Gait analysis plays an important role in the fields of healthcare and sports sciences. Conventional gait analysis relies on costly equipment such as optical motion capture cameras and wearable sensors, some of which require trained assessors for data collection and processing. With the recent developments in computer vision and deep neural networks, using monocular RGB cameras for 3D human pose estimation has shown tremendous promise as a cost-effective and efficient solution for clinical gait analysis. In this paper, a markerless human pose technique is developed using motion captured by a consumer monocular camera (800 × 600 pixels and 30 FPS) for clinical gait analysis. The experimental results have shown that the proposed post-processing algorithm significantly improved the original human pose detection model (BlazePose)’s prediction performance compared to the gold-standard gait signals by 10.7% using the MoVi dataset. In addition, the predicted T² score has an excellent correlation with ground truth (r = 0.99 and y = 0.94x + 0.01 regression line), which supports that our approach can be a potential alternative to the conventional marker-based solution to assist the clinical gait assessment.

Energy is at the center of human society and drives the technologies and overall human well-being. Today, artificial intelligence (AI) technologies are widely used for system modeling, prediction, control, and optimization in the energy sector. The internet of things (IoT) is the core of the third wave of the information industry revolution and AI. In the energy sector, tens of billions of IoT appliances are linked to the Internet, and these appliances generate massive amounts of data every day. Extracting useful information from the massive amount of data will be a very meaningful thing. Complex event processing (CEP) is a stream-based technique that can extract beneficial information from real-time data through pre-establishing pattern rules. The formulation of pattern rules requires strong domain expertise. Therefore, at present, the pattern rules of CEP still need to be manually formulated by domain experts. However, in the face of complex, massive amounts of IoT data, manually setting rules will be a very difficult task. To address the issue, this paper proposes a CEP rule auto-extraction framework by combining deep learning methods with data mining algorithms. The framework can automatically extract pattern rules from unlabeled air pollution data. The deep learning model we presented is a two-layer LSTM (long short-term memory) with an attention mechanism. The framework has two phases: in the first phase, the anomalous data is filtered out and labeled from the IoT data through the deep learning model we proposed, and then the pattern rules are mined from the labeled data through the decision tree data mining algorithm in the second phase. We compare other deep learning models to evaluate the feasibility of the framework. In addition, in the rule extraction stage, we use a decision tree data mining algorithm, which can achieve high accuracy. Experiments have shown that the framework we proposed can effectively extract meaningful and accurate CEP rules. The research work in this paper will help support the advancement of the sector of air pollution prediction, assist in the establishment of air pollution regulatory strategies, and further contribute to the development of a green energy structure.

Since anomaly targets in hyperspectral images (HSIs) with high spatial resolution appear as connected areas instead of single pixels or subpixels, both spatial and spectral information of HSIs can be exploited for a hyperspectal anomaly detection (AD) task. This article proposes a hyperspectral AD method based on Wasserstein distance (WD) and spatial filtering (called AD-WDSF). Based on the assumption that both background and anomaly targets obey the multivariate Gaussian distribution, background and anomaly target distributions are estimated in the local regions of HSIs. Subsequently, the anomaly intensity of test pixels centered in the local regions are determined via measuring the WD between background and anomaly target distributions. Lastly, spatial filters, i.e., guided filter (GF), total variation curvature filter (TVCF), and Maxtree filter, are exploited to further refine detection results. Experimental results conducted on two real hyperspectral data sets demonstrate that the proposed method achieves competitive detection performance compared with the state-of-the-art AD methods.

Space resources are of economic and strategic value. Infrared (IR) remote sensing, unaffected by geography and weather, is widely used in weather forecasting and defense. However, detecting small IR targets is challenging due to their small size and low signal-to-noise ratio, and the resulting low detection rates (DRs) and high false alarm rates (FRs). Existing algorithms struggle with complex backgrounds and clutter interference. This paper proposes an adaptive differential event detection method for space-based aerial target observation, tailored to the characteristics of target motion. The proposed IR differential event detection mechanism uses trigger rate feedback to dynamically adjust thresholds for strong, dynamic radiation backgrounds. To accurately extract targets from event frames, a lightweight target detection network is designed, incorporating an Event Conversion and Temporal Enhancement (ECTE) block, a Spatial-Frequency Domain Fusion (SFDF) block, and a Joint Spatial-Channel Attention (JSCA) block. Extensive experiments on simulated and real datasets demonstrate that the method outperforms state-of-the-art algorithms. To advance research on IR event frames, this paper introduces SITP-QLEF, the first remote-sensing IR event dataset designed for dim and moving target detection. The algorithm achieves an mAP@0.5 of 96.3%, an FR of 4.3 $\times {10}^{-5}$, and a DR of 97.5% on the SITP-QLEF dataset, proving the feasibility of event detection for small targets in strong background scenarios.

Space-based infrared (IR) detection, with wide coverage, all-time operation, and stealth, is crucial for aerial target surveillance. Under low signal-to-noise ratio (SNR) conditions, however, its small target size, limited features, and strong clutters often lead to missed detections and false alarms, reducing stability and real-time performance. To overcome these issues of energy-integration imaging in perceiving dim targets, this paper proposes a biomimetic vision-inspired Infrared Temporal Differential Detection (ITDD) method. The ITDD method generates sparse event streams by triggering pixel-level radiation variations and establishes an irradiance-based sensitivity model with optimized threshold voltage, spectral bands, and optical aperture parameters. IR sequences are converted into differential event streams with inherent noise, upon which a lightweight multi-modal fusion detection network is developed. Simulation experiments demonstrate that ITDD reduces data volume by three orders of magnitude and improves the SNR by 4.21 times. On the SITP-QLEF dataset, the network achieves a detection rate of 99.31%, and a false alarm rate of $1.97\times {10}^{-5}$, confirming its effectiveness and application potential under complex backgrounds. As the current findings are based on simulated data, future work will focus on building an ITDD demonstration system to validate the approach with real-world IR measurements.