How to use one surface electromyography sensor to recognize six hand movements for a mechanical hand in real time: a method based on Morse code.

Surface electromyography (sEMG) signal is a kind of physiological signal reflecting muscle activity and muscle force. At present, the existing methods of recognizing human motion intention need more than two sensors to recognize more than two kinds of movements, the sensor pasting positions are special, and the hardware conditions for execution are high. In this work, a real-time motion intention recognition method based on Morse code is proposed and applied to the mechanical hand. The short-time and long-term muscle contraction signals collected by a single sEMG sensor were extracted and encoded with the Morse code method, and then the developed mapping method from Morse code to six hand movements were used to recognize hand movements. The average recognition accuracy of hand movements was 94.8704 ± 2.3556%, the average adjusting time was 34.89 s for all subjects, and the execution time of a single movement was 381 ms. The corresponding experiment video can be found in the attachment to show the experiment. The method proposed in this work is a method with one sensor to recognize six movements, low hardware conditions, high recognition accuracy, and insensitive to the difference of sensor pasting position.

A new fractional fuzzy dispersion entropy and its application in muscle fatigue detection.

Recently, fuzzy dispersion entropy (DispEn) has attracted much attention as a new nonlinear dynamics method that combines the advantages of both DispEn and fuzzy entropy. However, it suffers from limitation of insensitivity to dynamic changes. To solve this limitation, we proposed fractional fuzzy dispersion entropy (FFDispEn) based on DispEn, a novel fuzzy membership function and fractional calculus. The fuzzy membership function was defined based on the Euclidean distance between the embedding vector and dispersion pattern. Simulated signals generated by the one-dimensional (1D) logistic map were used to test the sensitivity of the proposed method to dynamic changes. Moreover, 29 subjects were recruited for an upper limb muscle fatigue experiment, during which surface electromyography (sEMG) signals of the biceps brachii muscle were recorded. Both simulated signals and sEMG signals were processed using a sliding window approach. Sample entropy (SampEn), DispEn and FFDispEn were separately used to calculate the complexity of each frame. The sensitivity of different algorithms to the muscle fatigue process was analyzed using fitting parameters through linear fitting of the complexity of each frame signal. The results showed that for simulated signals, the larger the fractional order q, the higher the sensitivity to dynamic changes. Moreover, DispEn performed poorly in the sensitivity to dynamic changes compared with FFDispEn. As for muscle fatigue detection, the FFDispEn value showed a clear declining tendency with a mean slope of -1.658 × 10-3 as muscle fatigue progresses; additionally, it was more sensitive to muscle fatigue compared with SampEn (slope: -0.4156 × 10-3) and DispEn (slope: -0.1675 × 10-3). The highest accuracy of 97.5% was achieved with the FFDispEn and support vector machine (SVM). This study provided a new useful nonlinear dynamic indicator for sEMG signal processing and muscle fatigue analysis. The proposed method may be useful for physiological and biomedical signal analysis.

Real-time modeling and feature extraction method of surface electromyography signal for hand movement classification based on oscillatory theory.

Objective.Research of surface electromyography (sEMG) signal modeling and feature extraction is important in human motion intention recognition, prosthesis and exoskeleton robots. However, the existing methods mostly use the signal segmentation processing method rather than the point-to-point signal processing method, and lack physiological mechanism support.Approach. In this study, a real-time sEMG signal modeling and separation method is developed based on oscillatory theory. On this basis, an sEMG signal feature extraction method is constructed, and an ensemble learning method is combined to achieve real-time human hand motion intention recognition.Main results.The experimental results show that the average root mean square difference value of the sEMG signal modeling is 0.3838 ± 0.0591, and the average accuracy of human hand motion intention recognition is 96.03 ± 1.74%. On a computer with Intel (R) Core (TM) i5-8250U CPU running Matlab 2016Rb, the execution time for the sEMG signal with an actual duration of 2 s is 0.66 s.Significance. Compared with several existing methods, the proposed method has better modeling accuracy, motion intention recognition accuracy and real-time performance. The method developed in this study may provide a new perspective on sEMG modeling and feature extraction for hand movement classification.

A Retrospective Study on Risk Factors for Urinary Tract Infection in Patients with Intracranial Cerebral Hemorrhage.

OBJECTIVE: This study aimed to explore the risk factors of urinary tract infection (UTI) in patients with intracranial cerebral hemorrhage (ICH). DESIGN: This is a retrospective study, and a total of 77 patients with ICH consecutively admitted to the First Affiliated Hospital of USTC (Anhui Provincial Hospital, Hefei, China) during the period of August 2015 to August 2017 were included. The patients were divided into an UTI group (24 cases) and a non-UTI group (53 cases); patients with UTI were diagnosed according to clinical manifestations, recent urinary routines, and urine culture results. The following information in these two groups was recorded: age, sex, course of disease, side of paralysis, location and type of cerebral hemorrhage, disturbance of consciousness or not, the Brunnstrom stage of paralysed lower limbs, number of basic diseases, whether there were complications (tracheotomy, retention catheterization, pulmonary infection, pressure sore, deep venous thrombosis, etc.), whether rehabilitation interventions were conducted, blood routine, biochemistry index, DIC complete set, urine routine, and urine culture data. Univariate analysis and multivariate logistic regression analysis were used to examine the risk factors of UTI in patients with ICH. RESULTS: Univariate analysis showed that age, side of paralysis, disturbance of consciousness, the Brunnstrom stage of lower limbs, tracheotomies, retention catheterization, pulmonary infection, leukocyte count, neutrophil proportion, sodium, uric acid, D-dimer, and fibrinogen may be related to UTI in patients with ICH (P < 0.05). Regression analysis showed that age (OR (95% CI) = 1.207 (1.022-1.424), P < 0.05). Regression analysis showed that age (OR (95% CI) = 1.207 (1.022-1.424), P < 0.05). Regression analysis showed that age (OR (95% CI) = 1.207 (1.022-1.424), P < 0.05). Regression analysis showed that age (OR (95% CI) = 1.207 (1.022-1.424). CONCLUSIONS: Increased age and high D-dimer are independent risk factors for UTI in patients with ICH, while right-sided paralysis is a protective factor for UTI in patients with ICH.

Correction to: Spasticity assessment based on the Hilbert-Huang transform marginal spectrum entropy and the root mean square of surface electromyography signals: a preliminary study.

After the publication of the original article work [1], it was highlighted that the circuit information and the A/D converter used for acquiring sEMG signal were not correctly reported in the Methods section. The circuit included a 10 Hz high-pass filter instead of 10 Hz notch filter as stated in the original article. The A/D converter was ADS7818, instead of ADS1198 as stated in the original paper. The authors apologize to the readers for the inconvenience.

Spasticity Measurement Based on the HHT Marginal Spectrum Entropy of sEMG Using a Portable System: A Preliminary Study.

To facilitate stretch reflex onset (SRO) detection and improve accuracy and reliability of spasticity assessment in clinical settings, a new method to measure dynamic stretch reflex threshold (DSRT) based on Hilbert-Huang transform marginal spectrum entropy (HMSEN) of surface electromyography (sEMG) signals and a portable system to quantify modified Ashworth scale (MAS) for spasticity assessment were developed. The sEMG signals were divided into frames using a fixed-length sliding window, and the HMSEN of each frame was calculated. An adaptive threshold was set to measure the DSRT. The HMSEN based method can quantify muscle activity through time-frequency and nonlinear dynamics analysis, therefore providing deeper insight about the spastic muscle mechanisms during stretching and a reliable SRO detection method. Experimental results revealed that the HMSEN based method could reliably detect the SRO and measure the DSRT (recognition rate: 95.45%), and could achieve improved performance over the time-domain based method. There was a strong correlation ( to -0.900) between the MAS scores and the DSRT index, and the test-retest reliability was high. Additionally, limitations of the MAS were analyzed. This paper indicates that the presented framework can provide a promising tool to measure DSRT and a clinical quantitative approach for spasticity assessment.

Spasticity assessment based on the Hilbert-Huang transform marginal spectrum entropy and the root mean square of surface electromyography signals: a preliminary study.

BACKGROUND: Most of the objective and quantitative methods proposed for spasticity measurement are not suitable for clinical application, and methods for surface electromyography (sEMG) signal processing are mainly limited to the time-domain. This study aims to quantify muscle activity in the time-frequency domain, and develop a practical clinical method for the objective and reliable evaluation of the spasticity based on the Hilbert-Huang transform marginal spectrum entropy (HMSEN) and the root mean square (RMS) of sEMG signals. METHODS: Twenty-six stroke patients with elbow flexor spasticity participated in the study. The subjects were tested at sitting position with the upper limb stretched towards the ground. The HMSEN of the sEMG signals obtained from the biceps brachii was employed to facilitate the stretch reflex onset (SRO) detection. Then, the difference between the RMS of a fixed-length sEMG signal obtained after the SRO and the RMS of a baseline sEMG signal, denoted as the RMS difference (RMSD), was employed to evaluate the spasticity level. The relations between Modified Ashworth Scale (MAS) scores and RMSD were investigated by Ordinal Logistic Regression (OLR). Goodness-of-fit of the OLR was obtained with Hosmer-Lemeshow test. RESULTS: The HMSEN based method can precisely detect the SRO, and the RMSD scores and the MAS scores were fairly well related (test: χ2 = 8.8060, p = 0.2669; retest: χ2 = 1.9094, p = 0.9647). The prediction accuracies were 85% (test) and 77% (retest) when using RMSD for predicting MAS scores. In addition, the test-retest reliability was high, with an interclass correlation coefficient of 0.914 and a standard error of measurement of 1.137. Bland-Altman plots also indicated a small bias. CONCLUSIONS: The proposed method is manually operated and easy to use, and the HMSEN based method is robust in detecting SRO in clinical settings. Hence, the method is applicable to clinical practice. The RMSD can assess spasticity in a quantitative way and provide greater resolution of spasticity levels compared to the MAS in clinical settings. These results demonstrate that the proposed method could be clinically more useful for the accurate and reliable assessment of spasticity and may be an alternative clinical measure to the MAS.

Effects of modified sit-to-stand training on balance control in hemiplegic stroke patients: a randomized controlled trial.

OBJECTIVE: To explore the effectiveness of modified sit-to-stand training on balance function in hemiplegic stroke patients. DESIGN: Randomized controlled trial. SETTING: Rehabilitation medical centre. PARTICIPANTS: A total of 50 hemiplegic patients with stroke were randomly assigned to the control and experimental groups (n = 25 for each group). INTERVENTIONS: Patients in the control group received the sit-to-stand training with symmetrical foot position, while patients in the experimental group were given the modified sit-to-stand training in which the paretic foot placed posterior. Subjects in both groups received 30 minutes of sit-to-stand training, five times a week, for four weeks. MAIN OUTCOME MEASURES: The time and weight-bearing distribution during sit-to-stand movement, the centre of pressure sway length during quiet standing, the centre of pressure sway areas during dynamic standing and Berg Balance Scale were assessed before and after completing the four-week sit-to-stand training. RESULTS: Our data showed significant improvements in standing balance and the sit-to-stand movement for two groups in the post-training compared with the pre-training. After training, the rise time shortened more significantly in the experimental group (mean change, 0.90 ±0.25 seconds) than the control group (mean change, 0.42 ±0.18 seconds). Weight-bearing asymmetry showed significantly greater improvement in the experimental group (mean change, 0.17 ±0.10) than in the control group (mean change, 0.06 ±0.05). Centre of pressure sway length was significantly smaller in the experimental group (mean change, 27.85 ±10.58 cm) than in the control group (mean change, 21.95 ±8.19 cm). Centre of pressure sway areas was significantly larger in the experimental group (mean change, 84.24 ±26.48 cm(2)) than in the control group (mean change, 67.74 ±22.84 cm(2)) (P = 0.027). The Berg Balance Scale was significantly higher in the experiment group (mean change, 8.4 ±3.1) than the control group (mean change, 5.8±2.8). CONCLUSIONS: A modified sit-to-stand training improves the balance function in hemiplegic stroke patients.

High circulating CD39(+) regulatory T cells predict poor survival for sepsis patients.

BACKGROUND: Sepsis encompasses two phases, the 'hyper'-reactive phase and the 'hypo'-reactive phase. The initial inflammatory stage is quickly counterbalanced by an anti-inflammatory response, which compromises the immune system, leading to immune suppression. Regulatory T cells (Tregs) have been implicated in the pathogenesis of sepsis by inducing immunosuppression; however, the role of CD39(+) Tregs in the process of sepsis is uncertain. This study investigated the dynamic levels of CD39(+) Tregs and their phenotypic change in sepsis. METHODS: Fourteen patients with systemic inflammatory response syndrome (SIRS), 42 patients with sepsis, and 14 healthy controls were enrolled. Sequential blood samples were used to analyze the numbers of CD39(+) Tregs and their phenotypic changes. Survival at 28 days was used to evaluate the capacity of CD39(+) Treg levels to predict mortality in sepsis patients. RESULTS: Sepsis patients displayed a high percentage (3.13%, 1.46%, and 0.35%, respectively) and mean fluorescence intensity (MFI) (59.65, 29.7, and 24.3, respectively) of CD39(+) Tregs compared with SIRS patients and healthy subjects. High-level expression of CD39(+) Tregs was correlated with the severity of sepsis, which was reflected by the sepsis-related organ failure assessment score (r=0.322 and r=0.31, respectively). In addition, the expression of CD39(+) Tregs was associated with survival of sepsis patients (p<0.01). By receiver-operating characteristic (ROC) curve analysis, the percentage and MFI of CD39(+) Tregs showed similar sensitivities and specificities to predict mortality (74.2% and 85.1%, and 73.9% and 84.1%, respectively). Using Kaplan-Meier curves to assess the impact of CD39(+) Tregs percentage and MFI on overall survival, we found that a high CD39(+) Tregs percentage (p<0.001; >4.1%) and MFI (p<0.001; >49.2) were significantly associated with mortality. Phenotypically, CD39(+) Tregs from sepsis patients showed high expression of CD38 and PD-1 (p<0.01 and p<0.01 respectively). CONCLUSIONS: Increased expression of CD39(+) Tregs was associated with a poor prognosis for sepsis patients, which suggests that CD39(+) Treg levels could be used as a biomarker to predict the outcome of sepsis patients.

The expression and antigenicity of a truncated spike-nucleocapsid fusion protein of severe acute respiratory syndrome-associated coronavirus.

BACKGROUND: In the absence of effective drugs, controlling SARS relies on the rapid identification of cases and appropriate management of the close contacts, or effective vaccines for SARS. Therefore, developing specific and sensitive laboratory tests for SARS as well as effective vaccines are necessary for national authorities. RESULTS: Genes encoding truncated nucleocapsid (N) and spike (S) proteins of SARSCoV were cloned into the expression vector pQE30 and fusionally expressed in Escherichia coli M15. The fusion protein was analyzed for reactivity with SARS patients' sera and with anti-sera against the two human coronaviruses HCoV 229E and HCoV OC43 by ELISA, IFA and immunoblot assays. Furthermore, to evaluate the antigen-specific humoral antibody and T-cell responses in mice, the fusion protein was injected into 6-week-old BALB/c mice and a neutralization test as well as a T-cell analysis was performed. To evaluate the antiviral efficacy of immunization, BALB/c mice were challenged intranasally with SARSCoV at day 33 post injection and viral loads were determined by fluorescent quantitative RT-PCR. Serological results showed that the diagnostic sensitivity and specificity of the truncated S-N fusion protein derived the SARS virus were > 99% (457/460) and 100.00% (650/650), respectively. Furthermore there was no cross-reactivity with other two human coronaviruses. High titers of antibodies to SRASCoV appeared in the immunized mice and the neutralization test showed that antibodies to the fusion protein could inhibit SARSCoV. The T cell proliferation showed that the fusion protein could induce an antigen-specific T-cell response. Fluorescent quantitative RT-PCR showed that BALB/c mice challenged intranasally with SARSCoV at day 33 post injection were completely protected from virus replication. CONCLUSION: The truncated S-N fusion protein is a suitable immunodiagnostic antigen and vaccine candidate.

Antibody response and viraemia during the course of severe acute respiratory syndrome (SARS)-associated coronavirus infection.

To understand the time-course of viraemia and antibody responses to severe acute respiratory syndrome-associated coronavirus (SARS-CoV), RT-PCR and ELISA were used to assay 376 blood samples from 135 SARS patients at various stages of the illness, including samples from patients who were in their early convalescent phase. The results showed that IgM antibodies decreased and became undetectable 11 weeks into the recovery phase. IgG antibodies, however, remained detectable for a period beyond 11 weeks and were found in 100 % of patients in the early convalescent phase. SARS-CoV viraemia mainly appeared 1 week after the onset of illness and then decreased over a period of 1 month, becoming undetectable in the blood samples of the convalescent patients. At the peak of viraemia, viral RNA was detectable in 75 % of blood samples from patients who were clinically diagnosed with SARS 1 or 2 weeks before the test.

Real-time quantitative fluorescent reverse transcriptase-PCR for detection of severe acute respiratory syndrome-associated coronavirus RNA.

AIM: SARS-associated coronavirus (SARS-CoV) has been confirmed as the pathogen for severe acute respiratory syndrome (SARS). The aim of our study was to construct a sensitive and specific real-time quantitative fluorescent (QF) reverse transcriptase (RT)-PCR method for the detection of SARS-CoV RNA. METHODS: Stored blood specimens from 44 patients with confirmed SARS were used along with blood samples from two sets of controls, 30 healthy volunteers who had no contact with SARS patients, and 30 healthy doctors and nurses who had contact with SARS patients but were without symptoms of SARS. Two pairs of primers were synthesized by the Shanghai Sangon Company according to SARS-CoV BJ01 strain sequence (AY278488), and then a pair of primers were designed and compared with a pair of primers published by WHO. RESULTS: Using serial dilutions of SARS-CoV, the 44 blood samples from SARS patients specimens were tested. Using a 0.01% dilution of SARS-CoV, all 44 clinical samples tested positive in our assay. In comparison, using a 0.1% dilution of SARS-CoV, 26 of the 44 samples tested positive using the WHO primers. In the QF-RT-PCR assay, there was a linear amplification from 100 copies to 10(8) copies of the control RNA per RT-PCR and at least 10 copies, and sometimes even 1 copy, of target RNA tested positive in our assay. CONCLUSION: The primer we developed is sufficiently sensitive and specific to diagnose symptomatic SARS-CoV infections and for monitoring virus load.