Browsing by Author "Waris, Asim"

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    A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN
    (MDPI, 2020) Waris, Asim; ur Rehman, Muhammad Zia; Niazi, Imran Khan; Jochumsen, Mads; Englehart, Kevin; Jensen, Winnie; Haavik, Heidi; Kamavuako, Ernest Nlandu
    Recent developments in implantable technology, such as high-density recordings, wireless transmission of signals to a prosthetic hand, may pave the way for intramuscular electromyography (iEMG)-based myoelectric control in the future. This study aimed to investigate the real-time control performance of iEMG over time. A novel protocol was developed to quantify the robustness of the real-time performance parameters. Intramuscular wires were used to record EMG signals, which were kept inside the muscles for five consecutive days. Tests were performed on multiple days using Fitts’ law. Throughput, completion rate, path efficiency and overshoot were evaluated as performance metrics using three train/test strategies. Each train/test scheme was categorized on the basis of data quantity and the time difference between training and testing data. An artificial neural network (ANN) classifier was trained and tested on (i) data from the same day (WDT), (ii) data collected from the previous day and tested on present-day (BDT) and (iii) trained on all previous days including the present day and tested on present-day (CDT). It was found that the completion rate (91.6 ± 3.6%) of CDT was significantly better (p < 0.01) than BDT (74.02 ± 5.8%) and WDT (88.16 ± 3.6%). For BDT, on average, the first session of each day was significantly better (p < 0.01) than the second and third sessions for completion rate (77.9 ± 14.0%) and path efficiency (88.9 ± 16.9%). Subjects demonstrated the ability to achieve targets successfully with wire electrodes. Results also suggest that time variations in the iEMG signal can be catered by concatenating the data over several days. This scheme can be helpful in attaining stable and robust performance.
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    Multiday Evaluation of Techniques for EMG-Based Classification of Hand Motions
    (Institute of Electrical and Electronics Engineers, 2019-07) Waris, Asim; Niazi, Imran K.; Jamil, Mohsin; Englehart, Kevin; Jensen, Winnie; Kamavuako, Ernest Nlandu
    Currently, most of the adopted myoelectric schemes for upper limb prostheses do not provide users with intuitive control. Higher accuracies have been reported using different classification algorithms but investigation on the reliability over time for these methods is very limited. In this study, we compared for the first time the longitudinal performance of selected state-of-the-art techniques for electromyography (EMG) based classification of hand motions. Experiments were conducted on ten able-bodied and six transradial amputees for seven continuous days. Linear discriminant analysis (LDA), artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor (KNN), and decision trees (TREE) were compared. Comparative analysis showed that the ANN attained highest classification accuracy followed by LDA. Three-way repeated ANOVA test showed a significant difference (P <; 0.001) between EMG types (surface, intramuscular, and combined), days (1-7), classifiers, and their interactions. Performance on the last day was significantly better (P <; 0.05) than the first day for all classifiers and EMG types. Within-day, classification error (WCE) across all subject and days in ANN was: surface (9.12 ± 7.38%), intramuscular (11.86 ± 7.84%), and combined (6.11 ± 7.46%). The between-day analysis in a leave-one-day-out fashion showed that the ANN was the optimal classifier surface (21.88 ± 4.14%), intramuscular (29.33 ± 2.58%), and combined (14.37 ± 3.10%). Results indicate that within day performances of classifiers may be similar but over time, it may lead to a substantially different outcome. Furthermore, training ANN on multiple days might allow capturing time-dependent variability in the EMG signals and thus minimizing the necessity for daily system recalibration.
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    Multiday Evaluation of Techniques for EMG-Based Classification of Hand Motions
    (Institute of Electrical and Electronics Engineers, 2019-07) Waris, Asim; Niazi, Imran K.; Jamil, Mohsin; Englehart, Kevin; Jensen, Winnie; Kamavuako, Ernest Nlandu
    Currently, most of the adopted myoelectric schemes for upper limb prostheses do not provide users with intuitive control. Higher accuracies have been reported using different classification algorithms but investigation on the reliability over time for these methods is very limited. In this study, we compared for the first time the longitudinal performance of selected state-of-the-art techniques for electromyography (EMG) based classification of hand motions. Experiments were conducted on ten able-bodied and six transradial amputees for seven continuous days. Linear discriminant analysis (LDA), artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor (KNN), and decision trees (TREE) were compared. Comparative analysis showed that the ANN attained highest classification accuracy followed by LDA. Three-way repeated ANOVA test showed a significant difference (P <; 0.001) between EMG types (surface, intramuscular, and combined), days (1-7), classifiers, and their interactions. Performance on the last day was significantly better (P <; 0.05) than the first day for all classifiers and EMG types. Within-day, classification error (WCE) across all subject and days in ANN was: surface (9.12 ± 7.38%), intramuscular (11.86 ± 7.84%), and combined (6.11 ± 7.46%). The between-day analysis in a leave-one-day-out fashion showed that the ANN was the optimal classifier surface (21.88 ± 4.14%), intramuscular (29.33 ± 2.58%), and combined (14.37 ± 3.10%). Results indicate that within day performances of classifiers may be similar but over time, it may lead to a substantially different outcome. Furthermore, training ANN on multiple days might allow capturing time-dependent variability in the EMG signals and thus minimizing the necessity for daily system recalibration.
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    On the robustness of real-time myoelectric control investigations: a multiday Fitts’ law approach
    (IOP Publishing, 2019) Waris, Asim; Mendez, Irene; Englehart, Kevin; Jensen, Winnie; Kamavuako, Ernest Nlandu
    Objective. Real-time myoelectric experimental protocol is considered as a means to quantify usability of myoelectric control schemes. While usability should be considered over time to assure clinical robustness, all real-time studies reported thus far are limited to a single session or day and thus the influence of time on real-time performance is still unexplored. In this study, the aim was to develop a novel experimental protocol to quantify the effect of time on real-time performance measures over multiple days using a Fitts' law approach. Approach. Four metrics: throughput, completion rate, path efficiency and overshoot, were assessed using three train-test strategies: (i) an artificial neural network (ANN) classifier was trained on data collected from the previous day and tested on present day (BDT) (ii) trained and tested on the same day (WDT) and (iii) trained on all previous days including present day and tested on present day (CDT) in a week-long experimental protocol. Main results. It was found that on average, the completion rate (98.37%  ±  1.47%) of CDT was significantly better (P  <  0.01) than that of BDT (86.25%  ±  3.46%) and WDT (94.22%  ±  2.74%). The throughput (0.40  ±  0.03 bits s−1) of CDT was significantly better (P  =  0.001) than that of BDT (0.38  ±  0.03 bits s−1). Offline analysis showed a different trend due to the difference in the training strategies. Significance. Results suggest that increasing the size of the training set over time can be beneficial to assure robust performance of the system over time.
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    The effect of time on EMG classification of hand motions in able-bodied and transradial amputees
    (Elsevier, 2018-06) Waris, Asim; Niazi, Imran Khan; Jamil, Mohsin; Gilani, Omer; Englehart, Kevin; Jensen, Winnie; Shafique, Muhammad; Kamavuako, Ernest Nlandu
    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.