Avoiding objects independently in indoor environments for individuals with severe visual impairment is one of the significant challenges in daily life. This paper presents a wearable application to help visually impaired people quickly build situational awareness and traverse safely. The system utilizes Red, Green, Blue, and Depth (RGB-D) camera and an Inertial Measurement Unit (IMU) to detect objects and the collision-free path in real-time. A region proposal module is presented to decide where to identify the ground from 3D point clouds. The segmented ground area can act as the traversable path, and its corresponding region in the image is removed to prevent detecting painted objects. The system can provide information about the category, distance, and direction of the detected objects by fusing the depth image and the neural network results. A 3D acoustic feedback mechanism is designed to improve the situational awareness for visually impaired people, and guild them traverse safely. The advantage of this system is that our 3D region proposal module can robustly propose the potential ground region and greatly reduce the computation cost of the ground segmentation. Besides, a typical machine-learning-based approach may miss objects because they could not be recognized, though they still may pose a danger. Another advantage of our approach is that the imminent danger detector can detect such unrecognizable objects to help users avoid a collision. Finally, experimental results demonstrate that the proposed system can be a useful indoor assistant tool to help blind individuals with collision avoidance and wayfinding.INDEX TERMS Convolutional neural network, ground segmentation, object detection, point cloud, region of interest, visual impairment, wearable assistive system.
The fatigue energy consumption of independent gestures can be obtained by calculating the power spectrum of surface electromyography (sEMG) signals. The existing research studies focus on the fatigue of independent gestures, while the research studies on integrated gestures are few. However, the actual gesture operation mode is usually integrated by multiple independent gestures, so the fatigue degree of integrated gestures can be predicted by training neural network of independent gestures. Three natural gestures including browsing information, playing games, and typing are divided into nine independent gestures in this paper, and the predicted model is established and trained by calculating the energy consumption of independent gestures. The artificial neural networks (ANNs) including backpropagation (BP) neural network, recurrent neural network (RNN), and long short-term memory (LSTM) are used to predict the fatigue of gesture. The support vector machine (SVM) is used to assist verification. Mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) are utilized to evaluate the optimal prediction model. Furthermore, the different datasets of the processed sEMG signal and its decomposed wavelet coefficients are trained, respectively, and the changes of error functions of them are compared. The experimental results show that LSTM model is more suitable for gesture fatigue prediction. The processed sEMG signals are appropriate for using as the training set the fatigue degree of one-handed gesture. It is better to use wavelet decomposition coefficients as datasets to predict the high-dimensional sEMG signals of two-handed gestures. The experimental results can be applied to predict the fatigue degree of complex human-machine interactive gestures, help to avoid unreasonable gestures, and improve the user’s interactive experience.
While the COIVD-19 has triggered the rapid and large-scale promotion of online courses, it has also exposed the problem of poor user experience on online course platforms. Therefore, this research hopes to propose a complete user experience evaluation system to clarify the deficiencies of the platform and give directions for improvement. This research combines qualitative and quantitative, from the perspective of user experience, integrates data obtained through literature research, interview and network research to determine evaluation indices. The Delphi method and analytic hierarchy process are used to determine the weights of various indices and set the scoring level. It is obtained that the "ease of operation" has the most important impact on the user experience of the online live course platform. Innovatively adopting the t-test method to establish a comparative analysis method for the model application, a standardized process of model application is designed, and a complete evaluation system is constructed. Two pieces of software are selected as experimental samples to verify the model from both statistics and practical applications. The test results show that although the two experimental sample software have significant differences in the scores of multiple second-level indices, the overall scores are still similar, which is consistent with the direct evaluation results. Therefore, the user experience evaluation system proposed in this research is reliable, which can provide a reference for the construction of online live course platform and the optimization of user experience. INDEX TERMS User experience, evaluation index, analytic hierarchy process, online live course platform.
Companies generally set a standard color for product color design to enhance the corporate brand image. When a given color and the planned combination of given areas contain multiple constraints, the exhaustive process that has traditionally been used to obtain the optimal color scheme is often time consuming. Therefore, this study proposes the use of binary programming theory to
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