An advanced cost-efficient IoT method for stroke rehabilitation using smart gloves

Stroke represents a severe,widespread,and widely acknowledged health crisis on both national and international levels.It is one of the most prevalent life-threatening conditions.Despite impressive advances in treating stroke,in addition to a need for effective patient care services,many sufferers still rely solely on physical interventions.The present paper describes and explains the use of a newly designed gadget for stroke survivors who cannot move their fingers.This is a sophisticated mobile device that enables stroke patients to regain their muscle memory and thus their ability to perform repetitive actions by continuing to tighten and stretch their muscles without the intervention of a physiotherapist.Gamification methodology is used to encourage patients to become involved in the process of rehabilitation.The device also has sensors that take information and transmit it to an app through an ESP32 connection.This enables physicians to view glove usage information remotely and keep track of an individual patient’s health.Communication between app and glove is facilitated by a broker in the Amazon Web Service IoT.With the robotic glove presented here,the recovery rate is found to be 90.23%over four weeks’duration,which represents a significant improvement compared with existing hospital-based rehabilitation techniques.

Fever glove hand-shake method safe blood collection from children's fingertips in COVID-19 fever clinic

BACKGROUND During the coronavirus disease 2019(COVID-19)epidemic,the fever clinic is an important link for screening and diagnosing whether a patient is infected with the novel coronavirus.Blood collection from children’s fingertips is a commonly used detection method;however,in children,the blood collection process may cause discomfort and resistance.To address this problem,the use of heating gloves combined with hand swinging can be considered for fingertip blood collection in children.AIM To explore the application of fever gloves with the handshaking method for fingertip blood collection from children in fever clinics during the COVID-19 epidemic.METHODS A total of 100 children were selected for fingertip blood collection at the fever clinic of our hospital from June 2022 to June 2023 and were divided into two groups using a randomized numerical table method,with 50 cases in each group,including the control and observation groups.The patients in the control group followed the doctor's instructions to cooperate with the routine fingertip blood collection method,and the patients in the observation group followed the doctor's instructions to cooperate with the static fever gloves with the shaking hands method of children's fingertip blood collection.The level of the six blood routine and collection indexes,and the satisfaction of the examination of the patients in the peripheral blood group and the fever gloves with the shaking hands method of the children's fingertip blood collection group were compared.RESULTS The red and white blood cell count,hemoglobin,and red blood cell pressure volume in the observation group were higher than those in the control group(P<0.05);the platelet count in the control group was lower than that in the observation group(P<0.05);the number of times of squeezing the fingertip,the average time of blood collection,and the score of puncture pain in the observation group were significantly better than those in the control group(P<0.05);and satisfaction with the routine blood examination in the observation group was greater than that in the control group.CONCLUSION The application value of the fever gloves with shaking hands method for children's fingertip blood collection was better,the accuracy of examination indexes was higher,and patient satisfaction with the examination was greater.

CAS-Glove型数据手套运动建模与软件系统开发

数据手套是虚拟现实系统当中一种通用而且非常重要的交互设备。本文首先介绍了一种新型数据手套CAS-Glove,然后根据手指的生理约束条件,以及CAS-Glove中传感器的布置和特点,建立了一种改进的手指运动模型。最后对与其相配套的软件系统的组成、主要功能、实用的标定方法和数据处理过程做了详细介绍。

基于GloVe词向量的“技术——应用”发现研究

[目的/意义]本文探讨挖掘技术和应用间的关联关系,便于把握技术的目标应用场景和了解应用需求的可选技术方案,为技术转化提供基础。[方法/过程]首先,建立基于改进GloVe词向量的"技术——应用"发现模型,提出改进词向量的算法;然后,以机器学习技术为例,获取该领域学术论文文摘数据,预处理后建立训练文本集合,再使用改进GloVe模型训练获得词向量;最后,对词向量聚类获得"技术"和"应用"类词汇集,通过共现获得两者之间的关联并分析。[结果/结论]实验结果表明,本文改进GloVe模型后训练的词向量性能获得提升,聚类效果好,最终实现"技术——应用"的关联分析。

基于GloVe模型和注意力机制Bi-LSTM的文本分类方法

为了提高文本分类的准确性,扩展分类任务的多样性,提出一种结合一维卷积神经网络(1D-CNN)和双向长短期记忆网络(Bi-LSTM)的文本分类方法。首先,为了解决近义词、多义词的表征困难,采用GloVe模型表示词特征,充分利用全局信息和共现窗口的优势。然后,利用1D-CNN进行特征提取,以降低分类器或预测模型的输入特征维数。最后,对分类模块Bi-LSTM进行优化,其隐藏层由两个残差块组成,并引入注意力机制进一步改善预测的准确度。在多个公开数据集中进行二元分类和多元主题分类实验。实验结果表明,与其他优秀方法相比,所提方法在准确率、召回率和F1得分方面的性能更优,最高准确度达92.5%,最高F1得分为91.3%。

基于GloVe-BiLSTM的在线研讨信息分类模型研究

[目的/意义]把握并识别在线研讨信息类型,有助于研讨用户快速获取研讨信息、减轻认知负担、形成对研讨状态的正确判断,从而提升研讨效率。[方法/过程]基于深度学习的文本语义理解和挖掘,构建在线研讨信息分类识别的深度学习组合模型GloVe-BiLSTM,进行在线研讨信息的自动化分类预测。利用GloVe对待分类文本进行训练,以获得字词级别的向量,再将词向量输入BiLSTM层提取语义特征,最后输入新的在线研讨文本以得到分类预测的最终结果。[结果/结论]针对CMV社区在线研讨信息的实验表明,构建的GloVe-BiLSTM组合模型在分类准确率、精确率、召回率、F1值等方面均具有出色表现,能够有效实现在线研讨信息的类型分类,为在线研讨社区服务优化提供参考。

融合LDA和GloVe模型的病症文本聚类算法

针对隐含狄利克雷分布(LDA)模型特征提取时忽略语义信息的问题,提出一种融合LDA和全局文本表示(GloVe)模型的病症文本聚类算法LG&K-Medoide。首先,利用LDA对病症文本数据建模,采用JS(Jensen-Shannon)距离计算文本相似度;其次,利用GloVe对病症文本数据建模获取词向量,根据病症词性贡献度,对词向量权重进行标注,采用余弦距离计算基于GloVe建模加权的文本相似度;最后,将两种相似度进行结合,改进距离公式,实现K-Medoide聚类。实验结果表明,LG&K-Medoide算法较基于LDA,LDA+TF-IDF,LDA+Word2Vec模型的聚类算法具有较高的精度。

结合GloVe和GRU的文本分类模型

文本分类有着广泛的应用,对其分类算法的研究也一直备受关注。但是,传统文本分类算法普遍存在文本特征向量化维度过高、没有考虑关键词之间语义关系、训练参数过多等问题,这些都将影响到分类准确率等性能。针对这些问题,提出了一种结合词向量化与GRU的文本分类算法。对文本进行预处理操作;通过GloVe进行词向量化,尽可能多地蕴含文本语义和语法信息,同时降低向量空间维度;再利用GRU神经网络模型进行训练,最大程度保留长文本中长距离词之间的语义关联。实验结果证明,该算法对提高文本分类性能有较明显的作用。

Robotic Electrotactile Feedback Glove for Tele Presence System

Progress is described regarding the development of a new electrotactile feedback glove designed for application to dexterous robot. The sensitivity of operator's finger against electrical stimulus pulse is considered. It is found that frequency, duty ratio, and voltage amplitude of electrical stimulus pulse determine the sensitivity of finger. The effects of materials, sizes, arrangements and shapes of electrodes on sensitivity of finger are analyzed. Finally, the tactile tele presence system is designed to experimentally confirm that the robot with electrotactile feedback glove can manipulate dexterous robotic multi fingered hand and identify and classify three sorts of objects.

基于GloVe与SVM的文本分类研究

词向量在自然语言处理中起着重要作用,近年来受到越来越多学者关注。然而,在词向量研究中,基于Word2vec词向量的应用研究居多,对于GloVe词向量的应用研究却很少。因此,将GloVe词向量模型与支持向量机(SVM)相结合,利用GloVe词向量模型进行特征提取与选择,利用SVM进行分类,并与Word2vec词向量结合SVM作实验对比。实验结果表明,GloVe词向量特征提取与SVM分类相结合的方法能够取得较好的准确率、召回率及F值,因此在新闻文本分类中具有一定应用价值。

基于GloVe模型的词向量改进方法

使用词向量表示方法能够很好的捕捉词语的语法和语义信息,为了能够提高词向量语义信息表示的准确性,本文通过分析GloVe模型共现矩阵的特点,利用分布式假设,提出了一种基于GloVe词向量训练模型的改进方法.该方法主要通过对维基百科统计词频分析,总结出过滤共现矩阵中无关词和噪声词的一般规律,最后给出了词向量在词语类比数据集和词语相关性数据集的评估结果.实验表明,在相同的实验环境中,本文的方法能够有效的缩短词向量的训练时间,并且在词语语义类比实验中准确率得到提高.

Full‑Fiber Auxetic‑Interlaced Yarn Sensor for Sign‑Language Translation Glove Assisted by Artificial Neural Network

Yarn sensors have shown promising application prospects in wearable electronics owing to their shape adaptability, good flexibility, and weavability. However, it is still a critical challenge to develop simultaneously structure stable, fast response, body conformal, mechanical robust yarn sensor using full microfibers in an industrial-scalable manner. Herein, a full-fiber auxetic-interlaced yarn sensor(AIYS) with negative Poisson’s ratio is designed and fabricated using a continuous, mass-producible, structure-programmable, and low-cost spinning technology. Based on the unique microfiber interlaced architecture, AIYS simultaneously achieves a Poisson’s ratio of-1.5, a robust mechanical property(0.6 c N/dtex), and a fast train-resistance responsiveness(0.025 s), which enhances conformality with the human body and quickly transduce human joint bending and/or stretching into electrical signals. Moreover, AIYS shows good flexibility, washability, weavability, and high repeatability. Furtherly, with the AIYS array, an ultrafast full-letter sign-language translation glove is developed using artificial neural network. The sign-language translation glove achieves an accuracy of 99.8% for all letters of the English alphabet within a short time of 0.25 s. Furthermore, owing to excellent full letter-recognition ability, real-time translation of daily dialogues and complex sentences is also demonstrated. The smart glove exhibits a remarkable potential in eliminating the communication barriers between signers and non-signers.

一种改进的GloVe词向量表示学习方法

GloVe模型是一种广泛使用的词向量表示学习的模型。许多研究发现,学习得到的词向量维数越大,性能越好;但维数越大,模型学习耗时越长。事实上,GloVe模型中,耗时主要表现在两方面,一是统计词对共现矩阵,二是训练学习词向量表示。该文在利用GloVe模型统计语料中词对共现时,基于对称或非对称窗口得到两个共现矩阵,然后分别学习得到较低维度的词向量表示,再拼接得到较高维度的词向量表示。从计算的复杂度来看,该文方法并不会产生多的计算量,但显然统计共现矩阵和训练学习可通过并行方式实现,能够显著提高计算效率。在使用大规模语料的实验中,以对称和非对称窗口分别统计得到共现矩阵,分别学习得到300维词向量表示,再使用拼接方式得到600维词向量表示。与GloVe模型对称和非对称的600维的词向量相比,在中文和英文的词语推断任务上,显著地提高了预测的准确率,在词语聚类任务上,有较好的聚类效果,验证了该文方法的有效性。

Learning the signatures of the human grasp using a scalable tactile glove

Humans can sense, weigh and grasp different objects, deduce their physical properties at the same time, and exert appropriate forces – a challenging task for modern robots. Studying the mechanics of human grasping objects will play a supplementary role in visual-based robot object processing. These tools require large-scale tactile data sets with high spatial resolution. However, there is no large human-grasped tactile data set covering the whole hand, because dense coverage of the human hand with tactile sensors is challenging. Hence, the capability of observing and learning from successful daily humanobject interactions is the long-term goal of aiding the development of robots and prosthetics.

Design of a Lightweight Force-Feedback Glove with a Large Workspace

A wearable force-feedback glove is a promising way to enhance the immersive sensation when a user interacts with virtual objects in virtual reality scenarios.Design challenges for such a glove include allowing a large fingertip workspace,providing a desired force sensation when simulating both free-and constrained-space interactions,and ensuring a lightweight structure.In this paper,we present a forcefeedback glove using a pneumatically actuated mechanism mounted on the dorsal side of the user’s hand.By means of a triple kinematic paired link with a curved sliding slot,a hybrid cam-linkage mechanism is proposed to transmit the resistance from the pneumatic piston rod to the fingertip.In order to obtain a large normal component of the feedback force on the user’s fingertip,the profile of the sliding slot was synthesized through an analysis of the force equilibrium on the triple kinematic paired link.A prototype five-fingered glove with a mass of 245 g was developed,and a wearable force-measurement system was constructed to permit the quantitative evaluation of the interaction performance in both free and constrained space.The experimental results confirm that the glove can achieve an average resistance of less than 0.1 N in free-space simulation and a maximum fingertip force of 4 N in constrained-space simulation.The experiment further confirms that this glove permits the finger to move freely to simulate typical grasping gestures.

Evaluation of Ability of Two Different Pneumatic Boxing Gloves to Reduce Delivered Impact Forces and Improve Safety

Two prototype pneumatic boxing gloves of different design were compared against conventional 10?oz (Std 10?oz) and 16?oz (Std 16?oz) gloves in terms of ability to reduce impact forces delivered to a target. One of the pneumatic gloves (SBLI) contained a sealed air bladder inflated to a pressure of 2?kPa. The other (ARLI) incorporated a bladder that allowed release of air to the external environment upon contact with a target, followed by rapid air reuptake. Each glove was placed on to a mechanical fist and dropped 10 times on to an in-floor force plate from each of nine heights ranging from 1.0 to 5.0 metres, with the 5-metre drop generating a peak pre-impact glove velocity close to the reported maximum for elite boxers. Compared to the conventional gloves, the ARLI glove substantially reduced peak impact forces at all drop heights, with the reduction exceeding 30% even at the 5-metre level. The SBLI glove was as effective as the ARLI glove in reducing peak impact forces at drop heights of up to 2.5 metres, but its performance then progressively diminished, and at drop heights of 4.0, 4.5 and 5.0 metres it produced peak force readings similar to those recorded for the Std 10?oz and Std 16?oz gloves. The superiority of the ARLI glove was even more evident in relation to peak rate of force development, with reductions relative to the Std 10?oz glove being ~60% at drop heights up to 3.5 metres and still ~47% at 5 metres. Peak rate of force development for the SBLI glove exceeded that for the ARLI glove for all drop heights of 2.0 metres and above, and at 4.0, 4.5 and 5.0 metres it was higher than the readings for the Std 10 oz and 16?oz gloves. The protective effect of the ARLI glove was?associated with an increase in impact compliance and prolongation of contact time between glove and target. It is concluded that a pneumatic boxing glove that provides for air exchange with the external environment can greatly reduce impact magnitudes across the whole range of pre-impact glove velocities likely to be encountered in boxing, thereby mitigating risks associated with the sport. While acceptance of the gloves by the boxing community is uncertain, opportunity may exist for almost immediate uptake in modified boxing programs.

基于VISUAL BASIC平台开发的计算机信息加密保护软件——Glove安全门

Glove安全门是一种对计算机中的重要信息进行加密保护的新型软件,详细地介绍了Glove安全门的基本概念和开发的关键技术以及基于Visual Basic语言开发Glove安全门软件的过程。

基于TextRank和GloVe的自动文本摘要算法

经典的TextRank算法在做文档的自动摘要提取时需要计算句子节点间的相似性,传统上简单的采用计数句子间相同单词的方法,但忽略了词性,近义词等诸多语义方面的重要信息。针对此类问题,本文提出了改进的基于TextRank和Glo Ve词向量的自动文本摘要算法。算法通过将词向量嵌入句子从而构建了代表句子语义相似度的概率转移矩阵,并通过数次迭代逐步调整句子的权重因子。在中文文本中的实验表明本文提出的改进算法比对照方法能提取出更高质量的摘要。

以媒体铺路,踏乐学之旅——Whose gloves第一课时教学设计

一教材简解该课的核心是让学生初步熟悉、理解服饰类单词的单复数,由于学生对于单复数概念模糊,易混淆,所以安排第一课时不引入"a pair of"。同时本课时复习四个形容词"big、small、long、hort"能让学生学会句型"The________is/are too_______.",并初步会用、会写单数句型"Whose…is this?It’s…"

Pneumatic Boxing Glove Reduces Upward Drift in Peak Force and Loading Rate over a Long Series of Impacts

A conventional boxing glove and a prototype pneumatic glove were each fitted to a mechanical fist and dropped 253 times from a height of 3 metres on to a force plate covered by an ethylene vinyl acetate (EVA) mat. Impact dynamics were measured and modelled. From the outset, peak impact force and peak rate of force development (loading rate) were lower for the pneumatic glove. For both gloves, these variables displayed upward drift during the drop series, but the drift was smaller for the pneumatic glove. Consequently, the magnitude of the protective effect provided by the pneumatic glove increased with the number of impacts. For the conventional glove, change in peak force showed a close inverse relationship to force plate contact time (R2?>?0.96) and the time from first contact of the glove with the force plate to attainment of peak force (R2?=?0.85). These relationships were much weaker for the pneumatic glove (R2?=?0.09 and 0.59 respectively), suggesting the possibility of a more complex impact damping mechanism. Following the 253 drops of the pneumatic glove, the EVA mat covering the force plate was replaced, and another 10 drops then performed. Peak force readings were immediately reduced to an extent suggesting that 26%?-?34% of the increase that had occurred over the 253 drops was attributable to impact-induced change in mat properties. This has implications for future experimental designs. Overall, the findings provided further evidence of the potential of pneumatic gloves to enable safer boxing.