Zhang et al 2024b - Revision history

Zhangzhiyu1315 at 06:51, 23 June 2025

2025-06-23T06:51:04Z

Show changes

Zhangzhiyu1315 at 06:44, 23 June 2025

2025-06-23T06:44:23Z

Show changes

Zhangzhiyu1315 at 06:42, 23 June 2025

2025-06-23T06:42:22Z

Zhangzhiyu1315 at 05:54, 10 December 2024

2024-12-10T05:54:06Z

Zhangzhiyu1315 at 05:44, 10 December 2024

2024-12-10T05:44:58Z

Show changes

Zhangzhiyu1315 at 05:26, 10 December 2024

2024-12-10T05:26:52Z

Show changes

Zhangzhiyu1315 at 09:07, 4 June 2024

2024-06-04T09:07:00Z

Zhangzhiyu1315 at 09:02, 4 June 2024

2024-06-04T09:02:37Z

Rimni at 08:51, 3 June 2024

2024-06-03T08:51:45Z

Rimni at 12:25, 31 May 2024

2024-05-31T12:25:42Z

@@ Line 18: / Line 18: @@
 -->
 ==Abstract==
 In exploring the performance-enhancing effects of compression sportswear, this study investigates its impact on trunk muscle activation during core stability training, with a particular focus on the biomechanical implications of this relationship. This research posits the existence of an Equilibrium Point of Motion (EPM) and employs surface Electromyography (EMG) to measure muscular responses in the Erector Spinae (ES), External Oblique (EO), and Rectus Abdominis (RA) during exercises performed with and without compression wear. Results revealed that compression garments significantly augment root mean square (RMS) values for ES and EO muscles, suggesting differential muscle activation patterns. Further, the application of OpenSim modeling of full-body musculoskeletal mechanics facilitated a novel analysis of muscle forces and activations, enhancing the understanding of sportswear functionality in an ergonomic context. This approach uncovered significant correlations between actual and simulated muscle activations, particularly for the ES and EO during a gluteal bridge exercise. These insights contribute to the development of ergonomically-engineered sportswear aimed at optimizing athletic performance and provide a valuable reference for future sportswear design and research.

@@ Line 40: / Line 40: @@
 Branyan and Menguc synthesized a personalized anatomical curve muscle geometry model to simulate the mechanical behavior of muscles such as latissimus dorsi, extensor spinae, rectus abdominis, and obliquus abdominis [23]. The geometric model of curved muscles was also mapped by incorporating muscle geometry, moment arms, and fiber direction paths. Guo and Ren refined and optimized the microfilm element embedded in a flexible multibody dynamics model to enhance the understanding of the role of the core musculature in regulating the pressure distribution and stability of the spine [24]. The model included core muscle groups such as rectus abdominis, obliques, adductors, and multifidus. In summary, these studies, through the use of quantitative measurements and model simulation techniques, provide a simulation basis for a comprehensive understanding of the behavior of human muscles during exercise states and the interactions between muscles and bones, which in turn helps to facilitate the optimization of sports equipment design. Key research results and major findings of previous researchers provide valuable references in the field of muscle biomechanics modeling and simulation analysis. This study seeks to reveal how varying levels of tightness and the anatomical fit of the shorts could modulate this muscle activity, thereby contributing to a more targeted approach in the design of compression sportswear. The ultimate aim is to provide empirical data that could lead to enhanced ergonomic design of sportswear, precisely engineered to improve core training outcomes and reduce injury risk associated with lower back strain. This paper advocated that the relationship between the compression of sportswear and subject ergonomic aids and mechanisms should be further enhanced by achieving an equilibrium of muscle activation and pressure level.
 ==2. Methodology==
 ===2.1 Subjects===
-===2.1 Subjects ===
+The experiment was approved by the institution’s human research ethics committee (EST-2024-001). Written informed consent was obtained from fifteen healthy male participants (<math>22.4\pm 2.7</math> years old, <math>68.5\pm 1.6</math> kg, <math>178\pm 2.2</math> cm). The study involved subjects' surface electromyography (EMG) during various exercises to measure muscle activity. EMG data were used to infer characteristics of muscular strength. All participants were healthy, and they had agreed to and understood the content of the experiment.
+===2.2. Standardized compression===
-The experiment was approved by the institution’s human research ethics committee (EST-2024-001). Written informed consent was obtained from fifteen healthy male participants (22.4±2.7 years old, 68.5±1.6 kg, 178±2.2 cm). The study involved subjects' surface electromyography (EMG) during various exercises to measure muscle activity. EMG data were used to infer characteristics of muscular strength. All participants were healthy, and they had agreed to and understood the content of the experiment.
-−
-=== 2.2. Standardized Compression ===
 The study meticulously detailed the protocol implemented to ensure that each subject experienced a similar level of compression from the tight-fit shorts. Different sizes of shorts were provided to the subjects according to their somatotypes to ensure consistent clothing pressure. The clothing pressure experienced by the subjects was measured to confirm uniformity and compared against the pressure of cotton shorts, which served as a no-pressure control.
 ===2.3. Instrumentation===
-=== 2.3. Instrumentation ===
+====2.3.1. Subjective clothing pressure comfort evaluation====
-−
-==== 2.3.1. Subjective Clothing Pressure Comfort Evaluation ====
 Subjects were instructed to wear standardized tight-fit shorts for consistency. Following a tactile test, subjects completed a questionnaire regarding the comfort of the fabric. This subjective evaluation data was compiled and analyzed to compare comfort performance across various fabric types. Feedback on perceived compression was recorded, providing qualitative data to complement the quantitative analysis. Data analysis included assessing averaged pressure values and inter-subject variability to confirm a standardized perception of tightness among all participants.
 ====2.3.2. Electromyography====
-==== 2.3.2. Electromyography ====
+Surface electromyography (EMG) was used to capture muscle activity during different core training exercises. EMG data were collected using the Delsys Trigno Avanti sensor hardware and EMGworks software (version EW4, Inc, USA) at a high sampling rate of 1092 Hz. Subjects participated under two wear conditions: tight-fitting sports shorts and cotton shorts without pressure. This aimed to explore the impact of sportswear on muscle activation. [[Draft Zhou 675060936#tab-1|Table 1]] provides an overview of the placement of EMG sensors on various muscles.<div class="center" style="font-size: 85%;">'''Table 1'''.  Placement of EMG sensors on muscles in the human body</div><div id="tab-1"></div>
 Surface electromyography (EMG) was used to capture muscle activity during different core training exercises. EMG data were collected using the Delsys Trigno Avanti sensor hardware and EMGworks software (version EW4, Inc, USA) at a high sampling rate of 1092 Hz. Subjects participated under two wear conditions: tight-fitting sports shorts and cotton shorts without pressure. This aimed to explore the impact of sportswear on muscle activation. Table 1 provides an overview of the placement of EMG sensors on various muscles.<div class="center" style="font-size: 85%;">
 '''Table 1'''.  Placement of EMG sensors on muscles in the human body
 </div>
 {| class="wikitable" style="margin: 1em auto 0.1em auto;border-collapse: collapse;font-size:85%;width:auto;"
 |- style="text-align:center"
@@ Line 88: / Line 79: @@
 | style="vertical-align: center;" |6
 | style="vertical-align: center;" |Left
-| style="vertical-align: center;" |Erector Spinae  |}
+| style="vertical-align: center;" |Erector Spinae
 ====2.3.3 Testing sport shorts====
 The tight-fit shorts were selected and standardized to ensure each subject experienced uniform compression according to their somatotypes. The shorts' compression attributes were indicated by their elastic modulus in both warp and weft directions. Comparative analyses were conducted against control garments (cotton shorts with no compression features). This approach ensured a comparative analysis to discern potential differences in muscle activation patterns based on the type of sportswear worn during core exercises. [[Draft Zhou 675060936#tab-2|Table 2]] illustrates the fabric content of pants provided to participants.<div class="center" style="font-size: 85%;">'''Table 2'''. Detail of various garments provided to participants</div><div id="tab-1"></div>
@@ Line 412: / Line 404: @@
 [43] Delp S.L., Anderson F.C., Arnold A.S., Loan P., Habib A., John C.T., Guendelman E., Thelen D.G. OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Transactions on Biomedical Engineering, 54:1940-1950, 2007.

@@ Line 132: / Line 132: @@
 Assuming there is a sequence offacial images of a group oftourists encompassing diverse emotional expressions as they stand in front of museum exhibits. The objective is to employ a model for recognizing and classifying these emotions while maintaining a balance between speed and accuracy. The mini_xception architecture has been chosen for the network structure. Following each convolutional layer, batch normalization layers have been added to expedite training and enhance model generalization. Additionally, dropout regularization has been employed within the model to deactivate some nodes, mitigating the risk of overfitting randomly. Given a sequence of facial images of different tourists, during the forward propagation phase, a portion of neuron activations is randomly halted to a certain proportion. This ensures the model does not excessively rely on specific localized features. Considering the varying illumination conditions faced by real tourists in the museum, pre-processing is essential for input facial images. Operations such as cropping, scaling, and grayscale conversion are conducted to optimize the image input for network processing. Subsequently, the mini_xception algorithm is applied to extract features from the facial images. This algorithm accelerates recognition speed while reducing the number of parameters and computations. The mini_xception algorithm transforms tourists' facial images into feature-rich representations encompassing spatial and semantic information. Following this, the ResNet algorithm is employed to fuse features extracted by mini_xception. ResNet, being aresidual network, leverages residual connections to reduce training time and enhance accuracy. This algorithm combines the spatial and semantic features derived from mini_xception to elevate emotion classification's accuracy and generalization capabilities. Ultimately, a Softmax classifier is utilized to categorize the fused features, thereby predicting the emotional category of the facial image. This classifier maps the feature vector into a probability space, yielding the predicted probabilities for each category. Suppose the model has been trained. In that case, a facial image of a tourist can be predicted as belonging to the "liking" emotional category. From capturing facial images to feature extraction and emotional classification, this algorithm aids in the automated recognition of tourists' emotional expressions before historical and cultural exhibits in museums. Consequently, it offers a more scientific and effective basis for museum exhibit planning and cultural heritage preservation.
-In this model, <math display="inline">W</math> and <math display="inline">I</math> represent one network layer's weight tensor and input tensor, respectively. Among them, <math display="inline">c</math> represents the channel and  <math>w<w_{in}\mbox{ },h<h_{in}</math> represents the width and height, respectively. The network approximates <math display="inline">W</math> through a binary tensor  <math>B\text{ e}\{+1,-1\}^{c\text{х}w\text{х}h} </math> <math>cxwxh</math> and a scaling factor  <math>C\mbox{ }eR+</math>, as shown in Eq. (4):
+In this model, <math display="inline">W</math> and <math display="inline">I</math> represent one network layer's weight tensor and input tensor, respectively. Among them, <math display="inline">c</math> represents the channel, <math>w<w_{in}\mbox{ },h<h_{in}</math> represents the width and height, respectively. The network approximates <math display="inline">W</math> through a binary tensor  <math>B\text{ e}\{+1,-1\}^{c\text{х}w\text{х}h} </math> <math>cxwxh</math> and a scaling factor  <math>C\mbox{ }eR+</math>, as shown in Eq. (4):
 {| class="formulaSCP" style="width: 100%; text-align: center;"

@@ Line 115: / Line 115: @@
 where,  <math>\overset{1:1}{\Leftrightarrow }</math> refers to the real two-way mapping between the physical space entity model and the information space twin model. <math>\vartriangleleft </math> refers to the natural connections between different models. Therefore, <math display="inline">PE, DE, PP, DP, PW</math>, and <math display="inline">PW</math> should maintain asynchronous two-way real.
-===3.3 mapping. Design and research of facial emotion recognition strategy based on CNN===
+===3.3 Design and research of facial emotion recognition strategy based on CNN===
 Emotion refers to the subjective emotional experience and internal psychological state that humans have in response to external stimuli. It encompasses various complex experiences and reactions such as happiness, sadness, surprise, anger, fear, and more. Facial expressions are the outward manifestation of emotions, reflecting the emotional states of individuals through muscle movements and neural transmission. Facial expressions can be automatically recognized and classified using computer vision and image processing techniques, helping people gain a more accurate understanding of visitors' emotional states and responses in front of historical and cultural exhibits in museums. Facial recognition technology can identify facial expressions automatically by analyzing the changes and combinations of facial features, such as the eyes, mouth, eyebrows, and more. For example, when visitors appreciate an exhibit in a museum, they may display different emotions and attitudes, such as liking, disliking, or surprise. By utilizing facial recognition technology, people can monitor visitors'emotional changes and responses in real-time and connect this information with the knowledge and stories related to the displayed historical and cultural artifacts. This enhances visitors' experience and cultural awareness. In conclusion, facial expressions are the outward manifestation of emotions, and facial recognition technology can infer visitors'emotional states and responses by analyzing these expressions. By employing these technologies, people can better understand visitors' emotional experiences and responses in front of historical and cultural exhibits in museums, providing a more scientific and effective reference for exhibition planning and cultural heritage preservation in museums.

← Older revision		Revision as of 08:51, 3 June 2024
Line 293:		Line 293:


−	In Eq. (16), <math display="inline">b, h</math> are the results of the weight vector <math display="inline"> w </math> and the activation vector <math>C</math> after the Sign function. <math>C,\beta </math> is the corresponding scaling factor. The definition of the convolution operation ~~ofthe~~ network forward process is shown in Eq. (17):	+	In Eq. (16), <math display="inline">b, h</math> are the results of the weight vector <math display="inline"> w </math> and the activation vector <math>C</math> after the Sign function. <math>C,\beta </math> is the corresponding scaling factor. The definition of the convolution operation of the network forward process is shown in Eq. (17):

	{\| class="formulaSCP" style="width: 100%; text-align: center;"		{\| class="formulaSCP" style="width: 100%; text-align: center;"

@@ Line 487: / Line 487: @@
 [16] Koebner I.J., Fishman S.M., Paterniti D., et al. The art of analgesia: A pilot study of art museum tours to decrease pain and social disconnection among individuals with chronic pain. Pain Med., 20(4):681-691, 2019.
-[17] Xianying D. Analysis on the educational function of campus culture in higher vocational colleges. The Theory and Practice of Innovation and Enntrepreneurship, 4(23), 77-79, 2021.
+[17] Xianying D. Analysis on the educational function of campus culture in higher vocational colleges. The Theory and Practice of Innovation and Enntrepreneurship, 4(23):77-79, 2021.
 [18] Chikaeva K.S., Gorbunova N.V., Vishnevskij V.A., et al. Corporate culture of educational organization as a factor of influencing the social health ofthe Russian student youth. Práxis Educacional, 15(36):583-598, 2019.