Dataset Information

Strain-Insensitive Elastic Surface Electromyographic (sEMG) Electrode for Efficient Recognition of Exercise Intensities.

ABSTRACT: Surface electromyography (sEMG) sensors are widely used in the fields of ergonomics, sports science, and medical research. However, current sEMG sensors cannot recognize the various exercise intensities efficiently because of the strain interference, low conductivity, and poor skin-conformability of their electrodes. Here, we present a highly conductive, strain-insensitive, and low electrode-skin impedance elastic sEMG electrode, which consists of a three-layered structure (polydimethylsiloxane/galinstan + polydimethylsiloxane/silver-coated nickel + polydimethylsiloxane). The bottom layer of the electrode consists of vertically conductive magnetic particle paths, which are insensitive to stretching strain, collect sEMG charge from human skin, and finally transfer it to processing circuits via an intermediate layer. Our skin-friendly electrode exhibits high conductivity (0.237 and 1.635 m?.cm resistivities in transverse and longitudinal directions, respectively), low electrode-skin impedance (47.23 k? at 150 Hz), excellent strain-insensitivity (10% change of electrode-skin impedance within the 0%-25% strain range), high fatigue resistance (>1500 cycles), and good conformability with skin. During various exercise intensities, the signal-to-noise ratio (SNR) of our electrode increased by 22.53 dB, which is 206% and 330% more than that of traditional Ag/AgCl and copper electrode, respectively. The ability of our electrode to efficiently recognize various exercise intensities confirms its great application potential for the field of sports health.

SUBMITTER: Tang D

PROVIDER: S-EPMC7143104 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Strain-Insensitive Elastic Surface Electromyographic (sEMG) Electrode for Efficient Recognition of Exercise Intensities.

Tang Daxiu D Yu Zhe Z He Yong Y Asghar Waqas W Zheng Ya-Nan YN Li Fali F Shi Changcheng C Zarei Roozbeh R Liu Yiwei Y Shang Jie J Liu Xiang X Li Run-Wei RW

Micromachines 20200225 3

Surface electromyography (sEMG) sensors are widely used in the fields of ergonomics, sports science, and medical research. However, current sEMG sensors cannot recognize the various exercise intensities efficiently because of the strain interference, low conductivity, and poor skin-conformability of their electrodes. Here, we present a highly conductive, strain-insensitive, and low electrode-skin impedance elastic sEMG electrode, which consists of a three-layered structure (polydimethylsiloxane/ ...[more]

PMID: 32106451

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Project description:The aim of this study was to compare the effect of pelvic floor muscle training with surface electromyographic (sEMG) biofeedback (BF group) and Pilates exercises (P group) on the bioelectrical activity of pelvic floor muscles in women with stress urinary incontinence. The other aim aim was to compare changes in voiding diaries and scores on quality of life questionnaire against baseline values and between the groups. Women in the BF group (n = 18) participated in pelvic floor muscle training with sEMG biofeedback; the P group (n = 13) participated in basic level Pilates workouts. Both protocols were continued for eight weeks. Voiding diary, quality of life and electromyographic characteristics of the pelvic floor muscles were assessed at the three-time points: at baseline, after eight weeks' training, and at month six post-training. The sEMG activity of the pelvic floor muscles was tested during five trials in two positions. There was no marked improvement in bioelectrical activity of the pelvic floor muscles during contraction following training with sEMG biofeedback or Pilates exercises. Following eight weeks of sEMG biofeedback training, a decrease was noted in resting bioelectrical activity of pelvic floor muscles and during relaxation after sustained contraction but only in supine-lying. No such effect was observed in the Pilates group. In the BF group, the number of incontinence episodes after end of treatment (timpepoints: 1vs. 2) and at six month follow-up (timpepoints: 1vs. 3) decreased by 68.5% and 89.3%, respectively. The respective values in the P group were 78.6%, and 86.4%. The intergroup differences did not reach the level of statistical significance. As regards the quality of life, the questionnaire demonstrated that Pilates exercises had significantly better effects compared to biofeedback training both at the end of the eight-week exercise program and (p = 0.003) and at six month follow-up (p = 0.0009). The International Consultation on Incontinence Questionnaire-Short Form (ICIQ- SF) showed comparable efficacy of Pilates exercises and training with sEMG biofeedback. Intragroup improvements in micturition frequency, incontinence (leakage) episodes, and nocturia frequency were comparable. Alleviation of urinary incontinence symptoms was comparable in both groups, whereas the improvement in the quality of life was more notable in the Pilates group. The obtained results failed to demonstrate the superiority of any of the two methods regarding the bioelectrical activity of pelvic floor muscles in patients with stress urinary incontinence.

Project description:Hand gesture recognition tasks based on surface electromyography (sEMG) are vital in human-computer interaction, speech detection, robot control, and rehabilitation applications. However, existing models, whether traditional machine learnings (ML) or other state-of-the-arts, are limited in the number of movements. Targeting a large number of gesture classes, more data features such as temporal information should be persisted as much as possible. In the field of sEMG-based recognitions, the recurrent convolutional neural network (RCNN) is an advanced method due to the sequential characteristic of sEMG signals. However, the invariance of the pooling layer damages important temporal information. In the all convolutional neural network (ACNN), because of the feature-mixing convolution operation, a same output can be received from completely different inputs. This paper proposes a concatenate feature fusion (CFF) strategy and a novel concatenate feature fusion recurrent convolutional neural network (CFF-RCNN). In CFF-RCNN, a max-pooling layer and a 2-stride convolutional layer are concatenated together to replace the conventional simple dimensionality reduction layer. The featurewise pooling operation serves as a signal amplitude detector without using any parameter. The feature-mixing convolution operation calculates the contextual information. Complete evaluations are made on both the accuracy and convergence speed of the CFF-RCNN. Experiments are conducted using three sEMG benchmark databases named DB1, DB2 and DB4 from the NinaPro database. With more than 50 gestures, the classification accuracies of the CFF-RCNN are 88.87% on DB1, 99.51% on DB2, and 99.29% on DB4. These accuracies are the highest compared with reported accuracies of machine learnings and other state-of-the-art methods. To achieve accuracies of 86%, 99% and 98% for the RCNN, the training time are 2353.686 s, 816.173 s and 731.771 s, respectively. However, for the CFF-RCNN to reach the same accuracies, it needs only 1727.415 s, 542.245 s and 576.734 s, corresponding to a reduction of 26.61%, 33.56% and 21.19% in training time. We concluded that the CFF-RCNN is an improved method when classifying a large number of hand gestures. The CFF strategy significantly improved model performance with higher accuracy and faster convergence as compared to traditional RCNN.

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Project description:BackgroundExercise challenges people with type 1 diabetes in controlling their glucose concentration (GC). A multivariable adaptive artificial pancreas (MAAP) may lessen the burden.MethodsThe MAAP operates without any user input and computes insulin based on continuous glucose monitor and physical activity signals. To analyze performance, 18 60-h closed-loop experiments with 96 exercise sessions with three different protocols were completed. Each day, the subjects completed one resistance and one treadmill exercise (moderate continuous training [MCT] or high-intensity interval training [HIIT]). The primary outcome is time spent in each glycemic range during the exercise + recovery period. Secondary measures include average GC and average change in GC during each exercise modality.ResultsThe GC during exercise + recovery periods were within the euglycemic range (70-180?mg/dL) for 69.9% of the time and within a safe glycemic range for exercise (70-250?mg/dL) for 93.0% of the time. The exercise sessions are defined to begin 30?min before the start of exercise and end 2?h after start of exercise. The GC were within the severe hypoglycemia (<55?mg/dL), moderate hypoglycemia (55-70?mg/dL), moderate hyperglycemia (180-250?mg/dL), and severe hyperglycemia (>250?mg/dL) for 0.9%, 1.3%, 23.1%, and 4.8% of the time, respectively. The average GC decline during exercise differed with exercise type (P?=?0.0097) with a significant difference between the MCT and resistance (P?=?0.0075). To prevent large GC decreases leading to hypoglycemia, MAAP recommended carbohydrates in 59% of MCT, 50% of HIIT, and 39% of resistance sessions.ConclusionsA consistent GC decline occurred in exercise and recovery periods, which differed with exercise type. The average GC at the start of exercise was above target (185.5?±?56.6?mg/dL for MCT, 166.9?±?61.9?mg/dL for resistance training, and 171.7?±?41.4?mg/dL HIIT), making a small decrease desirable. Hypoglycemic events occurred in 14.6% of exercise sessions and represented only 2.22% of the exercise and recovery period.

Dataset Information

Strain-Insensitive Elastic Surface Electromyographic (sEMG) Electrode for Efficient Recognition of Exercise Intensities.

Publications

Strain-Insensitive Elastic Surface Electromyographic (sEMG) Electrode for Efficient Recognition of Exercise Intensities.

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