Capacitive Aptasensor Coupled with Microfluidic Enrichment for Real-Time Detection of Trace SARS-CoV-2 Nucleocapsid Protein.

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has lasted for almost 2 years. Stemming its spread has posed severe challenges for clinical virus detection. A long turnaround time, complicated operation, and low accuracy have become bottlenecks in developing detection techniques. Adopting a direct antigen detection strategy, we developed a fast-responding and quantitative capacitive aptasensor for ultratrace nucleocapsid protein detection based on a low-cost microelectrode array (MEA) chip. Employing the solid-liquid interface capacitance with a sensitivity of picofarad level, the tiny change on the MEA surface can be definitively detected. As a result, the limit of detection reaches an ultralow level of femtogram per milliliter in different matrices. Integrated with efficient microfluidic enrichment, the response time of this sensor from the sample to the result is shortened to 15 s, completely meeting the real-time detection demand. Moreover, the wide linear range of the sensor is from 10-5 to 10-2 ng/mL, and a high selectivity of 6369:1 is achieved. After application and evaluation in different environmental and body fluid matrices, this sensor and the detection method have proved to be a label-free, real-time, easy-to-operate, and specific strategy for SARS-CoV-2 screening and diagnosis.

A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMG.

Traditional rigid exoskeletons can be challenging to the comfort of wearers and can have large pressure, which can even alter natural hand motion patterns. In this paper, we propose a low-cost soft exoskeleton glove (SExoG) system driven by surface electromyography (sEMG) signals from non-paretic hand for bilateral training. A customization method of geometrical parameters of soft actuators was presented, and their structure was redesigned. Then, the corresponding pressure values of air-pump to generate different angles of actuators were determined to support four hand motions (extension, rest, spherical grip, and fist). A two-step hybrid model combining the neural network and the state exclusion algorithm was proposed to recognize four hand motions via sEMG signals from the healthy limb. Four subjects were recruited to participate in the experiments. The experimental results show that the pressure values for the four hand motions were about -2, 0, 40, and 70 KPa, and the hybrid model can yield a mean accuracy of 98.7% across four hand motions. It can be concluded that the novel SExoG system can mirror the hand motions of non-paretic hand with good performance.

The COVID-19 Infodemic: Infodemiology Study Analyzing Stigmatizing Search Terms.

BACKGROUND: In the context of the COVID-19 infodemic, the global profusion of monikers and hashtags for COVID-19 have found their way into daily communication and contributed to a backlash against China and the Chinese people. OBJECTIVE: This study examines public engagement in crisis communication about COVID-19 during the early epidemic stage and the practical strategy of social mobilization to mitigate the infodemic. METHODS: We retrieved the unbiased values of the top-ranked search phrases between December 30, 2019, and July 15, 2020, which normalized the anonymized, categorized, and aggregated samples from Google Search data. This study illustrates the most-searched terms, including the official COVID-19 terms, the stigmatized terms, and other controls, to measure the collective behavioral propensities to stigmatized terms and to explore the global reaction to the COVID-19 epidemic in the real world. We calculated the ratio of the cumulative number of COVID-19 cases to the regional population as the cumulative rate (R) of a specific country or territory and calculated the Gini coefficient (G) to measure the collective heterogeneity of crowd behavior. RESULTS: People around the world are using stigmatizing terms on Google Search, and these terms were used earlier than the official names. Many stigmatized monikers against China (eg, "Wuhan pneumonia," G=0.73; "Wuhan coronavirus," G=0.60; "China pneumonia," G=0.59; "China coronavirus," G=0.52; "Chinese coronavirus," G=0.50) had high collective heterogeneity of crowd behavior between December 30, 2019, and July 15, 2020, while the official terms "COVID-19" (G=0.44) and "SARS-CoV-2" (G=0.42) have not become de facto standard usages. Moreover, the pattern of high consistent usage was observed in 13 territories with low cumulative rates (R) between January 16 and July 15, 2020, out of 58 countries and territories that have reported confirmed cases of COVID-19. In the scientific literature, multifarious naming practices may have provoked unintended negative impacts by stigmatizing Chinese people. The World Health Organization; the United Nations Educational, Scientific and Cultural Organization; and the media initiated campaigns for fighting back against the COVID-19 infodemic with the same mission but in diverse voices. CONCLUSIONS: Infodemiological analysis can articulate the collective propensities to stigmatized monikers across search behaviors, which may reflect the collective sentiment of backlash against China and Chinese people in the real world. The full-fledged official terms are expected to fight back against the resilience of negative perceptual bias amid the COVID-19 epidemic. Such official naming efforts against the infodemic should be met with a fair share of identification in scientific conventions and sociocultural paradigms. As an integral component of preparedness, appropriate nomenclatures should be duly assigned to the newly identified coronavirus, and social mobilization in a uniform voice is a priority for combating the next infodemic.

sEMG-Based Drawing Trace Reconstruction: A Novel Hybrid Algorithm Fusing Gene Expression Programming into Kalman Filter.

How to reconstruct drawing and handwriting traces from surface electromyography (sEMG) signals accurately has attracted a number of researchers recently. An effective algorithm is crucial to reliable reconstruction. Previously, nonlinear regression methods have been utilized successfully to some extent. In the quest to improve the accuracy of transient myoelectric signal decoding, a novel hybrid algorithm KF-GEP fusing Gene Expression Programming (GEP) into Kalman Filter (KF) framework is proposed for sEMG-based drawing trace reconstruction. In this work, the KF-GEP was applied to reconstruct fourteen drawn shapes and ten numeric characters from sEMG signals across five participants. Then the reconstruction performance of KF-GEP, KF and GEP were compared. The experimental results show that the KF-GEP algorithm performs best because it combines the advantages of KF and GEP. The findings add to the literature on the muscle-computer interface and can be introduced to many practical fields.

Water regime-nitrogen fertilizer incorporation interaction: Field study on methane and nitrous oxide emissions from a rice agroecosystem in Harbin, China.

Water regime and nitrogen (N) fertilizer are two important factors impacting greenhouse gases (GHG) emission from paddy field, whereas their effects have not been well studied in cold region. In this study, we conducted a two-year field experiment to study the impacts of water regime and N fertilizer on rice yields and GHG emissions in Harbin, China, a cold region located in high latitudes. Our results showed that intermittent irrigation significantly decreased methane (CH4) emission compared with continuous flooding, however, the decrement was far lower than the global average level. The N2O emissions were very small when flooded but peaked at the beginning of the disappearance of floodwater. The N fertilizer treatments increased CH4 emissions at low level (75kgN/ha). But both CH4 and N2O emissions were uninfluenced at the levels of 150kgN/ha and 225kgN/ha. Rice yields increased under intermittent irrigation and were highest at the level of 150kgN/ha. From our results, we recommended that the intermittent irrigation and 150kgN/ha as the ideal water regime-nitrogen fertilizer incorporation for this area to achieve low GHG emissions without impacting rice yields.

An upper-limb power-assist exoskeleton using proportional myoelectric control.

We developed an upper-limb power-assist exoskeleton actuated by pneumatic muscles. The exoskeleton included two metal links: a nylon joint, four size-adjustable carbon fiber bracers, a potentiometer and two pneumatic muscles. The proportional myoelectric control method was proposed to control the exoskeleton according to the user's motion intention in real time. With the feature extraction procedure and the classification (back-propagation neural network), an electromyogram (EMG)-angle model was constructed to be used for pattern recognition. Six healthy subjects performed elbow flexion-extension movements under four experimental conditions: (1) holding a 1-kg load, wearing the exoskeleton, but with no actuation and for different periods (2-s, 4-s and 8-s periods); (2) holding a 1-kg load, without wearing the exoskeleton, for a fixed period; (3) holding a 1-kg load, wearing the exoskeleton, but with no actuation, for a fixed period; (4) holding a 1-kg load, wearing the exoskeleton under proportional myoelectric control, for a fixed period. The EMG signals of the biceps brachii, the brachioradialis, the triceps brachii and the anconeus and the angle of the elbow were collected. The control scheme's reliability and power-assist effectiveness were evaluated in the experiments. The results indicated that the exoskeleton could be controlled by the user's motion intention in real time and that it was useful for augmenting arm performance with neurological signal control, which could be applied to assist in elbow rehabilitation after neurological injury.

From medical strategy to foodborne prophylactic strategy: Stabilizing dental collagen with aloin.

Infectious oral diseases are longstanding global public health concerns. However, traditional medical approaches to address these diseases are costly, traumatic, and prone to relapse. Here, we propose a foodborne prophylactic strategy using aloin to safeguard dental collagen. The effect of aloin on the stability of dental collagen was evaluated by treating dentin with a solution containing aloin (0.1 mg/mL) for 2 min. This concentration is comparable to the natural aloin content of edible aloe. Furthermore, we investigated the mechanisms underlying the interactions between aloin and dentin collagen. Our findings, obtained through fluorescence spectroscopy, attenuated total reflection Fourier transform infrared spectroscopy, Gaussian peak fitting, circular dichroism spectroscopy, and X-ray diffraction, revealed that aloin interacts with dental collagen through noncovalent bonding, specifically hydrogen bonding in situ. This interaction leads to a reduction in the distance between molecules and an increase in the proportion of stable α-helical chains in the dental collagen. The ultimate tensile strength and thermogravimetric analysis demonstrated that dental collagen treated with aloin exhibited improved mechanical strength and thermostability. Additionally, the release of hydroxyproline, cross-linked carboxy-terminal telopeptide of type I collagen, and C-terminal cross-linked telopeptide of type I collagen, along with weight loss, indicated an enhancement in the enzymatic stability of dental collagen. These findings suggest that aloin administration could be a daily, nondestructive, and cost-effective strategy for managing infectious oral diseases.

FET-LM: Flow-Enhanced Variational Autoencoder for Topic-Guided Language Modeling.

Variational autoencoder (VAE) is widely used in tasks of unsupervised text generation due to its potential of deriving meaningful latent spaces, which, however, often assumes that the distribution of texts follows a common yet poor-expressed isotropic Gaussian. In real-life scenarios, sentences with different semantics may not follow simple isotropic Gaussian. Instead, they are very likely to follow a more intricate and diverse distribution due to the inconformity of different topics in texts. Considering this, we propose a flow-enhanced VAE for topic-guided language modeling (FET-LM). The proposed FET-LM models topic and sequence latent separately, and it adopts a normalized flow composed of householder transformations for sequence posterior modeling, which can better approximate complex text distributions. FET-LM further leverages a neural latent topic component by considering learned sequence knowledge, which not only eases the burden of learning topic without supervision but also guides the sequence component to coalesce topic information during training. To make the generated texts more correlative to topics, we additionally assign the topic encoder to play the role of a discriminator. Encouraging results on abundant automatic metrics and three generation tasks demonstrate that the FET-LM not only learns interpretable sequence and topic representations but also is fully capable of generating high-quality paragraphs that are semantically consistent.

A mixed reality-based navigation method for dental implant navigation method: A pilot study.

This in vitro study aimed to put forward the development and investigation of a novel Mixed Reality (MR)-based dental implant navigation method and evaluate implant accuracy. Data were collected using 3D-cone beam computed tomography. The MR-based navigation system included a Hololens headset, an NDI (Northern Digital Inc.) Polaris optical tracking system, and a computer. A software system was developed. Resin models of dentition defects were created for a randomized comparison study with the MR-based navigation implantation system (MR group, n = 25) and the conventional free-hand approach (FH group, n = 25). Implant surgery on the models was completed by an oral surgeon. The precision and feasibility of the MR-based navigation method in dental implant surgery were assessed and evaluated by calculating the entry deviation, middle deviation, apex deviation, and angular deviation values of the implant. The system, including both the hardware and software, for the MR-based dental implant navigation method were successfully developed and a workflow of the method was established. Three-Dimensional (3D) reconstruction and visualization of the surgical instruments, dentition, and jawbone were achieved. Real-time tracking of implant tools and jaw model, holographic display via the MR headset, surgical guidance, and visualization of the intraoperative implant trajectory deviation from the planned trajectory were captured by our system. The MR-based navigation system was with better precise than the free-hand approach for entry deviation (MR: 0.6914 ± 0.2507 mm, FH: 1.571 ± 0.5004 mm, P = 0.000), middle deviation (MR: 0.7156 ± 0.2127 mm, FH: 1.170 ± 0.3448 mm, P = 0.000), apex deviation (MR: 0.7869 ± 0.2298 mm, FH: 0.9190 ± 0.3319 mm, P = 0.1082), and angular deviation (MR: 1.849 ± 0.6120°, FH: 4.933 ± 1.650°, P = 0.000).

Enhancing bioactivity and stability of polymer-based material-tissue interface through coupling multiscale interfacial interactions with atomic-thin TiO2 nanosheets.

Stable and bioactive material-tissue interface (MTF) basically determines the clinical applications of biomaterials in wound healing, sustained drug release, and tissue engineering. Although many inorganic nanomaterials have been widely explored to enhance the stability and bioactivity of polymer-based biomaterials, most are still restricted by their stability and biocompatibility. Here we demonstrate the enhanced bioactivity and stability of polymer-matrix bio-composite through coupling multiscale material-tissue interfacial interactions with atomically thin TiO2 nanosheets. Resin modified with TiO2 nanosheets displays improved mechanical properties, hydrophilicity, and stability. Also, we confirm that this resin can effectively stimulate the adhesion, proliferation, and differentiation into osteogenic and odontogenic lineages of human dental pulp stem cells using in vitro cell-resin interface model. TiO2 nanosheets can also enhance the interaction between demineralized dentinal collagen and resin. Our results suggest an approach to effectively up-regulate the stability and bioactivity of MTFs by designing biocompatible materials at the sub-nanoscale. Electronic Supplementary Material: Supplementary material (further details of fabrication and characterization of TiO2 NSs and TiO2-ARCs, the bioactivity evaluation of TiO2-ARCs on hDPSCs, and the measurement of interaction with demineralized dentin collagen) is available in the online version of this article at 10.1007/s12274-022-5153-1.

Immunogenicity and Safety of Homologous Booster Doses of CoronaVac COVID-19 Vaccine in Elderly Individuals Aged 60 Years and Older: A Dosing Interval Study - Yunnan Province, China, 2021-2022.

What is already known about this topic?: Neutralization levels induced by inactivated vaccines rapidly wane after primary immunization, and a homologous booster can recall specific immune memory, resulting in a remarkable increase in antibody concentration. The optimal interval between primary and booster doses has yet to be determined. What is added by this report?: Booster doses given at three months or more after the two-dose regimen of the CoronaVac COVID-19 vaccine in elderly individuals aged 60 years and older triggered good immune responses. The geometric mean titers of neutralizing antibody on Day 14 after the booster doses increased by 13.3-26.2 fold of baseline levels, reaching 105.45-193.59 in groups with different intervals (e.g., 3, 4, 5, and 6 months). What are the implications for public health practice?: A 4- to 5-month interval between receiving the primary and booster series of CoronaVac could be an alternative to the 6-month interval in order to promote vaccine-induced immunity in elderly individuals. The findings support the optimization of booster immunization strategies.

[The influence of positive end-expiratory pressure on central venous pressure in patients with severe craniocerebral injury].

OBJECTIVE: To observe the impact of positive end-expiratory pressure (PEEP) on central venous pressure (CVP) in mechanically ventilated patients with severe craniocerebral injury. METHODS: A prospective, interventional, self-control study was conducted. Thirty severe craniocerebral injury patients with central respiratory failure were enrolled. The changes in CVP, mean arterial pressure (MAP), heart rate (HR) and pulse oxygen saturation [SpO2] were monitored at different PEEP levels [0, 3, 6, 9, 12, 15 cm H2O; 1 cm H2O=0.098 kPa] during mechanical ventilation and after weaning of mechanical ventilation. The influences of PEEP and its discontinuance on haemodynamics and oxygenation were analyzed. RESULTS: The values of CVP [cm H2O] were increased when PEEP increased (from 7.9±3.1 to 13.1±3.7), a linear correlation was found (R=0.509, P=0.000), and linear regression equation was CVP [cm H2O]=7.774+0.368×PEEP [cm H2O]; CVP was elevated about 0.368 cm H2O when PEEP increased 1 cm H2O. CVP values significantly decreased during discontinuance of mechanical ventilation, as compared to those measured at different PEEP levels during mechanical ventilation (F=24.429, P=0.000). The values of MAP, HR and SpO2 showed no significant change with increase of PEEP levels [MAP (mm Hg, 1 mm Hg=0.133 kPa): from 81.6±10.4 to 85.6±10.6; HR (beats per minute): from 79.9±13.5 to 88.1±15.4; SpO2: from 0.968±0.036 to 0.975±0.033, all P>0.05] in mechanically ventilated patients, but discontinuance of mechanical ventilation could significantly increase the levels of MAP and HR (95.3±8.4 and 94.9±10.3, respectively) and lower SpO2 levels (0.928±0.036, all P=0.000). CONCLUSIONS: CVP values were overestimated during an increase in PEEP in mechanically ventilated patients with severe craniocerebral injury. CVP was increased about 0.368 cm H2O following an increase of PEEP of 1 cm H2O, whereas the values of MAP, HR and SpO2 showed no significant change with increase in PEEP levels. This study could offer a theoretical base in the correct assessment of CVP values at different PEEP levels without discontinuation of mechanical ventilation.

Ependymoma and pilocytic astrocytoma: Differentiation using radiomics approach based on machine learning.

Mandatory accurate and specific diagnosis demands have brought about increased challenges for radiologists in pediatric posterior fossa tumor prediction and prognosis. With the development of high-performance computing and machine learning technologies, radiomics provides increasing opportunities for clinical decision-making. Several studies have applied radiomics as a decision support tool in intracranial tumors differentiation. Here we seek to achieve preoperative differentiation between ependymoma (EP) and pilocytic astrocytoma (PA) using radiomics analysis method based on machine learning. A total of 135 Magnetic Resonance Imaging (MRI) slices are divided into training sets and validation sets. Three kinds of radiomics features, including Gabor transform, texture and wavelet transform based ones are used to obtain 300 multimodal features. Kruskal-Wallis test score (KWT) and support vector machines (SVM) are applied for feature selection and tumor differentiation. The performance is investigated via accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) analysis. Results show that the accuracy, sensitivity, specificity, and AUC of the selected feature set are 0.8775, 0.9292, 0.8000, and 0.8646 respectively, having no significantdifferencescomparedwiththe overall feature set. For different types of features, texture features yield the best differentiation performance and the significance analysis results are consistent with this. Our study demonstrates texture features perform better than the other features. The radiomics approach based on machine learning is efficient for pediatric posterior fossa tumors differentiation and could enhance the application of radiomics methods for assisted clinical diagnosis.

Automatic bad channel detection in implantable brain-computer interfaces using multimodal features based on local field potentials and spike signals.

"Bad channels" in implantable multi-channel recordings bring troubles into the precise quantitative description and analysis of neural signals, especially in the current "big data" era. In this paper, we combine multimodal features based on local field potentials (LFPs) and spike signals to detect bad channels automatically using machine learning. On the basis of 2632 pairs of LFPs and spike recordings acquired from five pigeons, 12 multimodal features are used to quantify each channel's temporal, frequency, phase and firing-rate properties. We implement seven classifiers in the detection tasks, in which the synthetic minority oversampling technique (SMOTE) system and Fisher weighted Euclidean distance sorting (FWEDS) are used to cope with the class imbalance problem. The results of the two-dimensional scatterplots and classifications demonstrate that correlation coefficient, phase locking value, and coherence have good discriminability. For the multimodal features, almost all the classifiers can obtain high accuracy and bad channel detection rate after the SMOTE operation, in which the Random Forests classifier shows relatively better comprehensive performance (accuracy: 0.9092 ± 0.0081, precision: 0.9123 ± 0.0100, and recall: 0.9057 ± 0.0121). The proposed approach can automatically detect bad channels based on multimodal features, and the results provide valuable references for larger datasets.

Huge gastric disopyrobezoar: a case report and review of literatures.

We reported a case of huge gastric phytobezoar. The gastric phytobezoar was successfully removed through gastrotomy after two failed attempts in endoscopic fragmentation and removal. Disopyrobezoars could be treated either conservatively or surgically. Gastrotomy or laparoscopical management is recommended for the treatment of huge disopyrobezoars.

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

[Efficacy and safety of simulated artificial pancreas in modulating stress hyperglycemia in critically ill patients: a prospective randomized controlled study].

OBJECTIVE: To explore efficacy and safety of simulated artificial pancreas in modulating stress hyperglycemia in critically ill patients. METHODS: A prospective randomized controlled study was performed. Seventy-two critically ill patients with stress hyperglycemia, aged 18-85 years, acute physiology and chronic health evaluation II (APACHE II) score over 15, two consecutive random blood glucose 11.1 mmol/L or higher, glycated hemoglobin (HbA1C) below 0.065, unable to eat food for 3 days after inclusion, or only accepting parenteral nutrition, admitted to intensive care unit (ICU) in Shanghai Punan Hospital of Pudong New District from January 1st, 2015 to June 30th, 2017 were enrolled. The patients were divided into three groups according to the random number table method, high-intensity group and low-intensity group were injected Novolin R (high-intensity group 2/3 dosage, low-intensity group 1/3 dosage) to modulate stress hyperglycemia by simulated artificial pancreas. Simulated artificial pancreas consisted of Guardian real time glucose monitoring system (GRT system), close-circle control algorithm and micro-pump; subcutaneous injection of Humulin 70/30 was applied to modulate stress hyperglycemia in humulin group. Real-time glucose levels of interstitial fluid in abdominal wall, equivalent to blood glucose levels, 10 minutes each time, were monitored by using of GRT system for all patients in three groups. Fasting serum levels of stress hormones including epinephrine and cortisol and insulin resistance index (IRI) were recorded within 24 hours after inclusion. Mean blood glucose, blood glucose variation coefficient, blood glucose target-reaching rate, blood glucose target-reaching time, hypoglycemia rate and 6-month mortality were measured. Twenty healthy adults from health administration department of the hospital were recruited as healthy control group. RESULTS: A total of 60 eligible critically ill patients were included in this study, each group with 20 patients. There was no significant difference in gender, age, APACHE II scores among three groups. The levels of serum epinephrine, cortisol and IRI within 24 hours after inclusion in the three groups were significantly higher than those in healthy control group. The mean blood glucose levels of humulin group, low-intensity group, high-intensity group were decreased (mmol/L: 10.2±3.2, 8.4±2.6, 8.1±2.2), the blood glucose target-reaching rate were increased [40.2% (3 295/8 196), 71.1% (5 393/7 585), 80.4% (6 286/7 818)], the blood glucose target-reaching time were shortened (hours: 49.1±5.8, 24.6±4.6, 17.5±4.2), the hypoglycemia rates were increased respectively [1.3% (108/8 196), 2.8% (211/7 585), 4.0% (313/7 818)], with statistically significant differences (all P = 0.000). There was no significant difference in blood glucose variation coefficient and 6-month mortality among three groups [blood glucose variation coefficient: (29.4±3.7)%, (28.5±5.3)%, (26.1±4.6)%, 6-month mortality: 55.0%, 45.0%, 40.0%, all P > 0.05]. CONCLUSIONS: Simulated artificial pancreas could effectively and safely modulate stress hyperglycemia in critically ill patients, high-intensity modulation could bring about better efficacy in the regulation of hyperglycemia. High-frequency blood glucose monitoring by using GRT system could promptly identify hypoglycemia and help it to be corrected.

A Novel Hybrid Model for Drawing Trace Reconstruction from Multichannel Surface Electromyographic Activity.

Recently, several researchers have considered the problem of reconstruction of handwriting and other meaningful arm and hand movements from surface electromyography (sEMG). Although much progress has been made, several practical limitations may still affect the clinical applicability of sEMG-based techniques. In this paper, a novel three-step hybrid model of coordinate state transition, sEMG feature extraction and gene expression programming (GEP) prediction is proposed for reconstructing drawing traces of 12 basic one-stroke shapes from multichannel surface electromyography. Using a specially designed coordinate data acquisition system, we recorded the coordinate data of drawing traces collected in accordance with the time series while 7-channel EMG signals were recorded. As a widely-used time domain feature, Root Mean Square (RMS) was extracted with the analysis window. The preliminary reconstruction models can be established by GEP. Then, the original drawing traces can be approximated by a constructed prediction model. Applying the three-step hybrid model, we were able to convert seven channels of EMG activity recorded from the arm muscles into smooth reconstructions of drawing traces. The hybrid model can yield a mean accuracy of 74% in within-group design (one set of prediction models for all shapes) and 86% in between-group design (one separate set of prediction models for each shape), averaged for the reconstructed x and y coordinates. It can be concluded that it is feasible for the proposed three-step hybrid model to improve the reconstruction ability of drawing traces from sEMG.

Surface EMG-based Sketching Recognition Using Two Analysis Windows and Gene Expression Programming.

Sketching is one of the most important processes in the conceptual stage of design. Previous studies have relied largely on the analyses of sketching process and outcomes; whereas surface electromyographic (sEMG) signals associated with sketching have received little attention. In this study, we propose a method in which 11 basic one-stroke sketching shapes are identified from the sEMG signals generated by the forearm and upper arm muscles from 4 subjects. Time domain features such as integrated electromyography, root mean square and mean absolute value were extracted with analysis windows of two length conditions for pattern recognition. After reducing data dimensionality using principal component analysis, the shapes were classified using Gene Expression Programming (GEP). The performance of the GEP classifier was compared to the Back Propagation neural network (BPNN) and the Elman neural network (ENN). Feature extraction with the short analysis window (250 ms with a 250 ms increment) improved the recognition rate by around 6.4% averagely compared with the long analysis window (2500 ms with a 2500 ms increment). The average recognition rate for the eleven basic one-stroke sketching patterns achieved by the GEP classifier was 96.26% in the training set and 95.62% in the test set, which was superior to the performance of the BPNN and ENN classifiers. The results show that the GEP classifier is able to perform well with either length of the analysis window. Thus, the proposed GEP model show promise for recognizing sketching based on sEMG signals.