Characteristics Of Human Body Research Articles

Performance Analysis of Semi-refined Digital Forearm Modeling and Simplified Forearm Model in Electromagnetic Simulation

Objective: The objective of this study is to analyze the performance difference between simplified and digital models based on medical images. Methods: According to the characteristics of human anatomy, the finite element simulation software COMSOL Multiphysics 5.5 was employed to construct a simplified arm model using cylinders and a digital arm model based on Chinese digital human regarding electroacupuncture therapy as an example. A comparative analysis was then performed considering three aspects: mesh number, potential distribution, and resource consumption. Results: Through analysis, the digital arm model based on Chinese digital human requires significantly more mesh cells than the simplified arm model in mesh generation. Meanwhile, because the digital arm model based on the Chinese digital human fully expresses the nonuniformity of the tissue distribution in a real human body, its signal distribution in its interior is also relatively scattered, and the coupling potential slightly differs at the electrode vertex with the smallest change. In addition, the digital arm model has much higher resource consumption and computer hardware resource requirements compared with the simplified arm model. Conclusion: As a result, the digital model based on the Chinese digital human can more fully express the tissue distribution and electrical signal characteristics of a real human body. However, due to its high computational requirements, appropriate simplification can be selected to improve the computational efficiency of the model in practical applications.

Motor delay image recognition based on deep learning and human skeleton model

Children with developmental delays often exhibit gross motor delay during their early childhood. Identifying such delay typically requires standardized assessments, but these assessments are susceptible to subjective interpretation. To enhance objectivity and reliability in these assessments, this study proposes a novel method utilizing an AI model for identifying gross motor delay in children. The proposed method divides the assessment of gross motor skills into two components: static balance and dynamic balance. First, the Yolo target detection model and the Mediapipe human posture detection model are employed for preprocessing to determine skeletal joints and body positioning information. Subsequently, dimensionality reduction techniques are applied to extract key features. A sliding window method is then used to extract action features identifying static balance features of the human body (e.g., standing on one foot, standing on the left foot) and dynamic balance features (e.g., lifting the heels while walking, jumping on a mat). The machine learning classifier and LSTM-CNN are then used to categorize the static and dynamic balance feature data. Experimental results demonstrate that the proposed method achieved accuracy scores of 80 % and 87 % for static and dynamic balancing skills, respectively.

Human body features recognition based adaptive user interface for extra-large touch screens

With the widespread use of extra-large touch screens (eLTS) in various settings such as work and education, interaction efficiency and user experience have garnered increased attention. The current user interface (UI) layouts of eLTS are primarily categorized into two modes: fixed position and manual adjustment. The fixed UI layout fails to accommodate users of different heights and sizes, while the manual adjustment mode involves cumbersome steps and lacks sufficient flexibility. This study proposes an adaptive UI for eLTS. The optimal operational area on the eLTS is determined based on users’ height, eye level, arm length, face orientation, and distance from the screen. The eLTS menu is then positioned and displayed within this optimal area. Simulations involving users of various heights (P1 female, P50 male and female, and P99 male) were conducted to evaluate fatigue using the rapid upper limb assessment (RULA) method. The results indicate that the proposed adaptive UI significantly reduces user fatigue.

Design of Human Posture Recognition System Based on Internet of Things Technology and Image Segmentation Algorithm

Abstract To address the problem of the traditional human posture recognition system being easily affected by environmental factors such as noise, a human posture recognition system based on Internet of Things technology and an image segmentation algorithm was designed. To do this, we selected the attitude sensor, designed the attitude sensor structure based on micro electro mechanical system technology, and had a variety of selectable data output modes. A 36 V power supply was selected as the circuit design index to provide sufficient power for the system. The main control chip was designed, and its state information description and external output were installed in a single chip or the same component. The denoising process based on image segmentation algorithm was designed, the error correction model was constructed, the measured values were normalized, and the image segmentation model was constructed. We extracted the node features of human body posture, used the image segmentation algorithm to construct the structure diagram of human body posture nodes, located the local recognition area of the node structure map, and designed the human body posture recognition system. The experimental results show that the designed human gesture recognition system has better effect and better recognition performance.

IOT Based Remote Patient Monitoring System

Abstract: A remote monitoring technique that can accurately track human physiological indicators has applications in virtual reality, sports science, medicine, rehabilitation, and surveillance. The majority of systems now in use for tracking bodily parameters in humans need wiring that limits natural movement. In order to get over this restriction, a wearable wireless sensor network that monitors physiological human body characteristics has been built using an accelerometer, a pulse oximeter, a heart-rate sensor, a temperature sensor, and a galvanic skin response sensor. The individual is being tracked wirelessly using his own location. It would be simple to modify this system to watch athletes and young children. Unlike a tethered monitoring equipment, the wireless feature allows the human body to move freely, making the system genuinely portable, quick, and dependable. The portable and the designed sensor node's small size makes it simple to attach to the body.

A multi-featured expression recognition model incorporating attention mechanism and object detection structure for psychological problem diagnosis

Expression is the main method for judging the emotional state and psychological condition of the human body, and the prediction of changes in facial expressions can effectively determine the mental health of a person, thus avoiding serious psychological or psychiatric disorders due to early negligence. From a computer vision perspective, most researchers have focused on studying facial expression analysis, and in some cases, body posture is also considered. However their performance is more limited under unconstrained natural conditions, which requires more information to be used in human emotion analysis. In this paper, we design an Adaptive Multi-End Fusion Attention Mechanism suitable for extracting human body information based on the deep learning framework, depending on human expressions, postures and the environment they are in and add it to an object detection model to obtain the information we need from different regions of the human body and face and features of different sizes and use fusion networks for feature fusion and classification, and from different test methods to confirm that this fusion approach for expression recognition and prediction is feasible. This model achieves an average accuracy of 34.51 % in the Emotic contextual expression recognition dataset.

An intelligent recommendation system for personalised parametric garment patterns by integrating designer’s knowledge and 3D body measurements

Garment pattern-making is one of the most important parts of the apparel industry. However, traditional pattern-making is an experience-based work, very time-consuming and ignores the body shape difference. This paper proposes a parametric design method for garment pattern based on body dimensions acquired from a body scanner and body features (body feature points and three segmented body part shape classification) identified by designers according to their professional knowledge. By using this method, we construct a men’s shirt pattern recommendation system oriented to personalised fit. The system consists of two databases and three models. The two databases include a relational database (Database I) and a personalised basic pattern (PBP) database (Database II). The Database I is based on manual and three-dimensional (3D) measurements of human bodies by using designer’s knowledge. And Database I is a relational database, which is organised in terms of the relational model of the body part shape and its key body feature dimensions. After a deep analysis of measured data, the irrelevant measured dimensions to human body shape have been excluded by designers and extract representative human body feature dimensions. In addition, the relations between body shapes and previously identified body feature dimensions have been modelled. From the above relational model, we label key feature point positions on the corresponding 3D body model obtained from 3D body scanning and correct the whole 3D human upper body model into the semantically interpretable one. The 3D personalised basic pattern is drawn on the corrected model based on these key feature points. By using three-dimensional to two-dimensional (3D-to-2D) flattening technology, a 2D flatten graph of the 3D personalised basic pattern of the interpretable model is obtained and slightly adjusted to the form suitable for industrial production, i.e., PBP and the PBP database (Database II) is built. In addition, the three models include a basic pattern parametric model (Model I) (characterizing the relations between the basic pattern and its key influencing human dimensions (chest girth and back length)), a regression model (Model II) which enables to infer from basic pattern to PBP for three body parts based on the one-to-one correspondence of key points between the PBPs and the basic patterns and a personalised shirt pattern parametric model (Model III) (characterizing the structural relations between the personalised shirt pattern (PBPshirt) and PBP). The initial input items of the recommendation system are the body dimension constraint parameters, including chest girth, back length and the body feature dimensions used to determine each body part shape as well as three shirt style constraint parameters (slim, regular and loose). By using Model I, the corresponding basic pattern can be generated through the user’s chest girth and back length. Body feature dimensions determine the three body parts’ shapes. Then, Model II is used to generate the PBP for the corresponding body parts shape. Based on the shirt style chosen by the user, Mode III is used to generate the PBPshirt from the PBP. The output of the recommendation system is a fit-oriented PBPshirt. Moreover, if the PBPshirt is unsatisfactory after a virtual try-on, four adjustable parameters (front side-seam dart, back side-seam dart, waist dart and garment bodice length) are designed to adjust the PBPshirt generated by the proposed recommendation system.

Deep Learning-Based Recognition and Visualization of Human Motion Behavior

Human behavior recognition refers to the classification task of identifying the specific actions of human characters based on the characteristics of human body and the completed actions through a specific algorithm. It has a wide range of applications in intelligent surveillance, video retrieval and so on. The main challenge in this direction is to accurately extract the semantic information of each behavior to describe its dynamic changes in space and time. Therefore, this article introduces the latest research progress in the field of human behavior recognition. Through deep learning techniques, particularly convolutional neural networks and recurrent neural networks, human movements in video data can be effectively identified. However, deep learning models lack interpretability, which can be a challenge in practical applications. The researchers also introduce the application of traditional methods and deep learning-based methods to human behavior recognition, and explore the advantages of deep learning models in processing multi-time scale information and introducing attention mechanisms. Finally, the paper summarizes the potential of deep learning technology combined with multimodal data in behavioral analysis, and provides prospects for applications in smart fitness, health care and other fields.

Dance movement error correction based on decision tree algorithm

Abstract In an environment where action recognition technology is widely used, dance action recognition technology has also received extensive attention. Due to the complexity of dance movements, there will be a large number of errors in dance movement recognition. Therefore, this paper uses the decision tree algorithm to optimize dance movement recognition. In this paper, the general framework of dance movement recognition is first constructed, and the human body features are extracted and calculated by using the Laban motion analysis method for the changes in the joints of dance movements. Then, use the decision tree algorithm to classify and identify the dance movements, and use it to optimize the self-obscuring motion information repair algorithm to solve the problem of dance movement occlusion and error, and complete the construction of the dance movement recognition model based on CNN network. Empirical analysis shows that the model method's Top-1 and Top-5 accuracy are both 84.7% and 89.32%, respectively. The average probability of incorrect recognition, error recognition, and correct recognition of the optimized dance action recognition in this paper is 8.84%, 3.78 and 87.38 respectively. The average recognition correctness is improved by 12.02%, which indicates that the effect of recognition of the optimized model of dance action masking and error correction in this paper is excellent. It has achieved a better construction in the field of dance action error correction.

Application of 3D Printing Technology in Customized Garment Production

Abstract In this paper, human body images are acquired by DSLR camera shooting, and the photos are grayscaled based on the HIS model. Then, the different operations weaken the similar parts between the images, and the other parts of the photos are highlighted. The image is separated from the background by image binarization. After the contour detection of the image, the pixel coordinates of the contour points in the image are deposited into a dynamic array by the size judgment of the pixel values. The subsequent size calculation is called to make a pad. According to the human body, clothing size automation measurement has been derived from the human body characteristics of the size, the use of three-dimensional printing technology to produce clothing to meet consumer demand, and three-dimensional printing technology to support the production of customized clothing under the experimental analysis. It is verified that the data difference between the actual size of the subject’s ready-made clothes and the size of the clothes produced based on 3D printing technology is kept within ±0.48cm. The virtual fitting pressure is less than the comfort threshold of women’s upper body pressure (2.75kpa), which indicates that the clothes produced by this research will not cause physiological discomfort to the human body and, at the same time, meet the consumer’s personalized clothing customization needs.

Research on the Accuracy of Clothing Simulation Development: The Influence of Human Body Part Characteristics on Virtual Indicators

Currently, many virtual simulation design studies of compression pants do not consider pressure distribution and human body characteristics. Therefore, this study aimed to optimize the simulation design accuracy of compression pants by investigating female body characteristics to improve the pressure distribution and enhance comfort. Firstly, we divided the body part features into flexible and rigid parts, performed compression relationship analysis between the material and the body, and collected qualitative and quantitative data related to the potential influencing factors. Subsequently, by conducting correlation analysis of the data, a pressure prediction model was established to address the pressure value errors in the simulation data. The research results showed that there was a significant difference between the real and virtual pressures in the flexible parts of the female body, and that the real pressure was closely related to the elasticity and thickness properties of the material. By optimizing virtual pressure values, the consistency between the virtual pressure and real test results can be significantly improved. The accurate prediction and optimization of pressure values can lead to the reduction of material waste and energy consumption during the manufacturing process.

Slim or Plus-Size Burrito? A natural experiment of consumers’ restaurant choice

Anthropomorphic food labels, such as “Thin cookie” or “Fat burger”, are often used to describe the size and shape of food and have been shown to affect consumers’ food choices. More crucially, this effect may differ among vulnerable populations such as individuals with overweight and obesity due to self-image similitudes. We explore how anthropomorphic food labels influence the food choices of restaurant patrons with different weight status. We conduct a natural field experiment in a restaurant where the portion sizes in the menu are labeled using anthropomorphic human body features. Small/Large portions are presented as Slim/Regular in one treatment to reflect a desirable weight status. In a second treatment, Small/Large portions are presented as Regular/Plus Size to represent a less ideal weight status. Overall, we find that the Regular/Plus Size label increases the selection of small portions by 14.3 percentage points when objective portion size information is provided. In particular, individuals with overweight/obesity are 23.4 percentage points more likely to choose a small portion item under the Regular/Plus Size label compared to the Slim/Regular label. These results have marketing and policy implications for food labeling and their effect on food consumption among vulnerable populations.

Emotional Perception: Divergence of Early and Late Event-related Potential Modulation.

The early posterior negativity (EPN) is a mid-latency ERP component that is reliably enhanced by emotional cues, with a deflection beginning between 150 and 200 msec after stimulus onset. The brief, bilateral occipital EPN is followed by the centroparietal late positive potential (LPP), a long duration slow-wave that is strongly associated with emotional arousal ratings of scenes. A recent study suggests that the EPN is particularly sensitive to human bodies in scenes, independent of emotional intensity. Here, we directly investigate the influence of human body features on EPN modulation, using emotional and neutral scenes depicting people across a range of body exposures and orientations, in addition to scenes of pleasant, neutral, and unpleasant animals. The results demonstrate that the EPN is quite sensitive to human body features and weakly related to arousal ratings, whereas the LPP is strongly modulated by scenes that receive high arousal ratings. Based on these results and relevant work on body-specific visual perception, we speculate that modulation of the EPN may strongly reflect the early detection of human bodies, which serves as a predictor of emotional significance, whereas LPP modulation is more closely associated with the extended elaborative processing of scenes that are explicitly judged to be emotionally arousing.

Polarization characteristics of human body scattering in THz waves

As the 6th generation mobile communication system is expected to use the terahertz wave band, it is essential to clarify the human body blocking and scattering characteristics as many small cells for indoor and outdoor use will be needed. In addition, since it is important to use high-gain antennas with orthogonal polarization to realize efficient MIMO transmission, it is necessary to clarify the polarization characteristics of human body blocking and scattering. Experiments conducted to investigate human body scattering in the terahertz wave band show that the scattering characteristics are polarization dependent.

Deep learning-based approaches for human motion decoding in smart walkers for rehabilitation

Gait disabilities are among the most frequent impairments worldwide. Their treatment increasingly relies on rehabilitation therapies, in which smart walkers are being introduced to empower the user’s recovery state and autonomy, while reducing the clinicians effort. For that, these should be able to decode human motion and needs, as early as possible. Current walkers decode motion intention using information gathered from wearable or embedded sensors, namely inertial units, force sensors, hall sensors, and lasers, whose main limitations imply an expensive solution or hinder the perception of human movement. Smart walkers commonly lack an advanced and seamless human–robot interaction, which intuitively and promptly understands human motions. A contactless approach is proposed in this work, addressing human motion decoding as an early action recognition/detection problematic, using RGB-D cameras. We studied different deep learning-based algorithms, organised in three different approaches, to process lower body RGB-D video sequences, recorded from an embedded camera of a smart walker, and classify them into 4 classes (stop, walk, turn right/left). A custom dataset involving 15 healthy participants walking with the walker device was acquired and prepared, resulting in 28800 balanced RGB-D frames, to train and evaluate the deep learning networks. The best results were attained by a convolutional neural network with a channel-wise attention mechanism, reaching accuracy values of 99.61% and above 93%, for offline early detection/recognition and trial simulations, respectively. Following the hypothesis that human lower body features encode prominent information, fostering a more robust prediction towards real-time applications, the algorithm focus was also quantitatively evaluated using Dice metric, leading to values slightly higher than 30%. Promising results were attained for early action detection as a human motion decoding strategy, with enhancements in the focus of the proposed architectures.

Dual-Band Self-Complementary 5G Antenna for Wireless Body Area Network

A design of a self-complementary dual-band fifth-generation (5G) antenna for a wireless body area network (WBAN) has been presented in this paper. Like many other advantages, dual-band antennas have the benefit of being able to establish a reliable wireless connection in places that are frequently out of reach. The antenna operates at two popular 5G NR, FR-2 frequency bands: n257, or 28 GHz, and n260, or 39 GHz. FR-2 bands are prominent for high-speed data transactions within a limited range, which is perfect for WBAN applications. The proposed self-complementary single element design has a physical size of 6 mm × 8 mm × 1.59 mm and is designed with a Rogers RT6002 substrate with a dielectric constant of 2.94. In free space simulations, the antenna performed satisfactorily, achieving around 90% efficiency and having return losses of 16.32 dB and 20.47 dB at the lower and upper frequency bands sequentially. The antenna generated almost omnidirectional patterns at both frequencies with a peak gain of 4.74 dB. By placing the antenna next to a 3D human torso phantom that had been digitally created with accurate human body features, the design’s onbody performance was assessed. Onbody simulations produced favorable performance with a small dispersion from their peak values in some parameters, i.e., efficiency and reflection coefficients but produced more gains: 4.34 dBi at 28 GHz and 6.19 dBi at 39 GHz. For further distance-based investigation, the antenna was placed in five different positions relative to the torso. The test yields 67% efficiency with the minimum gap and 71% at the highest distance from the human body for the lowest band. Though the lower-frequency band produces better results with more gaps, the higher-frequency band performs consistently better in even the closest placement to the human body model by reaching more than 6 dB of gain with above 81% efficiency and wider than 10 GHz of bandwidth. The overall performance indicates this design can be a good solution to complex WBAN scenarios.

Neural Network-Based Method for Early Diagnosis of Autism Spectral Disorder Head-Banging Behavior from Recorded Videos

Autism spectrum disorder (ASD) is a mental developmental disorder associated with social and communicational defects and Stereotypical Motor Movements (SMM). SMM is a set of repetitive motor activities associated with several mental developmental disorders like Autism. SMM has several forms like arm flapping, head banging, ear covering, and spinning with various degrees of severity that might lead to self-injury in severe cases. Developing a computer-vision-based technology to detect noticeable SMM behaviors can help in the early diagnosis of autism. In this paper, a computer vision-based neural network model was proposed to detect and recognize repetitive motor behaviors. The proposed model went through three main stages: First, data preparation. Second, human body features extraction using deep learning pose estimation and the skeleton representation model, and finally, multiclass classification to distinguish between several classes of headbanging. The proposed solution was evaluated using the Self Stimulatory Behavior Dataset (SSBD) which is a public dataset of three classes of repetitive motor behaviors associated with autism. We also collected a set of 40 videos of autistic children exhibiting headbanging from public domains like YouTube. In addition to that, we captured 25 videos of typically developing subjects mimicking headbanging. The collected and the videoed videos were used to evaluate the proposed model. This work proves the applicability of diagnosing mental developmental syndrome symptoms using vision-based techniques in cooperation with neural networks. The produced results prove that the used techniques can operate well in real-world challenging applications. The proposed model achieved 85.5% accuracy on SSBD and 93% on the collected and recorded videos.

3D Dynamic Image Modeling Based on Machine Learning in Film and Television Animation

With the deep integration and rapid development of computer technology and film and television animation in recent years, computer animation technology has gradually created objective practical value. 3D animation technology also plays a key role in film and television special effects and advertising special effects. As a new type of multimedia data, human motion capture data can be used for 3D human model modeling and human motion simulation because of its high fidelity. A human motion pattern recognition method based on long short-term memory network (LSTM) is proposed. The model uses a deep learning neural network composed of a 2-layer LSTM network to automatically extract features from the collected human body feature information. Then, the multi-class motion patterns are modeled in time series to quickly identify different motion patterns in real time. To evaluate the performance of this model, the effectiveness of this method in identifying six different motion modes is validated using an open dataset. At the same time, this method is compared with four methods based on in-depth learning model. Experimental results verify the effectiveness of the method. It provides a feasible method for human motion recognition and modeling based on capture data in video animation.

Progres of deep learning based quantitative MRI reconstruction methods

磁共振成像是一种应用广泛的无创医学成像方法，因其丰富的软组织对比度可以成像人体几乎所有内部结构，包括器官、骨骼、肌肉和血管，已成为临床医学影像诊断的利器。然而磁共振成像存在两大公认的瓶颈：成像速度慢、扫描操作烦琐。深度学习给磁共振成像带来莫大的契机，给传统磁共振加速成像带来新的可能。鉴于该领域的快速发展性质，本文旨在总结文献中报道的大量深度学习和磁共振图像重建相结合的方法，以更好地了解该领域。本文简单介绍磁共振成像在多通道线圈接收的并行加速和压缩感知加速下的深度学习重建方法，其中单对比度图像可通过多通道接收线圈提供的冗余度用于加速，多对比度图像可额外使用不同对比度图像这一维度用于加速，动态图像与多对比度图像类似可额外使用时间维度用于加速，本文也将介绍深度学习在这些方面的发展。随着磁共振成像近年来由定性多对比度成像向定量多参数成像的发展，其中定量成像的图像中可内含多对比度图像，如何借用深度学习提供的能力将定性多对比度图像映射到参数图也是一个难点，近年来这一方向也获得了较快的发展，文中也将针对这方面内容进行调研并介绍。针对上述内容，分别介绍国际研究现状和国内研究现状，拟更好地总结国内外研究的发展的异同和趋势。最后对深度学习助力定量磁共振成像方面进行了展望。;Magnetic resonance imaging(MRI) is a commonly-used non-invasive medical imaging method. Due to richable soft tissue-related contrast features of human body, medical imaging diagnosis is beneficial from more internal structures-contextual image in clinic, including its organs, bones, muscles, and blood vessels. However, two sorts of bottlenecks of MRI like slow scanning speed and labor-intensive scanning operation are required to be resolved beyond the constraints of hardware and existing techniques. Current MRI has been facilitating in terms of the emerging deep learning based medical imaging technique. To optimize its acquisition time, parallel imaging and compressed sensing combined with the use of multi-array coils, conventional MRI mainly depends on the hardware improvements and new compactable sequence design. First, literature review of deep learning-based MR reconstruction methods are analyzed, including such deep learning issues originated from 1) integration of parallel imaging and compressed sensing of MRI and 2) acceleration of multi-contrast images and dynamic images of single-contrast image. Deep learning-based reconstruction models is oriented to get their generalizability incorporated with multiple datasets, in which MRI data are challenged for multiple factors like its relevance of centers, models, and field strengths. Two decadal development of quantitative MRI technique can provide pixel-level characterization of intrinsic tissue quantitative parameters, such as T1, T2, and ADC. Generally, quantitative MRI is often required to get multiple weighted images under different parameters, and quantitative tissue parameters can be generated via signal model-required pixel-level nonlinear fitting. Compared to the acquisition of contrast images, acquisition and reconstruction duration has become more longer. To accelerate the acquisition and reconstruction or facilitate accurate mapping, extension ability of deep learning has been developing for the reconstruction of MRI fast imaging. Apart from conventional single parameter mapping method, simultaneous multi-parameter mapping techniques have leaked out, such as MR fingerprinting. Compared to single parameter mapping, multi-parameter mapping can be synchronized and accelerated, and more efficient acquisition and inherent coregistration can be used to deal with such complicated reconstruction. Deep learning technique can be as a multifaceted tool to simplify the reconstruction and speed up the acquisition. However, it is still challenged for such constraints like deep learning algorithms and large amounts of training data. Deep learning methods are required to be melted into magnetic resonance physical models and traditional reconstruction algorithms to a certain extent, and to improve the interpretability of the model and alleviate the impact on large datasets, such methods of data enhancement, weakly supervised learning, unsupervised learning, and transfer learning can be involved in as well. A quantitative MRI-relevant unsupervised network can be potentially used for training in terms of different parameter mapping sequences, which can greatly shrink the amount of data samples further.

Compressive Strain Sensing Elastic Foam Based on Sericin Dispersed Carbon Nanotubes

The flexible strain sensor can transform the micro or macro deformation into the visual electrical signals for monitoring the physiological and motor characteristics of human body. However, the current commercial strain sensors generally have some shortcomings such as large stiffness and small strain range. In this work, a flexible compressive strain sensor with the 3D conductive network was fabricated by using biomacromolecule sericin protein dispersed carbon nanotubes (SSCNTs) as the conductive material and melamine foam (MF) as the elastic matrix. The results showed that the sensitivity of MF/SSCNTs elastic foam was 1.36 in the strain range of 0-70% with the CNTs content of 10 wt% and the cyclic compressive sensing performance was stable for at least 1600 cycles. This kind of flexible compressive strain sensor is environmentally friendly, low cost, simple process, and provides a new method for the preparation of green flexible sensor.