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.

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…"

Performance of Prototype Pneumatic Boxing Gloves under Two Different Conditions of Target Padding

The impact damping capabilities of four different boxing gloves were assessed under two different conditions of target padding to determine whether target characteristics might influence previous conclusions concerning potential for impact mitigation through novel glove design. A conventional 10?oz glove (Std 10?oz), a conventional 16?oz glove (Std 16?oz), a prototype pneumatic glove with a sealed bladder (SBLI) and a prototype pneumatic glove with a bladder allowing air exchange with the external environment (ARLI) were each dropped three times on to a force plate from six heights ranging from 2.5 to 5.0 metres. The force plate was covered by a 50 mm thick mat of EVA material and results obtained were compared with those of an earlier experiment involving use of a similar protocol but a 25 mm thick EVA force plate covering. The thicker mat greatly reduced peak impact forces for all gloves, with values for the Std 10?oz glove becoming much closer to those reported by other researchers for punches delivered by elite boxers to crash test manikins. Peak rates of force development were also substantially decreased. Protective effects provided by the ARLI glove relative to the Std 10?oz glove were diminished but still in the order of 17%?-?22% for peak impact force and 27%?-?49% for peak rate of force development across the range of drop heights. With the 50 mm mat thickness, the SBLI glove was as effective as the ARLI glove in reducing peak impact force, whereas this was not the case with the 25 mm mat. It was, however, always inferior to the ARLI glove in decreasing peak rate of force development. The ability of the ARLI glove to afford protection across a spectrum of impact conditions could yield important practical advantages.

Effect of Pneumatic Boxing Gloves on Impact Kinematics and Their Relationship to Impact Forces

This study was aimed at improved understanding of the mechanisms of previously reported protective effects of a pneumatic boxing glove. A Motion Capture System was used to obtain velocity data from four different boxing gloves dropped on to a force plate from nine heights ranging from 1 to 5 metres. Two gloves were of the conventional type but differed in mass. The other two were prototype pneumatic gloves. One of these (SBLI) had a sealed bladder while the other (ARLI) incorporated a port allowing air exchange with the external environment. The pneumatic gloves decelerated more slowly than the conventional gloves following impact and compressed through a greater absolute distance. Consequently, they took longer to reach zero velocity. As drop height increased, these trends became more pronounced for the ARLI glove than the SBLI glove. Increase in velocity during rebound was also slower for the pneumatic gloves. The ARLI glove had a lower coefficient of restitution than any of the other gloves at low to moderate drop heights but not at high drop heights. The SBLI glove had a higher coefficient of restitution than the other gloves at all drop heights from 2 metres upwards. This indicated that, overall, the ARLI glove was the most effective, and the SBLI glove the least effective, in dissipating the kinetic energy of impact through conversion to other energy forms. For all gloves at all drop heights, peak positive acceleration at the beginning of rebound was of lower absolute magnitude than peak negative acceleration at the end of compression. The influence of drop height on an index characterising this relationship differed between the conventional and pneumatic gloves, possibly reflecting structural changes to gloves as impact energy increased. The conventional and pneumatic gloves differed regarding temporal alignment between key kinematic and kinetic events, and there were some differences between the two pneumatic gloves in this respect. Nevertheless, peak glove deceleration correlated highly with peak impact force, not only for each glove individually but also when data for all gloves were combined. The findings confirmed the potential practical utility of the ARLI glove and identified air cushion thickness, glove compressibility and capacity for air release and subsequent reuptake as critical aspects of its design.

Obstacle Avoidance Gloves for the Blind Based on Ultrasonic Sensor

Ranging based on the reflection principle of ultrasonic wave propagating in the air has been widely used in modern life, such as car reversing radar, robot automatic obstacle avoidance etc. Aiming at the situation that the blinds have no way to know whether there are obstacles or big safety risks in front of them when they are walking, this paper designed obstacle avoidance gloves for the blinds based on ultrasonic sensors. With Arduino Nano single chip microcomputer as main controller, combined with ultrasonic sensor module, bluetooth module and speaker module, the glove realized the function of obstacle detection and alarm. The main working principle is by using the ultrasonic sensors to transmit and receive ultrasonic, the time difference for transmitting and receiving to detect the distance of obstacles ahead. Besides, by means of voice module output audio signals with different frequency according to the obstacle distance, the blinds can judge the distance between them to the obstacles based on the sound with different frequencies they heard. In this way, they can make responses in advance to avoid the obstacles ahead and the happening of the risk.

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.

“互联网+”环境下融合多特征的弹幕数据情绪监测模型构建

针对企业营销活动中未充分考虑客户情感特征,导致营销效果达不到预期的问题,提出一种融合多特征的ALBERT-SA-BIGRU模型。首先,基于企业营销活动弹幕数据构建表情符号词典及相关语料库。其次,将弹幕文本与弹幕属性共同输入ALBERT模型中提取弹幕文本的特征表示,并与GloVe预先训练好的表情符号特征融合。再次,利用自注意机制捕捉表情符号与弹幕文本、弹幕属性之间的关系,将捕获的词语特征输入BiGRU中从前向、后向捕获信息,加强语义依赖,提取情感特征。最后,用Softmax逻辑回归进行情感倾向性分类,构建情绪监测图。以某互联网营销平台163253条弹幕数据对模型进行性能验证的结果表明,模型准确率、精确率、召回率分别为88.8%、88.7%、88.9%,相较于其他模型均有一定提升,可为企业在营销活动中实现用户情绪监测智能化精准营销提供支持。

用于重建物理和虚拟抓取的可重构数据手套

In this work,we present a reconfigurable data glove design to capture different modes of human hand-object interactions,which are critical in training embodied artificial intelligence(AI)agents for fine manipulation tasks.To achieve various downstream tasks with distinct features,our reconfigurable data glove operates in three modes sharing a unified backbone design that reconstructs hand gestures in real time.In the tactile-sensing mode,the glove system aggregates manipulation force via customized force sensors made from a soft and thin piezoresistive material;this design minimizes interference during complex hand movements.The virtual reality(VR)mode enables real-time interaction in a physically plausible fashion:A caging-based approach is devised to determine stable grasps by detecting collision events.Leveraging a state-of-the-art finite element method,the simulation mode collects data on fine-grained four-dimensionalmanipulation events comprising hand and object motions in three-dimensional space and how the object's physical properties(e.g.,stress and energy)change in accordance with manipulation over time.Notably,the glove system presented here is the first to use high-fidelity simulation to investigate the unobservable physical and causal factors behind manipulation actions.In a series of experiments,we characterize our data glove in terms of individual sensors and the overall sys-tem.More specifically,we evaluate the system's three modes by①recording hand gestures and associ-ated forces,②improving manipulation fluency in VR,and③producing realistic simulation effects of various tool uses,respectively.Based on these three modes,our reconfigurable data glove collects and reconstructs fine-grained human grasp data in both physical and virtual environments,thereby opening up new avenues for the learning of manipulation skills for embodied Al agents.

基于知识增强的配电网运行信息关系抽取方法

知识图谱可辅助现场人员处理配电网运行过程中积累的海量异构文本,挖掘高价值运行信息。针对知识图谱构建需求人工标注成本过高的问题,提出一种基于知识增强的远程监督关系抽取方法。利用GloVe编码与Bi-GRU网络对配电网运行语料库的句包进行向量表征;同时,新增外部知识增强模块,通过Attention-GCN模型获取编码文本的句法结构以及非线性关系,实现低成本的关系抽取,解决传统远程监督学习存在的长尾分布及噪声问题,提升关系抽取精度。实验结果表明,该方法在配电网运行信息关系抽取任务中表现优秀,相较于主流模型在精确率上提升6%。

基于专利文本语义距离的跨领域关键共性技术识别方法研究

[研究目的]跨领域关键共性技术强调技术的跨领域关联与主导作用,对其识别将为政府推动跨产业跨领域创新、提前布局相关技术领域提供决策支持。[研究方法]首先,运用GloVe模型向量化专利文本,按照专利与技术领域的语义接近程度划分专利所属领域;其次,基于GMM算法提取各领域技术主题,依据技术主题之间的语义距离,构建技术主题关联网络;最后,利用漏斗模型,依据技术主题的共性指标、跨领域指标、关键性指标筛选出跨领域关键共性技术。[研究结论]运用养老科技领域专利数据进行实证研究,结果显示“智能控制辅助技术”“无接触式躯体感知技术”“沟通与信息辅助技术”“适用性技术”为跨领域关键共性技术,通过比对国家相关政策内容,验证了方法的有效性。

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.

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%。

Simulated Microbe Removal around Finger Rings Using Different Hand Sanitation Methods

Aim It is our opinion that the CDC and the WHO have underestimated cross-contamination under examination gloves in dental clinics while wearing jewelry, such as finger rings. These agencies only ＂recommend＂ removing jewelry, and only washing hands for 15 seconds with soap and warm water before donning gloves. This study examined several washing procedures and finger rings using simulated microbes. Methodology A gloved robber hand manikin was made and fitted with a flesh disposable vinyl glove. Four fingers were fitted with rings or no ring, dusted with simulated microbes, and washed with a scrub brush for 5, 15, and 25 seconds under 20℃ and 40℃ water alone, or with liquid hand soap. Light levels （in lux） of fluorescent powder before and after washing were measured and delta scores calculated for changes in light levels, equivalent to effectiveness of hand washing procedures. A full-factorial, 3-factor analysis of variance （ANOVA） was used to test for differences among levels of the three study factors-time, temperature, and soap use. Tukey＇s post hoc honestly significant difference （HSD） test was applied to significant factors to examine pair-wise differences between factor levels. Results It was found that the longer the hands with rings were washed with a scrub brush under flowing water, the more simulated microbes were removed. By 25 seconds, all methods were essentially the same. Simulated microbes were more difficult to remove from the palm compared to the back of the hand. The liquid hand soap used in this study was more effective with warm water than cold. When given a choice of washing with cold water up to 15 seconds, it would be preferable not to use soap to remove simulated microbes. Qualitatively, the outer surface of finger rings were more effectively cleaned than the crevice below the ring, and the ring with a stone setting appeared to accumulate and retain simulated microbes more than other rings. Conclusion The most effective treatment was washing with warm water and liquid soap. Longer times were more effective. Rings should not be worn under examination gloves due to difficulty cleaning in the crevice under the ring, and the well-known consequences of cross-contamination between the patient and the health care worker.

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。

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.

Evaluation of Asbestos Exposure Associated with Research Laboratories

Asbestos containing materials (ACM) have been used for decades in research laboratory products including gloves, tong sleeves, Transite board, and other materials. These materials typically contain chrysotile asbestos with concentrations ranging up to 80%. The objective of this research was to evaluate asbestos exposure from gloves, tong sleeves, and Transite board with simulated laboratory use. An environmental chamber was used to simulate laboratory application of the products. Bulk samples of various products were tested before and after use. Personal breathing zone air samples as well as one cumulative air sample were collected during testing and followed the National Institute for Occupational Safety and Health (NIOSH) 7400/7402 methodologies. Phase contrast microscopy (PCM) and transmission electron microscopy (TEM) were used for sample analyses. Analysis of air samples using PCM, showed airborne fiber concentrations as high as 0.058 f/cc during use of tongs fitted with asbestos sleeves. Further analysis using TEM showed that the highest airborne fiber concentration was 0.0036 f/cc. Manipulation of Transite board resulted in 8-hour time-weighted-average (TWA) asbestos levels as high as 0.02 f/cc. Testing of various asbestos containing materials used in research laboratories indicate low potential for asbestos fiber exposures.

基于LCN的医疗知识问答模型

中文医疗领域分词比较困难,导致现有算法对于医疗问题特征提取不充分,针对中文分词的特点,提出基于LCN(Lattice CNN,格子卷积神经网络)的医疗知识问答模型.首先,利用某三甲医院提供的15000份电子住院记录,基于电子住院记录利用Glove模型训练医学词向量.其次,通过各大医疗网站获得大量医学名词及名词间的关系,构建医学知识图谱,并提取知识图谱中的关系词,结合已训练的词向量获取关系向量.最终,以医学词向量作为模型输入端并利用LCN神经网络提取医疗问题特征,计算问题特征与关系向量的相似度,进而训练医疗知识问答模型.实验表明,LCN模型准确率可达89.0%,与同类问答模型比较,提高了2%.

基于预训练语言模型词向量融合的情感分析研究

针对传统情感分类模型的分类效果不足,无法准确地捕捉词语之间关系的问题,提出一种基于预训练语言模型词向量融合的GE-BiLSTM(Glove-ELMO-BiLSTM)情感分析模型。通过预训练语言模型ELMO以语言模型为目的训练词向量,再与传统的Glove模型的训练结果进行运算融合,结合了全局信息以及局部上下文信息,增加了词向量矩阵的稠密度,词语之间的特征得到更好的表达,结合BiLSTM神经网络可以更好地捕捉上下文信息的关系。实验结果证明:GE-BiLSTM情感分析模型可以达到更好的分类效果,准确率比传统模型提高了2.3百分点,F1值提升了0.024。

基于加权LeaderRank的用户社交网络排序算法

针对加权LeaderRank算法存在的权值均分、主题漂移等问题,提出一种用户社交网络排序算法。结合GloVe模型、余弦相似度计算方法和牛顿冷却定律,通过引入链入链出因子、主题相关度因子和时间衰减度因子,改善加权LeaderRank算法的不足。实验结果表明,与加权LeaderRank算法相比,该算法的精确率、点击率和NDCG值分别提高7.80%、6.73%和4.75%,可有效提高排序质量。