (4.6 ± 4.8%) were significantly different (P < 0.001) from each other. A regression between WCE and days (1-7) was on average not significant implying that performance may be considered similar within each day. Regression between BCE and time difference (Df) in days was significant. The slope between BCE and Df (0-6) was significantly different from zero for sEMG (R2 = 89%) and iEMG (R2 = 95%) in amputees. Results indicate that performance continuously degrades as the time difference between training and testing day increases. Furthermore, for iEMG, performance in amputees was directly proportional to the size of the residual limb.

Effect of Threshold Values on the Combination of EMG Time Domain Features: Surface versus Intramuscular EMG

Abstract In myoelectric control, the calculation of a number of time domain features uses a threshold. However there is no consensus on the choice of the optimal threshold values. In this study, we investigate the effect of threshold selection on the classification for prosthetic use. Surface and intramuscular EMG were recorded concurrently from four forearm muscles on nine able-bodied subjects. Subjects were prompted to elicit comfortable and sustainable contractions corresponding to eight classes of motion. Four repetitions of three seconds were collected for each motion during medium level steady state contractions. The threshold for each feature was computed as a factor (R = 0:0.02:6) times the average root mean square of the baseline. For each threshold value, classification error was quantified using linear discriminant analysis (LDA) and k-nearest neighbor (KNN, k = 4) first for each individual feature and when combined. Three-way ANOVA revealed no significant difference between surface and intramuscular EMG (P = 0.997). However there was a significant difference between the features (P = 0.006) and between the classifiers (P

Multiday EMG-Based Classification of Hand Motions with Deep Learning Techniques

Pattern recognition of electromyography (EMG) signals can potentially improve the performance of myoelectric control for upper limb prostheses with respect to current clinical approaches based on direct control. However, the choice of features for classification is challenging and impacts long-term performance. Here, we propose the use of EMG raw signals as direct inputs to deep networks with intrinsic feature extraction capabilities recorded over multiple days. Seven able-bodied subjects performed six active motions (plus rest), and EMG signals were recorded for 15 consecutive days with two sessions per day using the MYO armband (MYB, a wearable EMG sensor). The classification was performed by a convolutional neural network (CNN) with raw bipolar EMG samples as the inputs, and the performance was compared with linear discriminant analysis (LDA) and stacked sparse autoencoders with features (SSAE-f) and raw samples (SSAE-r) as inputs. CNN outperformed (lower classification error) both LDA and SSAE-r in the within-session, between sessions on same day, between the pair of days, and leave-out one-day evaluation (p < 0.001) analyses. However, no significant difference was found between CNN and SSAE-f. These results demonstrated that CNN significantly improved performance and increased robustness over time compared with standard LDA with associated handcrafted features. This data-driven features extraction approach may overcome the problem of the feature calibration and selection in myoelectric control.