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Sign Language Synthesis (SLS) is a domain-specific problem where multiple sign language words are stitched to generate a whole sentence in video, which serves to facilitate communications between the hearing-impaired people and healthy population. This paper presents a Variable Motion Frame Interpolation (VMFI) method for highly fluent SLS in scattered videos. Existing approaches for SLS mainly focus on mechanical virtual human technology, lacking high flexibility and natural effect. Also, the representative solutions to interpolate frames usually assume that the motion object moves at a constant speed which is not suitable for predicting the complex hand motion in frames of scattered sign language videos. To address the above issues, the proposed VMFI adopts acceleration to predict more accurate interpolated frames based on an end-to-end convolutional neural network. The framework of VMFI consists of variable optical flow estimation network and high-quality frame synthesis network that can approximate and fuse the intermediate optical flow to generate interpolated frames for synthesis. Experimental results on our realistic collected Chinese sign language dataset demonstrate that the proposed VMFI model achieves efficiency by performing better in PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity) and MA (Motion Activity) and gets higher score in MOS (Mean Opinion Score) than other two representative methods.

In this paper, a hybrid flexible current limiter is proposed to solve the problem of short circuit between positive pole and negative pole in DC distribution network. The current limiter consists of superconducting resistance and self-excited current limiter. In normal operating condition, the current limiter and self-excited current limiter are bypassed by the bridge circuit, so that the current limiter has no impact on DC distribution network. When a short-circuit fault happens between positive pole and negative pole, the bypass function of bridge circuit is failed. With the help of the current limiter, the fault current and the voltage drop of interelectrode capacitance can be quickly suppressed by superconducting resistance. For fully remove the negative effect of fault on interelectrode capacitance, through offering a controlled linearly rising current to an inductor in the set time, the self-excited current limiter can provide a positive clamping voltage, so as to keep the interelectrode capacitance’s voltage in its rated value. Finally, based on the theoretical derivation and simulation results, it can be proved that the major problems troubling a DC distribution network’s fault ride-through technologies can be resolved by the proposed method.

As most people suffer from the sub-health problem or chronic diseases, research on sensing and managing personal health is growing rapidly. In this paper, a user-friendly smart watch, which can sense personal physiological data and physical activity is presented. Based on the device, a smartphone software is developed, this hardware and software system can gather data and monitor personal health from the aspects of physiological conditions and physical conditions. Compared with other systems, it has the advantage of providing raw sensing data and unobtrusively continuous sensing, which can bring new opportunity to pervasive personal health monitoring, and promote wearable computing research in health monitoring and disease prevention.

Attention Deficit Hyperactivity Disorder (ADHD) is a mental disorder of the neurodevelopmental type. It is characterized by problems of paying attention, excessive activity, or difficulty controlling behavior which is not appropriate for a person’s age. Currently, as ADHD lacks of clear specificity, diagnosis of ADHD still mainly depends on doctors’ experiences and observation. Moreover, the monotonous environment of hospital may makes children feel nervous, which can lead to misdiagnosis. To cope with this problem, we design a contextualized and objective system to support auxiliary diagnosis of ADHD, which has eleven diagnostic scenarios designed according to the description of typical symptoms of ADHD in Diagnostic and Statistical Manual of Mental Disorders (DSM-V). During the testing, multi-source data are acquired, including physiological sensors’ data, motion sensors’ data and tasks related data. Now, Our system is still a primary prototype. In the next, we plan to deploy it in the hospital and collect more data, and research on the auxiliary diagnosis accuracy.

Services based on indoor navigation and localization play crucial roles in people's daily life, and they are even more important for individuals with visual impairments. In this paper, an indoor navigation and localization system CoFINLo with three key techniques: 1) dynamic feature extraction, 2) incremental model update, and 3) fine-grained trajectory generation, is proposed to implement robust indoor localization with friendly interaction design especially for visual impairment groups. Compared with other indoor localization systems, the proposed CoFINLo can overcome the obstacles caused by the diverse sensing devices, complex indoor environments, and fluctuated wireless signal. The proposed system is verified in an indoor office environment and implemented in a large-scale and comprehensive train station. The average 1.39m localization error suggests that CoFINLo can open a new opportunity to implement robust and user-friendly indoor navigation and localization in large and complex indoor environments, which can be a solid foundation for other location-based services.

Objective manual dexterity assessment is useful for early detection of age-related functional decline and for prediction of cognitive decline. Such assessments are carried out by clinicians using traditional tests and scales. The Box and Blocks Test (BBT) is one of such scale, focusing on the measurement of unilateral gross manual dexterity. Subjects are scored based on the number of blocks transferred from one compartment to the other compartment in a time window. Currently, most works on automatic BBT take advantage of the data captured by deep camera. However, these kind of solutions are limited by computational complexity and the accuracy of color segmentation. Based on recent success of wearable equipments, in this paper, we research multimodal wearable sensors based Box and Block Test system (wBBT). wBBT takes advantage of 5 kinds of time series signal, including Electromyographic, Accelerometer, Gyroscope, Orientation and Orientation Euler for automatic manual dexterity assessment. In addition, we research 9 deep neural network structures (3 multimodal signal fusion methods × 3 network cells) for multimodal signal based blocks moving motion recognition. To evaluate the feasibility of wBBT, we collect the data of 37 subjects, including 25 subjects with Mild Cognitive Impairment (MCI) and 12 normal subjects. Experimental results show that wBBT is a useful tool for automatic assessment of unilateral gross manual dexterity.

User's position and trajectory are very important personal informations, based on which, the user's personalized description can be realized, and the user's intentions, preferences and behavior patterns can also be found. For most of traditional approaches, the trajectory learning problems assume the availability of a vast amount of labeled training data, which requires a great deal of manual effort. In this paper we propose a indoor trajectory restoration method based on the spatial constraints on the position of points of interests (PoI). This method discovers PoI position by detecting the status changing of furniture and facilities and constructs spatial relationship of PoI positions with multidimensional scaling (MDS). Then, it synthetically optimizes the location model constructing of fitting error to labeled points, the manifold regularization of high-dimensional signal strength vectors and the MDS constraint of the spatial relationship of PoI positions, to achieve the optimizing trajectory by iteration. The experimental results show that our proposed method can effectively improve the robustness of the trajectory learning ability in different practical scenarios, benefiting from the constraint of PoI position.

With the rapid development of Uber and Didi, car-hailing services have had significant societal impacts. However, the increased popularity of car-hailing has also created drawbacks, as its security is significant problem. Researchers try to recognize human emotions effectively to protect the passenger's personal safety. Most of the current approaches are intrusive to passengers, e.g. using camera to capture passenger's facial expression or identifying conversation between passenger and driver. In this paper, we study the intonation and speed of speech signal to recognize three emotions, which are 'Neutrality', 'Happiness' and 'Anger'. Our proposed method contains two stages to make classification. In stage one, we distinguish between passenger and driver using unsupervised spectral clustering method. Experimental results show that the accuracy of our method is superior to representative off-the-shelf methods. In stage two, we propose a CNNs-based emotion recognition method. The results demonstrate that the model has the ability to effectively recognize different emotions, which the accuracy attains 78.14%

Human depth recovery is an essential task for tele-immersive video interaction systems with limited bandwidth consumption. This paper is motivated by the first human depth recovery method, i.e., the weighted large margin nearest center (WLMNC) distance based method, which compresses each human depth map into several skeletal block structures by learning a WLMNC distance at the remote stage and then recovers it by a rough-to-fine approach at the local stage. Since the WLMNC distance is equivalent to employing a linear transformation on the human pixels, for human postures with complex self-occlusion, the depth recovery performance of WLMNC is limited. To address the problem, this paper proposes to learn a nonlinear WLMNC distance via a lightweight neural network. Different from traditional classification or clustering problems, the neural network is introduced for storing skeletal block structure information instead of predicting new information. The quantitative experimental results on the VGA-sized benchmark dataset demonstrates that such a slight modification over WLMNC can achieve an inspiring performance improvement: the average depth recovery MAD error is reduced from 3.56cm to 2.77cm and the total running cost is reduced from about 150 seconds to 43 seconds on a laptop with just a little loss on the sampling rate, from 64x to 51x.

Characterized by inattention, hyperactivity and impulsivity, Attention Deficit Hyperactivity Disorder (ADHD) is a serious mental disease mainly affecting preschool children. In clinical diagnosis, the monotonous environment of hospital makes children feel scary easily, affecting their natural performance. Moreover, the assessment of ADHD primarily depends on the doctor’s experience and observation, lacking pleasant and objective diagnosis supports. To cope with this problem, we design a contextualized and objective system, named COSA, to support auxiliary diagnosis for ADHD, which is based on Diagnostic and Statistical Manual of Mental Disorders (DSM-V). COSA assesses ADHD symptoms by three Serious Game based contexts. At the same time, multi-dimensional data are acquired during testing, including physiological data, movement data and task related data. The effectiveness and acceptability of COSA are verified through questionnaires for doctors, parents and children.

Fatigue is not only common in clinical patients but is also prevalent in the healthy population. A lot of studies have been done to detect mental fatigue. Most of the current fatigue detection systems are based on individual questionnaire and computer vision. In this paper, we proposed a deep temporal model named Deep Convolutional Autoencoding Memory network to detect mental fatigue in real time based on biometrics (e.g., GSR, HR, R-R intervals and skin temperature) collected with smart band. The model can be regarded as a One-Class classifier consisted of two sub-networks: Characterization Network and Memory Network that can be co-optimized to extract features from raw data automatically and generate baseline model which can describe the sequential patterns of non-fatigue data. The major contributions of this paper are as follows: First, we solved the class imbalance problem between normal data and fatigue data through performing One-Class classification; Second, Characterization Network can extract features from the heterogeneous biometrics automatically; Third, Memory Network can generate a temporal model which accurately describes the normal pattern of sequential data; Last, our model uses a novel decision criterion to make the detection decision. Performance of the proposed method is validated on a realistic environmental dataset containing the biometrics of 6 participants collected in 6 weeks. Experimental results show that the accuracy of fatigue detection attains 82.9%, which is superior to the representative off-the-shelf methods.

Falling down is a great threat to the health of the elderly. Existing approaches for fall detection, such as threshold methods and offline classification methods, have been shown to be useful for providing emergency medical care for the elderly. However, those offline models lack generalization and adaptability for all the users in everyday life, which severely restricts their applications in real life situations. In this paper, we propose a cloud computing based fall detection framework which can update the model online with two-stage incremental update step. By applying cloud computing, the framework can take advantage of wearable sensors for more accurate and efficient fall detection. The proposed framework is comprised of a two-stage incremental update, which consists of local and cloud components. The local component updates the detection model with feedback from users, which can make the model more personalized for users in a timely manner. In the cloud component, the model can achieve self-improvement based on the data of daily livings collected from other users. Our simulation experiments show that our framework can achieve higher precision and recall with the incremental update based on data from all users.

This study focuses on improving the performance of human activity recognition when a small number of sensor data are available under some special practical scenarios and resource-limited environments, such as some high-risk projects, anomaly monitoring and actual tactical scenarios. The Human Activity Recognition (HAR) based on wearable sensors is an attractive research topic in machine learning and ubiquitous computing over the last few decades, and has extremely practicality in health surveillance, medical assistance, personalized services, etc. However, with the limitation of sensor sampling rate, sustainability, deployment, and other restricted conditions, it is difficult to collect enough and resultful sensor data anywhere, anytime. Therefore, the HAR based on wearable sensors always faces the challenges of the low-data regime under some practical scenarios, which leads to a low accuracy of activity recognition and needs to be solved urgently. Currently, the Generative Adversarial Networks (GANs) provide a powerful method for training resultful generative models that could generate very convincing verisimilar images. The framework of GANs and its variants shed many lights on improving the performance of HAR. In this paper, we propose a new generative adversarial networks framework called SensoryGANs that can effectively generate available sensor data used for HAR. To the best of our knowledge, SensoryGANs is the first unbroken generative adversarial networks applied in generating sensor data in the HAR research field. Firstly, we tried exploring and devising three activity-special GANs models for three human daily activities. Secondly, these specific models are trained with the guidance of unbroken vanilla GANs. Thirdly, the trained generators from adversarial optimization process are used to generate synthetic sensor data. Finally, the synthetic sensor data from SensoryGANs are used to enrich the original authentic sensor datasets, which can improve the performance of target activity recognition model. Meanwhile, we propose three visual evaluation methods for assessing synthetic sensor data produced by the trained generators in SensoryGANs models. Experimental results show that SensoryGANs models have the capability of capturing the implicit distribution of real sensor data of human activity, and then the synthetic sensor data generated by SensoryGANs models Have a potential for improving human activity recognition.

Sleep plays a vital role in good health and well-being throughout human life. A great deal of studies have been done to detect sleep stages. Most of the current sleep monitoring systems are invasive to users, e.g. requiring users to wear a device during sleep. In this paper, we use microphone to detect sleep stages including deep sleep, light sleep and rapid eye movement (REM), and propose a convolutional neural network using spectrogram as input. This paper's contribution mainly concentrates on the following two aspects: First, microphone is unobtrusive for sleep detection. Second, we build the mapping between acoustic signal and sleep stages with little manual intervention to extract features. Performance of the proposed method is validated on a realistic environmental dataset containing 52 nights of 5 participants. Experimental results show that the accuracy of sleep stages detection is superior to the representative off-the-shelf applications. Besides, we propose to utilize the attention maps to visualize acoustic data to better understand the relationship between sound and sleep stages. Experimental results show that the model has the ability to effectively reduce noises in classification by ignoring the high-frequency sounds and white noises.

Transfer learning has achieved promising results by leveraging knowledge from the source domain to annotate the target domain which has few or none labels. Existing methods often seek to minimize the distribution divergence between domains, such as the marginal distribution, the conditional distribution or both. However, these two distances are often treated equally in existing algorithms, which will result in poor performance in real applications. Moreover, existing methods usually assume that the dataset is balanced, which also limits their performances on imbalanced tasks that are quite common in real problems. To tackle the distribution adaptation problem, in this paper, we propose a novel transfer learning approach, named as Balanced Distribution \underline{A}daptation~(BDA), which can adaptively leverage the importance of the marginal and conditional distribution discrepancies, and several existing methods can be treated as special cases of BDA. Based on BDA, we also propose a novel Weighted Balanced Distribution Adaptation~(W-BDA) algorithm to tackle the class imbalance issue in transfer learning. W-BDA not only considers the distribution adaptation between domains but also adaptively changes the weight of each class. To evaluate the proposed methods, we conduct extensive experiments on several transfer learning tasks, which demonstrate the effectiveness of our proposed algorithms over several state-of-the-art methods., ICDM 2017

In clinical settings, the assessment of Parkinson's disease (PD) mainly depends on the doctor's experience and observation. Such assessment often lacks of unified standards and may result in varied diagnoses among different doctors. To cope with this problem, we propose an objective and quantified symptom assessment tool of PD on mobile devices, based on the Unified Parkinson's Disease Rating Scale (UPDRS). The mobile PD assessment tool, PdAssit, assesses PD symptoms by actively delivering six tasks and generates scores equivalent to UPDRS using machines learning models. PdAssit is applied in three essential applications, including medication response detection, symptom self-tracking and diagnostic assistance. The feasibility and effectiveness of PdAssit is demonstrated through clinical experiments.

International audience; In this paper, we propose a pedestrian attribute recognition approach and a CNN-based person re-identification framework enhanced by pedestrian attributes. The knowledge of person attributes can help video surveillance tasks like person re-identification as well as person search, semantic video indexing and retrieval to overcome viewpoint changes with their robustness to the inherent visual appearance variations. Compared to previous approaches, our attribute recognition method using Local Maximal Occurrence (LOMO) features and a Multi-Label Multi-Layer Perceptron (MLMLP) classifier proves to be more robust to different view points and is computationally more efficient. The experiments on three public benchmarks show that the proposed method improves the state-of-the art on attribute recognition. Furthermore, we integrate our attribute recognition algorithm into a triplet CNN similarity learning framework for person re-identification fusing both learned CNN features and attributes. This fusion leads to an overall improvement, and we achieve state-of-the-art results on person re-identification.

Wireless LANs, especially WiFi, have been pervasively deployed and have fostered myriad wireless communication services and ubiquitous computing applications. Indoor localization is an essential modules of these applications. A primary concern in designing scenario-tailored application is to obtain precise estimated distance combatting with harsh indoor wireless signal propagation issues, particularly multipath effect. The conventional propagation model based on received signal strength indicator(RSSI) is easily affected by temporal and spatial fluctuation due to the multipath effect, which leads to most of the distance estimation errors in current indoor localization systems. Intuitively, these positions in weak multipath effect(WME) conditions, which are slightly affected by multipath effect, perform better under free space propagation model. Therefore, the ability to distinguish weak multipath effect, which is slightly affected by multipath effect is a key enabler for accurate distance estimation. Enabling such capabilities on commercial WiFi infrastructure, however, is difficult due to the coarse multipath resolution with the MAC layer RSSI. In this paper, we propose a universal precise distance estimation scheme based on weak multipath effect identification, leveraging the channel state information(CSI) from the PHY layer. In our distance estimation system, we first select positions which are identified as weak multipath effect conditions. Then we build a free space propagation model with RSSI to estimate distance between the transmitter and the receiver, choosing these selected positions. Experimental results demonstrate that choosing positions in weak multipath effect conditions can effectively improve the accuracy of distance estimation in a variety of typical indoor environments.

Falls threaten the health and life of the elders heavily because they lead to injuries or even death. Therefore, a reliable monitoring and alarm mechanism is desperately in need to guarantee the quality of elders' life. In this paper, we propose a multistage machine learning method to perform fall detection and solve the false alarm and missing alarm problem in traditional fall detection methods. Our proposed method consists of three stages: 1) threshold filtering, 2) ELM classifier, and 3) orientation-based filtering. Our method utilizes a high-precision triaxial accelerometer to collect the relevant information. After filtered by our three-stage method, the signal can be determined whether it is a fall or not. Experimental results demonstrate that: different from the traditional state-of-art methods with a single machine learning classifier, our method can greatly reduce the missing alarm and false alarm rate on the premise of high accuracy for all detection.

Cerebral small vessel disease (SVD) is common in the elderly and is associated with loss of functional independence, institutionalization, and death. In this paper, we propose a coarse-to-fine feature selection method for accurate SVD detection and timely implementation of interventions. The proposed method first uses an Iterative Random Forest based Feature Selection (IRFFS) method to obtain the most representative features from a feature set that includes gait, balance, and agility performance features extracted from 17 predefined clinical actions. The method then uses the Feature Incremental Extreme Learning Machine (FIELM) model to further verify the discriminant ability of each kind of selected features. Our results demonstrate that the proposed method can effectively select the most significant features for SVD detection, which include gait and agility performance features. Our method achieves up to 91.44% classification accuracy, outperforming other state-of-the-art feature selection methods. Our findings also verify clinical observations indicating that the fine motor pattern features of upper and lower limbs are helpful for high-accuracy SVD detection.

Miscellaneous mini-wearable devices (e.g. wristbands, smartwatches, armbands) have emerged in our life, capable of recognizing activities of daily living, monitoring health information, so on. Conventional activity recognition (AR) models deployed inside these devices are generic classifiers learned offline from abundant data. Transferring generic model to user-oriented model requires time-consuming human effort for annotations. To solve this problem, we propose SS-ARTMAP-AR, a self-supervised incremental learning AR model updated from surrounding information such as Bluetooth, Wi-Fi, GPS, GSM data without user's annotation effort. Experimental results show that SS-ARTMAP-AR can gradually adapt individual users, become more incremental intelligence.

Accurate indoor localization is very important for various kinds of Location Based Services (LBS). For most of traditional approaches, the location estimation problems assume the availability of a vast amount of labeled calibrated data, which requires a great deal of manual effort. Previous researches cannot deal with this problem in both calibration reduction, location accuracy. In this paper, we propose a heterogeneous data driven manifold regularization model known as HeterMan to calibration-effort reduction for tracking a mobile node in a wireless sensor network. With the constraint of user heading orientation, we build a mapping function between signal space, physical space with extremely less labeled data, a large amount of unlabeled data. Experimental results show that we can achieve high accuracy with extremely less calibration effort comparing with previous methods. Furthermore, our method can reduce computation complexity, time consumption by parallel processing while maintaining high accuracy.

As a non-verbal communication mean, head gestures play an important role in face-to-face conversation and recognizing them is therefore of high value for social behavior analysis or Human Robotic Interactions (HRI) modelling. Among the various gestures, head nod is the most common one and can convey agreement or emphasis. In this paper, we propose a novel nod detection approach based on a full 3D face centered rotation model. Compared to previous approaches, we make two contributions. Firstly, the head rotation dynamic is computed within the head coordinate instead of the camera coordinate, leading to pose invariant gesture dynamics. Secondly, besides the rotation parame- ters, a feature related to the head rotation axis is proposed so that nod-like false positives due to body movements could be eliminated. The experiments on two-party and four-party conversations demonstrate the validity of the approach.

By utilizing captured characteristics of surrounding contexts through widely used Bluetooth sensor, user-centric social contexts can be effectively sensed and discovered by dynamic Bluetooth information. At present, state-of-the-art approaches for building classifiers can basically recognize limited classes trained in the learning phase; however, due to the complex diversity of social contextual behavior, the built classifier seldom deals with newly appeared contexts, which results in degrading the recognition performance greatly. To address this problem, we propose, an OSELM (online sequential extreme learning machine) based class incremental learning method for continuous and unobtrusive sensing new classes of social contexts from dynamic Bluetooth data alone. We integrate fuzzy clustering technique and OSELM to discover and recognize social contextual behaviors by real-world Bluetooth sensor data. Experimental results show that our method can automatically cope with incremental classes of social contexts that appear unpredictably in the real-world. Further, our proposed method have the effective recognition capability for both original known classes and newly appeared unknown classes, respectively.

Early diagnosis can significantly reduce the incidence of stroke which is a serious cerebrovascular disease to the elderly. Motivated by the Trail Making Test (TMT) in neuropsychological, a traditional test which uses pen and paper to signal brain damage, we present an intelligent system called strDoctor. Instead of seeing a real doctor, it can guide users to do TMT through body sensing game and give them diagnosis feedback automatically. Depending on a robust palm-pose-adaptive single fingertip tracking method, this paper extracts effective features to construct a stroke indicating model for the body sensing game based TMT. Finally the validity of strDoctor was verified by our experiments.

The past years has seen the emergence and fast growing of various wearable devices, such as smart watches, wrist bands, rings, glasses, jewelry, garments, etc. Although wearable devices have a wide usage scope, healthcare is one of the most promising field which is being exploited by a number of big enterprises and research institutes. However, existing healthcare related wearable products usually work individually. There is almost no data transmission or information exchange between different kinds of devices, which prevents data fusion of multiple devices and development of upper layer healthcare applications. In this demo, we present WearableHUB, a mobile phone based open pervasive wearable data fusion platform for personal health management. WearableHUB can transform multimodal data (motion data, physiological indices, etc.) into unified format according to predefined standard. Other than traditional wearable computing system, WearableHUB not only can get preloaded function results from different devices based on module library according to data type and device position, but also can trigger and get dynamic online downloaded APP function base on fusing multimodal data and extracting higher level information.

Video quality assessment plays an essential role in multimedia systems and services. In the case of scalable video coding, which enables dynamic adaptation based on terminal capabilities and heterogeneous network, variable resolution is one of the most prominent types of video distortions. In this paper, we propose a new spatial distortion metric for evaluating video streaming quality with variable spatial resolution. The key idea is to project video sequence into feature domain and calculate the distortion of content information from the projected principal component matrix and its eigenvectors. This metric can measures the degree of content information degradation especially in spatial resolution scalable video. The performance of the proposed metric is evaluated and compared to some state-of-the-art quality evaluation metrics in the literature. Our results show that the proposed metric achieves good correlations with the subjective evaluations of the EPFL scale video database.

This paper presents a cooperative video transmission mechanism based on game theory for heterogeneous devices during broadcasting. Broadcasting is a multipoint delivery of transmission that sends data from a source to multiple destinations. The terminal is involved in cooperative transmission when the station broadcast video data. To enhance performance, the heterogeneity and forwarding capabilities should be considered. This work studies power control and allocation in a collaborative transmission based on game theory, which provides an effective strategy when network resources are limited. First, a novel power allocation model of the base station (BS) based on non-cooperative game theory and bidding is presented in this study. Additionally, we also propose the utility function of Signal-to-Noise Ratios (SNRs) along with Signal-to-Interference Ratio (SIRs). Subsequently, based on such non-cooperative game theory with a utility function, the model of the power distribution of terminal in cooperative transmission can be built. Experiments have proven the correctness of the designed model and superiority, verifying the effectiveness of the proposed power-control idea.

Real-time video transmission over Wireless Local Area Networks (WLANs) is a new challenge for wireless network research. This work solves the problem of video streaming transmission encoded with H.264/SVC (Scalable Video Coding). Firstly, we analyze the common theoretical foundations of video transmission in WLANs. Second, we present a review of the game model and use it to develop a mathematical method for understanding the relationship among workstations. Game theory is applied to analyze individual and group activities of workstations. The solution to Carrier Sense Multiple Access Scheme (CSMA)/Collision Avoid (CA) based on game theory presents a strategy for workstations to choose the back-off windows and reaches the equilibrium points. Third, the new module "System-in-the-Loop (SITL)" for OPNET Modeler is examined in this study to facilitate the video streaming transmission between real and simulated devices. The simulation results establish analytical statistics and provide insights for the process of the real response of transmission.