<p> An accurate estimation of muscle fatigue is critical for adaptive control of existing assistive devices, such as an exoskeleton, prosthesis, and functional electrical stimulation (FES)-based neuroprostheses. However, the estimation of muscle fatigue using surface electromyography (sEMG) for a long duration of time becomes challenging due to loosening of sEMG sensors, sweating, and other accidental failures. These problems can be potentially solved by forecasting future sEMG signals using initially recorded high-quality data points. For the first time, we attempt to forecast the fatigue-induced electromyography signal using the initial sEMG recorded for a shorter interval of time, during biceps curl with weights of 1 kg, 2 kg, 3 kg, and 4 kg. An attention-based deep CNN-BiLSTM neural network model that captures input sEMG dynamics to forecast future sEMG signals corresponding to fatigue state was trained and tested. An average mean absolute percentage error (MAPE) of 26.7% between forecasted and recorded sEMG was observed across eight subjects, five muscles, and four weights. In addition, the time domain features like integrated EMG (IEMG), root-mean-square (RMS) value, and variance of EMG (VEMG) were compared between forecasted and recorded sEMG (fatigue state), which yielded an average MAPE of 8%, 19.2%, and 31.7%, across eight subjects, five muscles, and four weights, for (IEMG and MAV), RMS, and (VEMG and SSI) respectively. The results encourage combining the proposed approach with wearable technology for forecasting fatigue-induced sEMG to drive stimulation devices like FES and robotic devices. </p>
Abstract The nasal dominance (ND) determination is crucial for nasal synchronized ventilator, optimum nasal drug delivery, identifying brain hemispheric dominance, nasal airway obstruction surgery, mindfulness breathing, and for possible markers of a conscious state. Given these wider applications of ND, it is interesting to understand the patterns of ND with varying temperature and respiration rates. In this paper, we propose a method which measures peak-to-peak temperature oscillations (difference between end-expiratory and end-inspiratory temperature) for the left and right nostrils during nasal breathing. These nostril-specific temperature oscillations are further used to calculate the nasal dominance index (NDI), nasal laterality ratio (NLR), inter-nostril correlation, and mean of peak-to-peak temperature oscillation for inspiratory and expiratory phase at (1) different ambient temperatures of 18 °C, 28 °C, and 38 °C and (2) at three different respiration rate of 6 bpm, 12 bpm, and 18 bpm. The peak-to-peak temperature ( T pp ) oscillation range (averaged across participants; n = 8) for the left and right nostril were 3.80 ± 0.57 °C and 2.34 ± 0.61 °C, 2.03 ± 0.20 °C and 1.40 ± 0.26 °C, and 0.20 ± 0.02 °C and 0.29 ± 0.03 °C at the ambient temperature of 18 °C, 28 °C, and 38 °C respectively (averaged across participants and respiration rates). The NDI and NLR averaged across participants and three different respiration rates were 35.67 ± 5.53 and 2.03 ± 1.12; 8.36 ± 10.61 and 2.49 ± 3.69; and −25.04 ± 14.50 and 0.82 ± 0.54 at the ambient temperature of 18 °C, 28 °C, and 38 °C respectively. The Shapiro–Wilk test, and non-parametric Friedman test showed a significant effect of ambient temperature conditions on both NDI and NLR. No significant effect of respiration rate condition was observed on both NDI and NLR. The findings of the proposed study indicate the importance of ambient temperature while determining ND during the diagnosis of breathing disorders such as septum deviation, nasal polyps, nosebleeds, rhinitis, and nasal fractions, and in the intensive care unit for nasal synchronized ventilator.
Morphometric study of submandibular salivary gland in 30 normal human fetuses at different stages of development was done.Morphometric parameters studied in fetal submandibular glands were length and breadth of glands.It was observed that with increase in gestational age of fetuses, there was gradual increase in morphometric parameters of fetal submandibular salivary glands of both sides.
Identification of the toe off event is critical in many gait applications. Accelerometer threshold-based algorithms lack adaptability and have not been tested for transitions between locomotion states. We describe a new approach for toe off identification using one accelerometer in over ground and ramp walking, including transitions. The method uses invariant foot acceleration features in the segment of gait, where toe off is probable. Wavelet analysis of foot acceleration is used to derive a unique feature in a particular frequency band, yielding estimated toe off occurrence. We tested the new method for five conditions: over ground walking (W), ramp ascending (RA), ramp descending (RD); transitions between states (W-RA, W-RD). Mean absolute estimation error was 17.4 ± 12.5, 13.8 ± 8.5, and 22.0 ± 16.4 ms for steady states W, RA, and RD, 20.1 ± 15.5, and 17.1 ± 13.7 ms for transitions W-RA and W-RD, respectively. Algorithm performance was equivalent across all pairs of transition and locomotion state except between RA and RD ( p = 0.03), demonstrating adaptability. The db1 wavelet outperformed db2 across states and transitions (p < 0.01). The presented algorithm is a simple, robust approach for toe off detection.
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