Browsing by Author "Niazi, Imran Khan"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.