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Register a new userNovel Time Domain Based Upper-Limb Prosthesis Control using Incremental Learning Approach
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The upper limb of the body is a vital for various kind of activities for human. The complete or partial loss of the upper limb would lead to a significant impact on daily activities of the amputees. EMG carries important information of human physique which helps to decode the various functionalities of human arm. EMG signal based bionics and prosthesis have gained huge research attention over the past decade. Conventional EMG-PR based prosthesis struggles to give accurate performance due to off-line training used and incapability to compensate for electrode position shift and change in arm position. This work proposes online training and incremental learning based system for upper limb prosthetic application. This system consists of ADS1298 as AFE (analog front end) and a 32 bit arm cortex-m4 processor for DSP (digital signal processing). The system has been tested for both intact and amputated subjects. Time derivative moment based features have been implemented and utilized for effective pattern classification. Initially, system have been trained for four classes using the on-line training process later on the number of classes have been incremented on user demand till eleven, and system performance has been evaluated. The system yielded a completion rate of 100% for healthy and amputated subjects when four motions have been considered. Further 94.33% and 92% completion rate have been showcased by the system when the number of classes increased to eleven for healthy and amputees respectively. The motion efficacy test is also evaluated for all the subjects. The highest efficacy rate of 91.23% and 88.64% are observed for intact and amputated subjects respectively.
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In EMG based pattern recognition (EMG-PR), deep learning-based techniques have become more prominent for their self-regulating capability to extract discriminant features from large data-sets. Moreover, the performance of traditional machine learning-based methods show limitation to categorize over a certain number of classes and degrades over a period of time. In this paper, an accurate, robust, and fast convolutional neural network-based framework for EMG pattern identification is presented. To assess the performance of the proposed system, five publicly available and benchmark data-sets of upper limb activities were used. This data-set contains 49 to 52 upper limb motions (NinaPro DB1, NinaPro DB2, and NinaPro DB3), Data with force variation, and data with arm position variation for intact and amputated subjects. The classification accuracies of 91.11% (53 classes), 89.45% (49 classes), 81.67% (49 classes of amputees), 95.67% (6 classes with force variation), and 99.11% (8 classes with arm position variation) have been observed during the testing and validation. The performance of the proposed system is compared with the state of art techniques in the literature. The findings demonstrate that classification accuracy and time complexity have improved significantly. Keras, TensorFlows high-level API for constructing deep learning models, was used for signal pre-processing and deep-learning-based algorithms. The suggested method was run on an Intel 3.5GHz Core i7, 7th Gen CPU with 8GB DDR4 RAM.
A brain-machine interface (BMI) based on electroencephalography (EEG) can overcome the movement deficits for patients and real-world applications for healthy people. Ideally, the BMI system detects user movement intentions transforms them into a control signal for a robotic arm movement. In this study, we made progress toward user intention decoding and successfully classified six different reaching movements of the right arm in the movement execution (ME). Notably, we designed an experimental environment using robotic arm movement and proposed a convolutional neural network architecture (CNN) with inception block for robust classify executed movements of the same limb. As a result, we confirmed the classification accuracies of six different directions show 0.45 for the executed session. The results proved that the proposed architecture has approximately 6~13% performance increase compared to its conventional classification models. Hence, we demonstrate the 3D inception CNN architecture to contribute to the continuous decoding of ME.
Brain-computer interface (BCI) technologies have been widely used in many areas. In particular, non-invasive technologies such as electroencephalography (EEG) or near-infrared spectroscopy (NIRS) have been used to detect motor imagery, disease, or mental state. It has been already shown in literature that the hybrid of EEG and NIRS has better results than their respective individual signals. The fusion algorithm for EEG and NIRS sources is the key to implement them in real-life applications. In this research, we propose three fusion methods for the hybrid of the EEG and NIRS-based brain-computer interface system: linear fusion, tensor fusion, and $p$th-order polynomial fusion. Firstly, our results prove that the hybrid BCI system is more accurate, as expected. Secondly, the $p$th-order polynomial fusion has the best classification results out of the three methods, and also shows improvements compared with previous studies. For a motion imagery task and a mental arithmetic task, the best detection accuracy in previous papers were 74.20% and 88.1%, whereas our accuracy achieved was 77.53% and 90.19% . Furthermore, unlike complex artificial neural network methods, our proposed methods are not as computationally demanding.
In this paper, we propose a frequency-time division network (FreqTimeNet) to improve the performance of deep learning (DL) based OFDM channel estimation. This FreqTimeNet is designed based on the orthogonality between the frequency domain and the time domain. In FreqTimeNet, the input is processed by parallel frequency blocks and parallel time blocks in sequential. Introducing the attention mechanism to use the SNR information, an attention based FreqTimeNet (AttenFreqTimeNet) is proposed. Using 3rd Generation Partnership Project (3GPP) channel models, the mean square error (MSE) performance of FreqTimeNet and AttenFreqTimeNet under different scenarios is evaluated. A method for constructing mixed training data is proposed, which could address the generalization problem in DL. It is observed that AttenFreqTimeNet outperforms FreqTimeNet, and FreqTimeNet outperforms other DL networks, with acceptable complexity.
While showing great promise, circuit synthesis techniques that combine numerical optimization with search over circuit structures face scalability challenges due to large number of parameters, exponential search spaces, and complex objective functions. The LEAP algorithm improves scaling across these dimensions using iterative circuit synthesis, incremental reoptimization, dimensionality reduction, and improved numerical optimization. LEAP draws on the design of the optimal synthesis algorithm QSearch by extending it with an incremental approach to determine constant prefix solutions for a circuit. By narrowing the search space, LEAP improves scalability from four to six qubit circuits. LEAP was evaluated with known quantum circuits such as QFT and physical simulation circuits like the VQE, TFIM and QITE. LEAP is able to compile four qubit unitaries up to $59times$ faster than QSearch and five and six qubit unitaries with up to $1.2times$ fewer CNOTs compared to the advanced QFAST package. LEAP is able to reduce the CNOT count by up to $48times$, or $11times$ on average, compared to the IBM Qiskit compiler. Although employing heuristics, LEAP has generated optimal depth circuits for all test cases where a solution is known a priori. The techniques introduced by LEAP are applicable to other numerical optimization based synthesis approaches.
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