Inertial measurement unit (IMU) technology has gained popularity in human activity recognition (HAR) due to its ability to identify human activity by measuring acceleration, angular velocity, and magnetic flux in key body areas like the wrist and knee. It has propelled the extensive application of HAR across various domains. In the healthcare sector, HAR finds utility in monitoring and assessing movements during rehabilitation processes, while in the sports science field, it contributes to enhancing training outcomes and preventing exercise-related injuries. However, traditional sensor fusion algorithms often require intricate mathematical and statistical processing, resulting in higher algorithmic complexity. Additionally, in dynamic environments, sensor states may undergo changes, posing challenges for real-time adjustments within conventional fusion algorithms to cater to the requirements of prolonged observations. To address these limitations, we propose a novel hybrid human pose recognition method based on IMU sensors. The proposed method initially calculates Euler angles and subsequently refines them using magnetometer and gyroscope data to obtain the accurate attitude angle. Furthermore, the application of FFT (Fast Fourier Transform) feature extraction facilitates the transition of the signal from its time-based representation to its frequency-based representation, enhancing the practical significance of the data. To optimize feature fusion and information exchange, a group attention module is introduced, leveraging the capabilities of a Multi-Layer Perceptron which is called the Feature Fusion Enrichment Multi-Layer Perceptron (GAM-MLP) to effectively combine features and generate precise classification results. Experimental results demonstrated the superior performance of the proposed method, achieving an impressive accuracy rate of 96.13% across 19 different human pose recognition tasks. The proposed hybrid human pose recognition method is capable of meeting the demands of real-world motion monitoring and health assessment.

Human Activity Recognition (HAR) has been proven to be effective in various healthcare and telemonitoring applications. Current HAR methods, especially deep learning, are extensively employed owing to their exceptional recognition capabilities. However, in pursuit of enhancing feature expression abilities, deep learning often introduces a trade-off by increasing Time complexity. Moreover, the intricate nature of human activity data poses a challenge as it can lead to a notable decrease in recognition accuracy when affected by additional noise. These aspects will significantly impair recognition performance. To advance this field further, we present a HAR method based on an edge-computing-assisted and GRU deep-learning network. We initially proposed a model for edge computing to optimize the energy consumption and processing time of wearable devices. This model transmits HAR data to edge-computable nodes, deploys analytical models on edge servers for remote training, and returns results to wearable devices for processing. Then, we introduced an initial convolution method to preprocess large amounts of training data more effectively. To this end, an attention mechanism was integrated into the network structure to enhance the analysis of confusing data and improve the accuracy of action classification. Our results demonstrated that the proposed approach achieved an average accuracy of 85.4% on the 200 difficult-to-identify HAR data, which outperforms the Recurrent Neural Network (RNN) method’s accuracy of 77.1%. The experimental results showcase the efficacy of the proposed method and offer valuable insights for the future application of HAR.

In this paper, a novel Ti-doped hierarchically mesoporous silica microspheres/tungsten oxide (THMS/WO₃) hybrid film was prepared by simultaneous electrodeposition of Ti-doped hierarchically mesoporous silica microspheres (THMSs) and WO₃ nanocrystallines onto the fluoride doped tin dioxide (FTO) coated glass substrate. It is demonstrated that the incorporation of THMSs resulted in the hybrid film with improved electrochromic property. Besides, the content of THMSs plays an important role on the electrochromic property of the hybrid film. An excellent electrochromic THMS/WO₃ hybrid film with good optical modulation (52.00% at 700 nm), high coloration efficiency (88.84 cm² C⁻¹ at 700 nm), and superior cycling stability can be prepared by keeping the weight ratio of Na₂WO₄·2H₂O (precursor of WO₃):THMSs at 15:1. The outstanding electrochromic performances of the THMS/WO₃ hybrid film were mainly attributed to the porous structure, which facilitates the charge-transfer, promotes the electrolyte infiltration and alleviates the expansion of the film during Li⁺ insertion. This kind of porous THMS/WO₃ hybrid film is promising for a wide range of applications in smart homes, green buildings, airplanes, and automobiles.

This paper presents a new interactive three-dimensional(3D) segmentation using region growing algorithms. The principle of this method is to build region growing sequence by increasing the maximal homogeneity threshold recursively until it satisfied with the needs. Firstly we calculate the average of the markers' coordination to get the seed point. Then calculate the difference between the neighbours and the average intensity of the seed points as well as using the modification of the Euclidean distance. Finally by conducting extensive performance evaluation to prove the proposed method more accurate and robust to the noise in CT images. We have successfully applied this approach to 3D segmentations of human femur obtained from CT scans. Satisfactory results have been achieved showing the effectiveness and superiority of the proposed method.