Flexible Tactile Electronic Skin Sensor with 3D Force Detection Based on Porous CNTs/PDMS Nanocomposites

Flexible tactile sensors have broad applications in human physiological monitoring,robotic operation and human-machine interaction.However,the research of wearable and flexible tactile sensors with high sensitivity,wide sensing range and ability to detect three-dimensional(3D)force is still very challenging.Herein,a flexible tactile electronic skin sensor based on carbon nanotubes(CNTs)/polydimethylsiloxane(PDMS)nanocomposites is presented for 3D contact force detection.The 3D forces were acquired from combination of four specially designed cells in a sensing element.Contributed from the double-sided rough porous structure and specific surface morphology of nanocomposites,the piezoresistive sensor possesses high sensitivity of 12.1 kPa?1 within the range of 600 Pa and 0.68 kPa?1 in the regime exceeding 1 kPa for normal pressure,as well as 59.9 N?1 in the scope of<0.05 N and>2.3 N?1 in the region of<0.6 N for tangential force with ultra-low response time of 3.1 ms.In addition,multi-functional detection in human body monitoring was employed with single sensing cell and the sensor array was integrated into a robotic arm for objects grasping control,indicating the capacities in intelligent robot applications.

Labeling Algorithm for Face Detection Using Skin and Hair Characteristics

This research presents an algorithm for face detection based on color images using three main components： skin color characteristics, hair color characteristics, and a decision structure which converts the obtained information from skin and hair regions to labels for identifying the object dependencies and rejecting many of the incorrect decisions. Here we use face color characteristics that have a good resistance against the face rotations and expressions. This algorithm is also capable of being combined with other methods of face recognition in each stage to improve the detection.

Self-Powered Implantable Skin-Like Glucometer for Real-Time Detection of Blood Glucose Level In Vivo

Implantable bioelectronics for analyzing physiological biomarkers has recently been recognized as a promising technique in medical treatment or diagnostics. In this study, we developed a self-powered implantable skinlike glucometer for real-time detection of blood glucose level in vivo. Based on the piezo-enzymatic-reaction coupling effect of GOx@ZnO nanowire, the device under an applied deformation can actively output piezoelectric signal containing the glucose-detecting information. No external electricity power source or battery is needed for this device, and the outputting piezoelectric voltage acts as both the biosensing signal and electricity power. A practical application of the skin-like glucometer implanted in mouse body for detecting blood glucose level has been simply demonstrated. These results provide a new technique path for diabetes prophylaxis and treatment.

Skin Cancer Detection Using Temperature Variation Analysis

In the medical field, new technologies are incorporated for the sole purpose of enhancing the quality of life for the patients and even for the normal healthy people. Infrared technology is one of the technologies that have some applications in both the medical and biological fields. In this work, the thermal infrared (IR) measurement is used to investigate the potential of skin cancer detection. IR enjoys non-invasive and non-contact advantages as well as favorable cost, apparently. It is also very well developed regarding the technological and methodological aspects. IR per se is an electro-metric radiation that all objects emit when their temperature is above the absolute zero. And the human body is not different in this regard. The IR range extends, ideally, to cover wavelengths from 800 nanometer to few hundred micrometer. Cancer, in modern life, has grown tangibly due to many factors, such as life expectancies increase, personal habits and ultraviolet radiation exposures among others. Moreover, the significant enhancement of technologies has helped identifying more types of cancers than before. The sole purpose of this work is to investigate further IR technology methods and applications not yet matured in skin cancer detection to enhance the detection ability with higher safety level.

MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning

Mobile clouds are the most common medium for aggregating,storing,and analyzing data from the medical Internet of Things(MIoT).It is employed to monitor a patient’s essential health signs for earlier disease diagnosis and prediction.Among the various disease,skin cancer was the wide variety of cancer,as well as enhances the endurance rate.In recent years,many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors,including malignant melanoma(MM)and other skin cancers.However,accurate cancer detection was not performed with minimum time consumption.In order to address these existing problems,a novel Multidimensional Bregman Divergencive Feature Scaling Based Cophenetic Piecewise Regression Recurrent Deep Learning Classification(MBDFS-CPRRDLC)technique is introduced for detecting cancer at an earlier stage.The MBDFS-CPRRDLC performs skin cancer detection using different layers such as input,hidden,and output for feature selection and classification.The patient information is composed of IoT.The patient information was stored in mobile clouds server for performing predictive analytics.The collected data are sent to the recurrent deep learning classifier.In the first hidden layer,the feature selection process is carried out using the Multidimensional Bregman Divergencive Feature Scaling technique to find the significant features for disease identification resulting in decreases time consumption.Followed by,the disease classification is carried out in the second hidden layer using cophenetic correlative piecewise regression for analyzing the testing and training data.This process is repeatedly performed until the error gets minimized.In this way,disease classification is accurately performed with higher accuracy.Experimental evaluation is carried out for factors namely Accuracy,precision,recall,F-measure,as well as cancer detection time,by the amount of patient data.The observed result confirms that the proposed MBDFS-CPRRDLC technique increases accuracy as well as lesser cancer detection time compared to the conventional approaches.

Skin Detection Using Simple Arithmetic Operations

Skin detection is the primary step in a large number of computer vision applications. Speed and simplicity are vital components in many of these applications. Various methods have been implemented. However they lack either speed or simplicity or both. In previous studies, simple color component subtraction and threshold in RGB color space were used. However, in this research study, the threshold is found empirically using a known images database. In addition, all the RGB color components were used in the calculation. Optimistic results were obtained. The obtained results illustrate that this method is a promising approach used in skin detection applications.

Effect of artificial dermis combined with jinfu ning on skin healing and bacterial detection rate of finger abdomen

Objective: To explore the effect of artificial dermis combined with rhGM-CSF(Jinfuning) on healing of soft tissue defect of finger ventral skin and the influence of bacterial detection rate. Methods: Totally 110 patients with finger injury admitted to the rehabilitation department of our department from January 2017 to June 2018 were collected and divided into control group and observation group according to the random number table method with 55 cases in each group. The control group received direct artificial derma lrepairing after thorough debridement, while the observation group received recombinant gm-csf gel coating on the wound surface before artificial dermal repairing, Wound healing, wound inflammation, bacterial detection rate, inflammatory factor expression, follow-up and adverse reactions were compared between the two groups. Results: The wound healing rate of the observation group at 7, 14, 21 and 28 days after treatment was significantly higher than that of the control group (t= 11.211, P =0.000).( T = 14.895, P =0.000;T = 25.346, P=0.000;T =8.247, P=0.000). The wound healing time of the observation group was (19.7±2.3) d, and that of the control group was (27.4±3.3) d. The average wound healing time of the observation group was significantly shorter than that of the control group, and the difference was statistically significant (t=14.197, P= 0.000). Observation group wound inflammation at each time point score was significantly lower than the control group, the group rooms, time points, ·point interaction effect between the comparison, the differences were statistically significant (P <0.05), the observation group wound bacteria detection rate of 7.27% (4 cases) : the control bacteria detection rate was 21.81% (12 cases), difference was statistically significant (chi-square = 4.68, P= 0.0305), the observation group of bacteria detection rate was significantly lower than the control group;The bacteria detected in the two groups were mainly e. coli, tetanus bacillus and fungi. There was no significant difference in the indicators between the two groups before treatment, and the values of inflammatory cytokines il-1 and TNF- IOD in the two groups were significantly decreased after treatment, and the observation group was significantly lower than the control group, with statistically significant differences (P < 0.05). No serious adverse reactions occurred in either group during the treatment. Conclusion: the application of artificial dermals combined with jinfuning can promote wound healing of skin and soft tissue defect of finger abdomen, effectively inhibit bacterial infection of wound surface, reduce inflammation and infection,reducing bacterial detection rate.

Multi-face detection based on downsampling and modified subtractive clustering for color images

This paper presents a multi-face detection method for color images. The method is based on the assumption that faces are well separated from the background by skin color detection. These faces can be located by the proposed method which modifies the subtractive clustering. The modified clustering algorithm proposes a new definition of distance for multi-face detection, and its key parameters can be predetermined adaptively by statistical information of face objects in the image. Downsampling is employed to reduce the computation of clustering and speed up the process of the proposed method. The effectiveness of the proposed method is illustrated by three experiments.

Face Detection under Complex Background and Illumination

For face detection under complex background and illumination, a detection method that combines the skin color segmentation and cost-sensitive Adaboost algorithm is proposed in this paper. First, by using the characteristic of human skin color clustering in the color space, the skin color area in YC b C r color space is extracted and a large number of irrelevant backgrounds are excluded; then for remedying the deficiencies of Adaboost algorithm, the cost-sensitive function is introduced into the Adaboost algorithm; finally the skin color segmentation and cost-sensitive Adaboost algorithm are combined for the face detection. Experimental results show that the proposed detection method has a higher detection rate and detection speed, which can more adapt to the actual field environment.

Automatic Facial Spots and Acnes Detection System

Recently medical cosmetic has attracted significant business opportunity. Micro cosmetic surgery usually involves invasive cosmetic procedures such as non-ablative laser procedure for skin rejuvenation. However, to select an appropriate treatment for skin relies on accurate preoperative evaluations. In this paper, an automatic facial skin defects detection and recognition method is proposed. The system first locates the facial region from the input image. Then, the shapes of faces were recognized using a contour descriptor. The facial features are extracted to define regions of interest and an image segment method is used to extract potential defect. A support-vector-machine-based classifier is then used to classify the potential defects into spots, acnes and normal skin. Experimental results demonstrate effectiveness of the proposed method.

Acral melanoma detection using dermoscopic images and convolutional neural networks

Acral melanoma(AM)is a rare and lethal type of skin cancer.It can be diagnosed by expert dermatologists,using dermoscopic imaging.It is challenging for dermatologists to diagnose melanoma because of the very minor differences between melanoma and non-melanoma cancers.Most of the research on skin cancer diagnosis is related to the binary classification of lesions into melanoma and non-melanoma.However,to date,limited research has been conducted on the classification of melanoma subtypes.The current study investigated the effectiveness of dermoscopy and deep learning in classifying melanoma subtypes,such as,AM.In this study,we present a novel deep learning model,developed to classify skin cancer.We utilized a dermoscopic image dataset from the Yonsei University Health System South Korea for the classification of skin lesions.Various image processing and data augmentation techniques have been applied to develop a robust automated system for AM detection.Our custombuilt model is a seven-layered deep convolutional network that was trained from scratch.Additionally,transfer learning was utilized to compare the performance of our model,where AlexNet and ResNet-18 were modified,fine-tuned,and trained on the same dataset.We achieved improved results from our proposed model with an accuracy of more than 90%for AM and benign nevus,respectively.Additionally,using the transfer learning approach,we achieved an average accuracy of nearly 97%,which is comparable to that of state-of-the-art methods.From our analysis and results,we found that our model performed well and was able to effectively classify skin cancer.Our results show that the proposed system can be used by dermatologists in the clinical decision-making process for the early diagnosis of AM.

Eczema Disease Detection and Recognition in Cloud Computing

ED (eczema disease) is the most common form of skin inflammation in humans. Most of the skin disease is curable at initial stages with the advancement of technology. So, an early detection of skin disease can save the patient’s life and prevent theprogression of the disease. It is proposed to have a study on the diagnosis of ED using BpNN (backpropagation neural network) in cloud computing approach due to that BpNN is currently widespread research area in medicine and plays an important role in a decision support system. In this paper, an attempt has been made to make use of BpNN in the medical field along cloud computing to detect ED.

Face Detection Technology Based on Robot Vision

One being developed automatic sweep robot, need to estimate if anyone is on a certain range of road ahead then automatically adjust running speed, in order to ensure work efficiency and operation safety. This paper proposed a method using face detection to predict the data of image sensor. The experimental results show that, the proposed algorithm is practical and reliable, and good outcome have been achieved in the application of instruction robot.

Transfer Learning Empowered Skin Diseases Detection in Children

Human beings are often affected by a wide range of skin diseases,which can be attributed to genetic factors and environmental influences,such as exposure to sunshine with ultraviolet(UV)rays.If left untreated,these diseases can have severe consequences and spread,especially among children.Early detection is crucial to prevent their spread and improve a patient’s chances of recovery.Dermatology,the branch of medicine dealing with skin diseases,faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance,type of skin,and others.This study presents a method for detecting skin diseases using Deep Learning(DL),focusing on the most common diseases affecting children in Saudi Arabia due to the high UV value in most of the year,especially in the summer.The method utilizes various Convolutional Neural Network(CNN)architectures to classify skin conditions such as eczema,psoriasis,and ringworm.The proposed method demonstrates high accuracy rates of 99.99%and 97%using famous and effective transfer learning models MobileNet and DenseNet121,respectively.This illustrates the potential of DL in automating the detection of skin diseases and offers a promising approach for early diagnosis and treatment.

基于YOLOv7通道冗余改进的飞机蒙皮损伤检测

为提高蒙皮损伤检测的自动化程度,提出一种基于改进YOLOv7通道冗余的机器视觉检测方法。首先针对飞机蒙皮损伤数据集背景单一的特点,提出增强型颈部特征融合改进算法,提高了飞机蒙皮损伤的识别精度和检测速度;其次针对主干特征提取网络的卷积通道冗余的问题,引入部分卷积PConv(Partial convolution),提出主干特征提取网络轻量化,减少模型的参数量,同时提高损伤的识别效率。试验部分首先在飞机蒙皮损伤数据集上探索了不同增强型颈部特征融合改进算法,确定了最优的改进方案;接着在飞机蒙皮损伤数据集上做消融和对比试验,改进算法与原YOLOv7算法比较,mAP(Mean average precision)提升了2.3%,FPS(Frames per second)提升了22.1 f/s,模型参数量降低了34.13%;最后将改进的YOLOv7模型与主流目标检测模型对比,证明了改进算法的先进性。

基于改进YOLOv5的人体结核菌素试验后皮下硬块检测算法研究

针对传统皮下硬块检测方法精度低、计算速度慢等问题,提出了一种基于改进YOLOv5的皮下硬块目标检测算法。首先,将YOLOv5模型的主干网络CSPDarknet53进行改进,引入Faster模块来替换其中的C3模块。其次,利用模型剪枝在保证整个模型性能的同时减轻其计算复杂度。最后,引入Wise-IoU来进一步提升网络的回归性能。实验结果表明,基于改进YOLOv5的皮下硬块目标检测算法相比于原始的YOLOv5,准确率提升了1.2%,参数量减少了77.7%,整个算法更加轻量化,有效提高了算法对于皮下硬块的检测精度,减少了计算参数量,提升了算法的运行速度。

无创性皮肤生理功能测试仪的应用进展

无创性皮肤功能检测技术由于其方便、快捷、无创等优势,可用于多种常见皮肤病的辅助检查,对指导疾病诊治具有重要意义。其中皮肤生理功能测试仪是近年来新兴的无创性皮肤功能检测技术,可以通过检测经皮失水量(TEWL)、表皮pH值、角质层含水量(SCH)、皮脂含量等对皮肤屏障进行评估。本文阐述和探讨无创性皮肤生理功能测试仪在皮肤科的临床应用及实际意义,为其分析皮肤屏障功能与诊疗效果的相关性提供依据。

基于多尺度曲面的飞机蒙皮凹坑损伤检测算法

针对飞机蒙皮凹坑损伤噪声干扰强、检测时间久,机身表面不平整,且在二维图像中缺乏视觉信息、难以进行自动检测的问题,设计了一种基于多尺度曲面模型的飞机蒙皮凹坑损伤自动检测算法.首先,通过无人车、升降杆和深度相机搭建自动化采集平台,用来获得飞机蒙皮点云数据;然后,基于半径滤波、体素滤波、最小移动二乘法算法得到预处理数据;最后,在此基础上,基于多尺度区域划分、随机抽样一致算法和表面特征聚类进行损伤检测,得到最终的损伤结果.在损伤数据集上进行测试,实验结果表明:本文提出的算法在准确率、召回率、F值以及平均检测时间4个指标上均有明显提升,其均值分别为92.86%、86.67%、89.92%和6 s,损伤检测结果优于现有的几种点云损伤检测算法,本文提出的算法实现了在飞机蒙皮场景中自动检测凹坑损伤的目标.

基于自适应尺度混合海森滤波器的面部皱纹检测及评分

为解决全面部皱纹缺乏量化评价方法的问题,提出了一种基于自适应尺度混合海森滤波器(adaptive scale hybrid Hessian filter,ASHHF)的面部皱纹检测方法及评分机制。根据受试者年龄自适应调整海森滤波器的尺寸(σ)范围及步长参数并对其面部高分辨率图像进行滤波,基于81个面部特征点在滤波结果中去除五官、背景,仅保留面部皱纹,使用不同颜色将不同深度的皱纹检测结果标注在原始图像中,最后计算得分量化皮肤衰老程度。以专业医生标注皱纹为参考,50名受试者(年龄为22~65岁)的检测结果表明,相比于传统的固定尺度混合海森滤波器(fixed scale hybrid Hessian filter,FSHHF),ASHHF方法的检测准确率平均提升68.57%,运行时间平均缩短26.26%,评分机制结果与检测准确率的变化趋势一致。综上,本文检测方法能够准确、快速检测面部皱纹分布、深度及宽度,所提评分机制能够科学反映受试者皮肤衰老程度,有望为化妆品、医疗美容等抗衰方法提供功效量化评价手段。