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In this paper, three easily implemented hardware algorithms, including the adaptive prediction error filter based on the Gram-Schmidt algorithm (GS-APEF), the least mean square adaptive filter and the comb filter, are extensively investigated for artifact denoising on a constructed semi-simulated database with varied ten-fold frequency stimulation. By implementing the GS-APEF in the field-programmable gate array (FPGA) and using the edge noise mitigating technique, a stimulation artifact denoising system is designed to realize real-time stimulation artifact removal under varied ten-fold frequency functional electrical stimulation. Good performance of the artifact denoising is demonstrated in proof-of-concept experiments on able-bodied subjects with a mean correlation coefficient between the root mean square profile of denoised surface electromyography and volitional force of 0.94, verifying the validity of the proposed prototype.

Rainfall runoff is one of the important sources of urban river pollution. In order to understand the pollution characteristics of rainfall runoff, the synchronous sampling and monitoring of rainfall runoff in the old urban area of Nanning were carried out; the pollution condition, initial scouring effect, and pollution contribution ratio of different underlying surfaces under different rainfall conditions were analyzed; and the calculation method and influence factor analysis of initial scouring of runoff pollution were discussed. According to the underlying surface of the vegetable market in the old urban area, the selection standard and necessity of the underlying surface of rainfall runoff were discussed. The results showed that the average concentration (EMC) of COD and TSS in roads and vegetable markets were greater than those in green spaces and roofs in the runoff pollution of the old urban area of Nanning, and the EMC values of nutrient pollutants in field rainfall runoff were ranked in decreasing order as vegetable markets, green spaces, roofs, and roads. Under the condition of heavy rain, each underlying surface had an obvious initial scouring effect, the average value of initial scouring coefficient (

In this study, a wearable prototype system was developed for multiple-gesture rehabilitation using electrical stimulation controlled by a volitional surface electromyography (sEMG) scan of a healthy forearm. The purpose of the prototype system is to reconstruct multiple gestures of a paralysed limb and to simplify the positioning of sEMG detection sites on a healthy forearm. A self-designed eight-channel sEMG detection armband was used to detect the sEMG signal distributions of the muscle groups in healthy forearms. Linear discriminant analysis (LDA) was used to classify the sEMG signal distributions corresponding to different gestures, and then the classification results were mapped to corresponding stimulation channels. The sEMG signal with the maximum root mean square (RMS) was used as the source of stimulus coding for each gesture. Our proposed mean absolute value (MAV)/number of slope sign changes (NSS) dual-coding (MNDC) algorithm was used to encode the sEMG signal into an electrical stimulus with a dynamic pulse width and frequency. The constant-current stimulation armband electrically stimulated multiple muscles in the affected forearm by means of a circuit designed with a time-division multiplexed stimulation channel. An experiment involving 6 able-bodied volunteers showed that when the detection armband was located near the middle of the forearm, the gesture classification accuracy was greater than 90%, and each active sEMG signal was high. Gesture bridge experiments, including grasping, wrist flexion, wrist extension and finger extension, were carried out among six hemiplegic subjects and between one able-bodied volunteer acting as a controller and each of six stroke patients as the controllee. Both sets of results show that the proposed system can reconstruct these four gestures in the controlled subject with a delay of at most 360 ms and with a correlation coefficient of >0.72.

A surface electromyography (sEMG) detector, that not only removes stimulation artifacts entirely but also increases the recording time, has been developed in this paper. The sEMG detector consists of an sEMG detection circuit and a stimulation isolator. The sEMG detection circuit employs a stimulus isolate switch (SIS), a blanking (BLK) and non-linear feed-back (NFB) circuit to remove the artifacts and to increase the recording time. In the SIS, the connection between stimulator and stimulation electrodes, along with the stimulation electrodes and the ground are controlled by an opto-isolator, and the connection of instrument amplifier and the recording electrodes are controlled by CMOS-based switches. The mode switches of the BLK and the NFB circuit also employs CMOS-based switches. By an accurate timing adjustment, the voluntary EMG can be recorded during electrical stimulation. Two 6 able-bodied experiments have been performed to test the three anti-artifact sEMG detector: BLK, BLK&SIS, BLK&SIS&NFB. The results indicate that the BLK&SIS&NFB proposed in this work effectively removes stimulus artifacts and M-waves, and has a longer recording time compared with BLK and BLK&SIS circuits.

Objectives: The goal of this study is to design a novel approach for extracting volitional electromyography (vEMG) contaminated by functional electrical stimulation (FES) with time variant amplitudes and frequencies. Methods: A selective interpolation (SI) is adopted to eliminate the initial spike. Then the interpolated signal is decomposed into intrinsic mode functions by using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Each IMF is window-filtered based on a logistic regression (LR) classifier to identify the IMFs contaminated by FES. Semi-simulated signals were generated using EMG and stimulation response and three metrics were adopted to validate the performance of the proposed algorithm, including the a) signal-to-noise ratio (SNR), b) normalized root mean squared error (NRMSE) and c) cross-correlation coefficient between the clean EMG and the extracted EMG. Real FES-contaminated EMG was collected from six able bodied volunteers and one stroke patient. The correlation coefficients between the extracted EMG and the wrist torque were analyzed. Results: The simulation results showed a higher SNR (2.12 to −2.13dB), higher correlation (0.88± 0.08) and lower NRMSE (0.78 to 1.29) than those of the comb filter and EMD-Notch algorithm. The EMG-Torque correlation coefficients were 0.75± 0.07 for monopolar pulses and 0.77± 0.12 for bipolar pulses. For the stroke patient, the algorithm also successfully extracted underlying vEMG from time variant FES noises. Conclusions: All results showed that SICEEMDAN-LR is capable of extracting EMG during FES with time-variant parameters.

In this paper, a wireless wearable functional electrical stimulator controlled by Android phone with real-time-varying stimulation parameters for multichannel surface functional electrical stimulation application has been developed. It can help post-stroke patients using more conveniently. This study focuses on the prototype design, including the specific wristband concept, circuits and stimulation pulse-generation algorithm. A novel stimulator circuit with a driving stage using a complementary current source technique is proposed to achieve a high-voltage compliance, a large output impedance and an accurate linear voltage-to-current conversion. The size of the prototype has been significantly decreased to 17 × 7.5 × 1 cm3. The performance of the prototype has been tested with a loaded resistor and wrist extension/flexion movement of three hemiplegic patients. According to the experiments, the stimulator can generate four-channel charge-balanced biphasic stimulation with a voltage amplitude up to ...

In order to recognize upper limb's motion more accurately, this paper presents a method for locating transcutaneous surface electromyography (sEMG) based on traditional Chinese medicine (TCM) acupoint sites. To get sEMG root mean square (RMS) value distribution, sEMG signals of nine kinds of movements of seven healthy individuals were collected under the acupoint coordinate system. Finally, this paper obtains a trans-individual localization map of sEMG detection sites of upper limbs. This method of localization of sEMG detection sites not only can quickly find sites across individual, but also is accepted easily for clinical application.

Objective. In this study, a hybrid method combining hardware and software architecture is proposed to remove stimulation artefacts (SAs) and extract the volitional surface electromyography (sEMG) in real time during functional electrical stimulations (FES) with time-variant parameters. Approach. First, an sEMG detection front-end (DFE) combining fast recovery, detector and stimulator isolation and blanking is developed and is capable of preventing DFE saturation with a blanking time of 7.6 ms. The fragment between the present stimulus and previous stimulus is set as an SA fragment. Second, an SA database is established to provide six high-similarity templates with the current SA fragment. The SA fragment will be de-artefacted by a 6th-order Gram–Schmidt (GS) algorithm, a template-subtracting method, using the provided templates, and this database-based GS algorithm is called DBGS. The provided templates are previously collected SA fragments with the same or a similar evoking FES intensity to that of the current SA fragment, and the lengths of the templates are longer than that of the current SA fragment. After denoising, the sEMG will be extracted, and the current SA fragment will be added to the SA database. The prototype system based on DBGS was tested on eight able-bodied volunteers and three individuals with stroke to verify its capacity for stimulation removal and sEMG extraction. Results. The average stimulus artefact attenuation factor, SA index and correlation coefficient between clean sEMG and extracted sEMG for 6th-order DBGS were 12.77 ± 0.85 dB, 1.82 ± 0.37 dB and 0.84 ± 0.33 dB, respectively, which were significantly higher than those for empirical mode decomposition combined with notch filters, pulse-triggered GS algorithm, 1st-order and 3rd-order DBGS. The sEMG-torque correlation coefficients were 0.78 ± 0.05 and 0.48 ± 0.11 for able-bodied volunteers and individuals with stroke, respectively. Significance. The proposed hybrid method can extract sEMG during dynamic FES in real time.

It is still challenging to achieve a complex grasp or fine finger control by using surface functional electrical stimulation (FES), which usually requires a precise electrode configuration under laboratory or clinical settings. The goals of this study are as follows: 1) to study the possibility of selectively activating individual fingers; 2) to investigate whether the current activation threshold and selective range of individual fingers are affected by two factors: changes in the electrode position and forearm rotation (pronation, neutral and supination); and 3) to explore a theoretical model for guidance of the electrode placement used for selective activation of individual fingers. A coordinate system with more than 400 grid points was established over the forearm skin surface. A searching procedure was used to traverse all grid points to identify the stimulation points for finger extension/flexion by applying monophasic stimulation pulses. Some of the stimulation points for finger extension and flexion were selected and tested in their respective two different forearm postures according to the number and the type of the activated fingers and the strength of finger action response to the electrical stimulation at the stimulation point. The activation thresholds and current ranges of the selectively activated finger at each stimulation point were determined by visual analysis. The stimulation points were divided into three groups ("Low", "Medium" and "High") according to the thresholds of the 1st activated fingers. The angles produced by the selectively activated finger within selective current ranges were measured and analyzed. Selective stimulation of extension/flexion is possible for most fingers. Small changes in electrode position and forearm rotation have no significant effect on the threshold amplitude and the current range for the selective activation of most fingers (p > 0.05). The current range is the largest (more than 2 mA) for selective activation of the thumb, followed by those for the index, ring, middle and little fingers. The stimulation points in the "Low" group for all five fingers lead to noticeable finger angles at low current intensity, especially for the index, middle, and ring fingers. The slopes of the finger angle variation in the "Low" group for digits 2~4 are inversely proportional to the current intensity, whereas the slopes of the finger angle variation in other groups and in all groups for the thumb and little finger are proportional to the current intensity. It is possible to selectively activate the extension/flexion of most fingers by stimulating the forearm muscles. The physiological characteristics of each finger should be considered when placing the negative electrode for selective stimulation of individual fingers. The electrode placement used for the selective activation of individual fingers should not be confined to the location with the lowest activation threshold.

The voluntary participation of the paralyzed patients is crucial for the functional electrical stimulation (FES) therapy. In this study, we developed a strategy called “EMG Bridge” (EMGB) for volitional control of multiple movements using FES technique. The surface electromyography (sEMG) signals of the agonist muscles were transformed to stimulation pulses with various pulse width and frequency to stimulate the target paralyzed muscles using MAV/NSS co-modulation (MNDC) algorithm we proposed recently. Motion pattern classification based on linear discriminant analysis (LDA) was included to recognize the motion status and mapping the sEMG detection channel to the corresponding stimulation channel. A prototype EMGB system was built for real-time control of four hand movements. The test results showed that the movements can be reproduced with a successful rate of 92.5±3.5%. The angle trajectory of wrist joint and metacarpal-phalangeal joint can be mimicked with a maximum cross-correlation coefficient > 0.84 and a latency less than 300 ms.

In this paper, a novel distributed functional electrical stimulation (FES) and assessment system for hand movement using wearable technology is proposed. It consists of an FES of wireless EMG-bridge (EMGB) type, a sensor glove and a smartphone-based application software for displaying the virtual 3D hand environment. Combined with the FES therapy and the sensor glove, the proposed system provides a way to evaluate individual responses to rehabilitation among the stroke patients. The smartphone-based application software for displaying the virtual 3D hand environment has been applied to enhance the engagement and motivation needed to drive neuroplastic changes. The prototype system has been validated by performing wrist and finger extension on one healthy subject.

This work presents motor function rebuilding of paralyzed limbs of hemiplegic patients after stroke and anergic limbs of senile people. The biomedical methods and the traditional physical methods for rehabilitation of the paralyses are reviewed. The core part is to discuss EMG-signal regeneration and the limb function rebuilding based on communication principles and functional electrical stimulation–a novel concept developed by the speakers. For the EMG-signal transmission, different radio-frequency (RF) transmission systems are utilized. The construction of the whole bio-electronic system and the preliminary experiments on healthy patients are demonstrated.

In this study, we present an alternating controlled functional electrical stimulation (FES) strategy for rehabilitation of lower extremity motor function of hemiplegia after stroke. The muscle activity onset time, determined by using sample entropy (SampEn) analysis of an electromyographic (EMG) signal, is used as a trigger for FES to manage stimulations. The EMG-bridge (EMGB) type FES is a novel motor functional rehabilitation idea that it exploits sEMG signal from a healthy limb to regulate the stimulus parameters of stimulations applied to the paralyzed limb, so as to achieve synchronous movement of bilateral or different limbs. The alternating controlled FES strategy was realized on the basis of combing muscle activity onset time with EMGB-type FES system. Using this FES control strategy, experiments on a healthy subject have been carried out successfully to realize alternating stimulation to plantar flexor (PF) and dorsiflexor (DF) muscles of lower limb in sitting position.

In this presentation the motor function rebuilding of paralyzed limbs of hemiplegic patients after stroke and paraplegic patients due to spinal cord injury is concerned. Novel biomedical methods and traditional physical methods for the rehabilitation of two kinds of paralyses are reviewed. The core part is to discuss the electromyography signal bridge and the rebuilding of limb functions based on principles of communication of body sensor network and functional electrical stimulation. For the communication part, a ZigBee system and a super-regenerative system are incorporated. The construction of the whole bio-electronic system, the animal experiments, and the preliminary experiments on healthy subjects and paralyzed patients will be demonstrated.

The micro-electronic neural bridge (MENB) aims to rebuild lost motor function of paralyzed humans by routing movement-related signals from the brain, around the damage part in the spinal cord, to the external effectors. This study focused on the prototype system design of the MENB, including the principle of the MENB, the neural signal detecting circuit and the functional electrical stimulation (FES) circuit design, and the spike detecting and sorting algorithm. In this study, we developed a novel improved amplitude threshold spike detecting method based on variable forward difference threshold for both training and bridging phase. The discrete wavelet transform (DWT), a new level feature coefficient selection method based on Lilliefors test, and the ${\rm k}$ -means clustering method based on Mahalanobis distance were used for spike sorting. A real-time online spike detecting and sorting algorithm based on DWT and Euclidean distance was also implemented for the bridging phase. Tested by the data sets available at Caltech, in the training phase, the average sensitivity, specificity, and clustering accuracies are 99.43%, 97.83%, and 95.45%, respectively. Validated by the three-fold cross-validation method, the average sensitivity, specificity, and classification accuracy are 99.43%, 97.70%, and 96.46%, respectively.

In this paper, a four-channel pulse-triggered functional electrical stimulator using complementary current source and time division output method is proposed for the research and application of functional electrical stimulation (FES). The high-voltage compliance and output impedance is increased by adopting the complementary current source, which can also realize the linear voltage-to-current conversion and high channel isolation. A high-voltage analog switch chip MAX14803, combined with a FIFO queue algorithm in the microprocessor, is used to setup the H-bridge and multiplexers for the four-channel time division multiplexing output. With this method, the size and cost of the key components are reduced greatly. The stimulating core circuit area is 30 × 50 mm(2). According to the experiments, the stimulator can achieve the four-channel charge-balanced biphasic stimulation with a current range between 0 and 60 mA and a single-phase pulse amplitude up to 60 V.

Voluntary participation of hemiplegic patients is crucial for functional electrical stimulation therapy. A wearable functional electrical stimulation system has been proposed for real-time volitional hand motor function control using the electromyography bridge method. Through a series of novel design concepts, including the integration of a detecting circuit and an analog-to-digital converter, a miniaturized functional electrical stimulation circuit technique, a low-power super-regeneration chip for wireless receiving, and two wearable armbands, a prototype system has been established with reduced size, power, and overall cost. Based on wrist joint torque reproduction and classification experiments performed on six healthy subjects, the optimized surface electromyography thresholds and trained logistic regression classifier parameters were statistically chosen to establish wrist and hand motion control with high accuracy. Test results showed that wrist flexion/extension, hand grasp, and finger extension could be reproduced with high accuracy and low latency. This system can build a bridge of information transmission between healthy limbs and paralyzed limbs, effectively improve voluntary participation of hemiplegic patients, and elevate efficiency of rehabilitation training.

In this presentation the motor function rebuilding of paralyzed limbs of the paraplegic patients caused by spinal cord injury and the hemiplegic patients after stroke and SCI is concerned. The biomedical methods and the traditional physical methods for the rehabilitation of two kinds of paralyses are reviewed. The core part is to discuss the neural and muscular signal regeneration and the limb function rebuilding based on the principles of communication and functional electrical stimulation - a novel concept developed by the speakers. For the communication, a microwave transmission system is incorporated. The construction of the whole bio-electronic system, the animal experiments, and the elementary experiments on healthy and paralyzed patients will be demonstrated.

Objective Surface electromyography (sEMG) is often used as a control signal in neuromuscular electrical stimulation (NMES) systems to enhance the voluntary control and proprioceptive sensory feedback of paralyzed patients. Most sEMG-controlled NMES systems use the envelope of the sEMG signal to modulate the stimulation intensity (current amplitude or pulse width) with a constant frequency. The aims of this study were to develop a strategy that co-modulates frequency and pulse width based on features of the sEMG signal and to investigate the torque-reproduction performance and the level of fatigue resistance achieved with our strategy. Approach We examined the relationships between wrist torque and two stimulation parameters (frequency and pulse width) and between wrist torque and two sEMG time-domain features (mean absolute value (MAV) and number of slope sign changes (NSS)) in eight healthy volunteers. By using wrist torque as an intermediate variable, customized and generalized transfer functions were constructed to convert the two features of the sEMG signal into the two stimulation parameters, thereby establishing a MAV/NSS dual-coding (MNDC) algorithm. Wrist torque reproduction performance was assessed by comparing the torque generated by the algorithms with that originally recorded during voluntary contractions. Muscle fatigue was assessed by measuring the decline percentage of the peak torque and by comparing the torque time integral of the response to test stimulation trains before and after fatigue sessions. Main results The MNDC approach could produce a wrist torque that closely matched the voluntary wrist torque. In addition, a smaller decay in the wrist torque was observed after the MNDC-coded fatigue stimulation was applied than after stimulation using pulse-width modulation alone. Significance Compared with pulse-width modulation stimulation strategies that are based on sEMG detection, the MNDC strategy is more effective for both voluntary muscle force reproduction and muscle fatigue reduction.

In this paper, a system aiming at motor function restoration based on myoelectric signals recognition has been presented. This system contains four main components: analog front, AD converter, digital signal processor, and functional electrical stimulator. Functions and designing consideration of each component, and a myoelectric signals recognition algorithm with low computational complexity have been introduced. Finally, the testing results of the system will be presented and the qualified performance for the further animal experiment is demonstrated.

Electromyographic (EMG) signals are the resultant of electrical activity of muscle fibers during a muscle contraction, whose pattern can provide a significant reference of a motor rehabilitation system. The EMG decoding method using “refractory period” and “threshold” is appropriate for real-time processing system due to its low algorithm complexity and the good fidelity of time domain information. In this paper, the distribution of intervals between continuous wavelet transform modulus maxima was analyzed to provide a reasonable determination of the “refractory period”. In addition, the source signals were decoded according to the “refractory period”. Promising results are demonstrated.

Hemiparesis is one of the most common consequences of stroke. Advanced rehabilitation techniques are essential for restoring motor function in hemiplegic patients. Functional electrical stimulation applied to the affected limb based on myoelectric signal from the unaffected limb is a promising therapy for hemiplegia. In this study, we developed a prototype system for evaluating this novel functional electrical stimulation-control strategy. Based on surface electromyography and a vector machine model, a self-administered, multi-movement, force-modulation functional electrical stimulation-prototype system for hemiplegia was implemented. This paper discusses the hardware design, the algorithm of the system, and key points of the self-oscillation-prone system. The experimental results demonstrate the feasibility of the prototype system for further clinical trials, which is being conducted to evaluate the efficacy of the proposed rehabilitation technique.