Wearable Resistive-based Gesture-Sensing Interface Bracelet
18 Jul 2019-pp 1-4
TL;DR: Developed wearable resistive-based wrist-worn gesture sensing system classifies the hand gesture with high accuracy (>70%) and the results are displayed on the GUIDE user interface.
Abstract: This paper presents a gesture recognition system based on the pressure changes produced by wrist tendon movements for wearable devices. The data of the pressure variations are captured by means of flexible and ultrathin force resistive sensors. A learning algorithm, Support Vector Machine, helps the system to distinguish various hand gestures through developed programming on MATLAB after extracting the key features of data. In order to achieve rapid gesture recognition with a shorter computational time, higher precision and less space complexity, genetic optimization algorithm is used to find the optimal parameter c (cost factor) and g (kernel function parameters) in SVM algorithm. The SVM parameter optimization improves the classification accuracy and the performance of the classifier. Finally, developed wearable resistive-based wrist-worn gesture sensing system classifies the hand gesture with high accuracy (>70%) and the results are displayed on the GUIDE user interface.
Citations
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TL;DR: A novel fully–textile chipless tag for gesture recognition applications is presented by associating to each finger an open ended stub that is electrically connected or not to the microstrip line depending on the finger position.
Abstract: In this paper a novel fully–textile chipless tag for gesture recognition applications is presented. The proposed tag consists of a microstrip line with transmission zeros controlled by the position of the fingers. This is obtained by associating to each finger an open ended stub that is electrically connected or not to the microstrip line depending on the finger position. A three–finger prototype fabricated by using a non woven conductive fabric and suitable to be integrated into a glove is presented and discussed. The reported results demonstrate the suitability of using the proposed tag for gesture recognition applications.
9 citations
TL;DR: In this paper , a wearable sensing system for high-density resistive array readout is presented, which is implemented on a printed circuit board (PCB) and had a compact dimension of 3 cm × 3 cm.
Abstract: This work presents a wearable sensing system for high-density resistive array readout. The system comprising readout electronics for a high-density resistive sensor array and a rechargeable battery, was realized in a wristband. The analyzed data with the proposed system can be visualized using a custom graphical user interface (GUI) developed in a personal computer (PC) through a universal serial bus (USB) and using an Android app in smartphones via Bluetooth Low Energy (BLE), respectively. The readout electronics were implemented on a printed circuit board (PCB) and had a compact dimension of 3 cm × 3 cm. It was designed to measure the resistive sensor with a dynamic range of 1 KΩ–1 MΩ and detect a 0.1% change of the base resistance. The system operated at a 5 V supply voltage, and the overall system power consumption was 95 mW. The readout circuit employed a resistance-to-voltage (R-V) conversion topology using a 16-bit analog-to-digital converter (ADC), integrated in the Cypress Programmable System-on-Chip (PSoC®) 5LP microcontroller. The device behaves as a universal-type sensing system that can be interfaced with a wide variety of resistive sensors, including chemiresistors, piezoresistors, and thermoelectric sensors, whose resistance variations fall in the target measurement range of 1 KΩ–1 MΩ. The system performance was tested with a 60-resistor array and showed a satisfactory accuracy, with a worst-case error rate up to 2.5%. The developed sensing system shows promising results for applications in the field of the Internet of things (IoT), point-of-care testing (PoCT), and low-cost wearable devices.
5 citations
01 Aug 2020
TL;DR: The paper explored how predict accuracy would be impacted when twisting the wrist and showed that a gesture in different angles was classified as different gestures.
Abstract: This paper aim to design a smart wristband for gesture recognition. Tendon movements around the wrist were measured by FSR sensors as input variables to classify different gestures. Polydimethylsiloxane material (PDMS) was applied to encapsulate FSR sensors, so that the wristband is flexible and suitable for people with different wrist sizes. Subsequently, the sensor data was transmitted to the computer via Bluetooth low energy (BLE) technology. MATLAB was used to train a classifier with ensemble subspace discrimination algorithm. After that, the received signal was processed by this trained classifier and made prediction. The accuracy is about 99.4%. Additionally, the paper explored how predict accuracy would be impacted when twisting the wrist. The result showed that a gesture in different angles was classified as different gestures. Overall, the wristband is rechargeable, portable and can accurately recognize over 6 gestures.
3 citations
Cites methods from "Wearable Resistive-based Gesture-Se..."
...Piezoelectric sensors [4], surface electromyography (SEMG) sensors [5] and Force-sensing resistor (FSR) sensors [6] are applicable for designing a gesture recognition wristband....
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DOI•
TL;DR: A double-layer flexible optical fiber crossover structure is proposed to improve the measurement accuracy of the sensor for human body signals and demonstrate that the FOFT sensor has a huge potential in HMI and healthcare.
Abstract: Flexible sensors are the main components of wearable devices. Compared with flexible substrate-based skin-like sensors, flexible textile sensors have the intrinsic advantages of excellent breathability, compliance with the human body and comfort to human skin. In this article, a flexible optical fiber-based smart textile (FOFT) sensor has been proposed for the application of intelligent human–machine interaction (HMI) and healthcare. The FOFT sensor integrated fiber Bragg gratings (FBGs) with yarns in the way of the plain weave. A double-layer flexible optical fiber crossover structure is proposed to improve the measurement accuracy of the sensor for human body signals. Such a structure leads to a highly flexible textile and wearing comfort. The weak characteristic information of the human body can be extracted by the sensor based on the wavelength demodulation of double FBGs. Combined with the backpropagation (BP) neural network, an FOFT sensor-based gesture recognition application with an off-line accuracy of 97.02% has been developed. Furthermore, the FOFT sensor is used to monitor the human respiratory signals under different postures and obtain the respiratory rate. A series of experiments demonstrate that the FOFT sensor has a huge potential in HMI and healthcare.
3 citations
TL;DR: In this article , the authors used principal component analysis (PCA) and k-nearest neighbor (kNN) classifier to identify the bioimpedance patterns of six hand gestures.
Abstract: Electrical impedance tomography (EIT) is based on the physical principle of bioimpedance defined as the opposition that biological tissues exhibit to the flow of a rotating alternating electrical current. Consequently, here, we propose studying the characterization and classification of bioimpedance patterns based on EIT by measuring, on the forearm with eight electrodes in a non-invasive way, the potential drops resulting from the execution of six hand gestures. The starting point was the acquisition of bioimpedance patterns studied by means of principal component analysis (PCA), validated through the cross-validation technique, and classified using the k-nearest neighbor (kNN) classification algorithm. As a result, it is concluded that reduction and classification is feasible, with a sensitivity of 0.89 in the worst case, for each of the reduced bioimpedance patterns, leading to the following direct advantage: a reduction in the numbers of electrodes and electronics required. In this work, bioimpedance patterns were investigated for monitoring subjects’ mobility, where, generally, these solutions are based on a sensor system with moving parts that suffer from significant problems of wear, lack of adaptability to the patient, and lack of resolution. Whereas, the proposal implemented in this prototype, based on the so-called electrical impedance tomography, does not have these problems.
3 citations
References
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TL;DR: A new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features, which is comparable to recently published SVM-based whole-brain classification methods.
392 citations
"Wearable Resistive-based Gesture-Se..." refers methods in this paper
...Support vector machine is a method based on multidimensional classification boundary [8]....
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TL;DR: The aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets and to compare the classification accuracy, ROC, F-measure, and computational times of training SVM.
Abstract: Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.
229 citations
"Wearable Resistive-based Gesture-Se..." refers methods in this paper
...Therefore, we chose this algorithm in this paper because of relatively few sample requirements, nonlinear and high dimensional pattern recognition [5-7]....
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TL;DR: A self-training semi-supervised support vector machine (SVM) algorithm and its corresponding model selection method, which are designed to train a classifier with small training data are presented and the convergence of this algorithm is proved.
221 citations
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05 Oct 2014
TL;DR: Using an array of force sensitive resistors worn around the wrist, the interface can distinguish subtle finger pinch gestures with high accuracy (>80 %) and speed and it is demonstrated that the number of gestures can be extended with orientation data from an accelerometer.
Abstract: In this paper we present WristFlex, an always-available on-body gestural interface. Using an array of force sensitive resistors (FSRs) worn around the wrist, the interface can distinguish subtle finger pinch gestures with high accuracy (>80 %) and speed. The system is trained to classify gestures from subtle tendon movements on the wrist. We demonstrate that WristFlex is a complete system that works wirelessly in real-time. The system is simple and light-weight in terms of power consumption and computational overhead. WristFlex's sensor power consumption is 60.7 uW, allowing the prototype to potentially last more then a week on a small lithium polymer battery. Also, WristFlex is small and non-obtrusive, and can be integrated into a wristwatch or a bracelet. We perform user studies to evaluate the accuracy, speed, and repeatability. We demonstrate that the number of gestures can be extended with orientation data from an accelerometer. We conclude by showing example applications.
191 citations
Additional excerpts
...The FSR were selected in this system due to its light weight, small size, high sensing accuracy, ultra-thin material and low power consumption [14]....
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TL;DR: With the implementation of the incremental process, training meetings, the need for large-scale data storage space, result in slow training, the online learning algorithm based on VSVM can solve the problem.
Abstract: In view of the long execution time and low execution efficiency of Support Vector Machine in large-scale training samples, the paper has proposed the online incremental and decremental learning algorithm based on variable support vector machine (VSVM). In deep understanding of the operation mechanism and correlation algorithms for VSVM, each sample has increased training datasets changes and it needs to update the classifier of learning algorithm. Firstly, they are given the online growth amount of learning algorithm taken full advantage of the incremental pre-calculated information, and doesn’t require retraining for the new incremental training datasets. Secondly, the incremental matrix inverse calculation process had greatly reduced the running time of algorithm, and it is given in order to verify out the validity of the online learning algorithm. Finally, the nine groups of datasets in the standard library have been selected in the pattern classification experiment. The experimental results are shown that the online learning algorithm given in the case to ensure the correct classification rates and effective training’s speed. With the implementation of the incremental process, training meetings, the need for large-scale data storage space, result in slow training, the online learning algorithm based on VSVM can solve the problem.
140 citations
"Wearable Resistive-based Gesture-Se..." refers methods in this paper
...Therefore, we chose this algorithm in this paper because of relatively few sample requirements, nonlinear and high dimensional pattern recognition [5-7]....
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