<div>The aim of this study was to compare a newly developed ultrathin wearable surface electromyography (sEMG) sensors patch (patent pending, inventors: Lee & Malandraki) (i.e., experimental sensors) to commercially available and widely-used sEMG sensors (i.e., conventional sensors) in monitoring submental muscle activity during swallowing in healthy older adults. A randomized crossover design was employed to compare the performance of the experimental sensors with the performance of conventional snap-on sensors. Forty healthy older adults participated (24F; age range 53-85). Participants completed the same experimental protocol with both sensor types in a counterbalanced order. Swallow trials completed with both types of sensors included 5 trials of 5ml and 10ml water swallows. Comparisons were made on: a) signal related factors (i.e., signal-to-noise ratio, baseline amplitude, normalized amplitude of the swallow trials, and duration of sEMG burst during swallow trials); and b) safety and preclinical factors (safety/adverse effects, efficiency, and satisfaction/comfort).</div><div><br></div><div><div>In terms of signal related factors (Aim 1), we hypothesized that the signal-to-noise ratio and baseline amplitude values acquired using the experimental sensors will not be inferior to the ones acquired using the conventional sensors. These hypotheses were tested using non-inferiority tests. Moreover, we hypothesized that the normalized amplitude values and the sEMG burst duration during swallow trials will be comparable/equivalent between the two sensor types. These hypotheses were tested using equivalency tests. In terms of safety and pre-clinical factors</div><div>(Aim 2), we predicted that no adverse effects will be reported after using either type of sensors. We also hypothesized that sensor placement will be more efficient, and satisfaction/comfort level will be higher with the experimental sensors. These hypotheses were tested using paired t-tests.</div></div><div><br></div><div><div>Overall, the findings supported our hypotheses for Aim 1. Results showed that the experimental sensors did not perform inferiorly to the conventional sensors based on signal-tonoise ratio (left sensors: t(39) = 3.95, p <0.0002; right sensors: t(39) = 2.66, <i>p <0.0056</i>) and baseline amplitude values (left sensors: t(39) = -7.72, p <<i>0.0001</i>; right sensors: t(39) = -7.43, <i>p</i><<i>0.0001</i>). The normalized amplitude values were deemed equivalent for all swallow trials (5ml left: t_u = 4.25, t_l = -6.22; overall <i>p-value <0.0001</i>; 5ml right: t_u = 2.07, t_l = -4.06; overall <i>p-value <0.0224</i>; 10ml left: t_u = 5.49, t_l = -7.20; overall <i>p-value <0.0001</i>; 10ml right: t_u = 3.36 t_l = -5.28; overall <i>p-value <0.0012</i>).The duration of sEMG burst was also deemed equivalent for all variables (5ml left: t_u = 9.48, t_l = -7.25; overall <i>p-value <0.0001</i>; 5ml right: t_u = 9.03, t_l = -6.35; overall <i>p-value <0.0001</i>; 10ml left: t_u = 6.75, t_l = -6.11; <i>p-value <0.0001</i>; 10ml right: t_u = 6.58, t_l = -6.23; overall <i>p-value < 0.0001</i>).</div></div><div><br></div><div><div>In terms of safety and adverse effects (Aim 2, hypothesis #1), mild redness and itchiness occurred with the conventional sensors in six participants, whereas only one participant reported itchiness with the experimental sensors. No redness or skin irritation was observed or reported by any of the participants after the removal of the experimental sensors. In terms of time efficiency of electrode placement (Aim 2, hypothesis #2), our hypothesis was not proven, as there were no statistically significant differences in the time it took to place both sensor types; (t(39) = 1.87, <i>p= 0.9657</i>). However, as hypothesized (Aim 2, hypothesis #3) satisfaction/comfort level was significantly higher with the experimental sensors than the conventional ones, albeit with a relatively small effect size, t(39) = 1.71, <i>p = 0.0476</i>, <i>d = 0.226</i>.</div></div><div><br></div><div><div>Taken together, these findings indicate that the newly developed ultrathin wearable sEMG sensors obtain comparable signal quality and signal parameters to conventional and widely used sEMG snap-on electrodes; have fewer adverse effects associated with them compared to the conventional sensors, and healthy older adults are highly satisfied and comfortable using them. Future research is warranted to optimize the wearable sEMG sensors, before clinical trials examining the effectiveness of these sensors in the treatment of dysphagia can be initiated.</div></div>

In the field of ergonomics, biomechanics, sports and rehabilitation muscle fatigue is regarded as an important aspect since muscle fatigue is considered to be one of the main reasons for musculoskeletal disorders. Classical signal processing techniques used to understand muscle behavior are mainly based on spectral based parameters estimation, and mostly applied during static contraction and the signal must be stationary within the analysis window; otherwise, the resulting spectrum will make little physical sense. Furthermore, the shape and size of the analysis window also directly affect the spectral estimation. But fatigue analysis in dynamic conditions is of utmost requirement because of its daily life applicability. It is really difficult to consistently find the muscle fatigue during dynamic contraction due to the inherent non-stationary nature and associated noise in the signal along with complex physiological changes in muscles. Nowadays, in addition to linear signal processing, different non-linear signal processing techniques are adopted to find out the consistent and robust indicator for muscle fatigue under dynamic condition considering the high degree of non-linearity (caused by functional interference between different muscles, changes of signal sources and paths to recording electrodes, variable electrode interface etc.) in the signal. In this work, various linear and nonlinear-non-stationary signal processing methods, applied on surface EMG signal for muscular fatigue analysis under dynamic contraction are studied. In present study, surface EMG (sEMG) signals are recorded from Biceps Brachii muscles from eight (N=8) physically active college students during dynamic lifting 7 kg load at the rate of 20 lifts/min till they become fatigue. EMG data is processed in two ways -1. taking the whole EMG response and 2. breaking into three ranges of contraction (0-45)o, (45-90)o and >90o, to study better response region. It is observed that in spectral estimation techniques auto-regressive (AR) based spectral estimation technique gives better frequency resolution than periodogram for small epochs, as AR is based on parametric estimation. Both the previous methods provide only the frequency information in the signal. In order to estimate the time varying nature of frequency content in a signal various time-frequency signal processing techniques are used like – Short Time-Fourier Transform (STFT), Smoothed pseudo Wigner-Ville (SPWD), Choi-William distribution (CWD), Continuous Wavelet Transform (CWT), Huang-Hilbert Transform (HHT) and Recurrence Quantification Analysis (RQA) are used. The last two techniques are used by considering the EMG signal as non-linear and non-stationary signals. Among these techniques, STFT is the simplest time-frequency analysis technique. But tradeoff between time and frequency resolution is the major constraint in STFT, therefore, a window length of 256 samples are considered in this study. In order to tackle time-frequency resolution problem different Cohen-class distribution techniques are used like SPWD and CWD, where the result is severely affected by the presence of interference terms which make its interpretation really difficult. Different adaptive filters are used in SPWD and CWD to suppress these interference terms during analysis. Among these time-frequency analysis techniques continuous wavelet transform provides the most accurate results in comparison to other time-frequency analysis techniques. Similar result is obtained in present study. This fatigue response is further improved using non-linear and non-stationary techniques like HHT and RQA. HHT shows less variation in frequency response than CWT analysis result. Percentage of determinism calculated using recurrence quantification analysis method is found to be more sensitive than mean frequency estimation. Therefore, non-linear and non-stationary signal processing techniques are to be better indicator of muscle fatigue during dynamic contraction.