Pose tracking and captioning are extensively employed for motion capturing and activity description in daylight vision scenarios. Activity detection through camera systems presents a complex challenge, necessitating the refinement of numerous algorithms to ensure accurate functionality. Even though there are notable characteristics, IP cameras lack integrated models for effective human activity detection. With this motivation, this paper presents a gait-driven OpenCV and MediaPipe machine learning framework for human pose and movement captioning. This is implemented by incorporating the Generative 3D Human Shape (GHUM 3D) model which can classify human bones, while Python can classify the human movements as either usual or unusual. This model is fed into a website equipped with camera input, activity detection, and gait posture analysis for pose tracking and movement captioning. The proposed approach comprises four modules, two for pose tracking and the remaining two for generating natural language descriptions of movements. The implementation is carried out on two publicly available datasets, CASIA-A and CASIA-B. The proposed methodology emphasizes the diagnostic ability of video analysis by dividing video data available in the datasets into 15-frame segments for detailed examination, where each segment represents a time frame with detailed scrutiny of human movement. Features such as spatial-temporal descriptors, motion characteristics, or key point coordinates are derived from each frame to detect key pose landmarks, focusing on the left shoulder, elbow, and wrist. By calculating the angle between these landmarks, the proposed method classifies the activities as “Walking” (angle between −45 and 45 degrees), “Clapping” (angles below −120 or above 120 degrees), and “Running” (angles below −150 or above 150 degrees). Angles outside these ranges are categorized as “Abnormal”, indicating abnormal activities. The experimental results show that the proposed method is robust for individual activity recognition.

Alzheimer’s disease (AD), an intense neurological illness, severely impacts memory, behavior, and personality, posing a growing concern worldwide due to the aging population. Early and accurate detection is crucial as it enables preventive measures. However, current diagnostic methods are often inaccurate in identifying the disease in its early stages. Although deep learning-based bioimaging has shown promising results in medical image classification, challenges remain in achieving the highest accuracy for detecting AD. Existing approaches, such as ResNet50, VGG19, InceptionV3, and AlexNet have shown potential, but they often lack reliability and accuracy due to several issues. To address these gaps, this paper suggests a novel bioimaging technique by developing a custom Convolutional Neural Network (CNN) model for detecting AD. This model is designed with optimized layers to enhance feature extraction from medical images. The experiment’s first phase involved the construction of the custom CNN structure with three max-pooling layers, three convolutional layers, two dense layers, and one flattened layer. The Adam optimizer and categorical cross-entropy were adopted to compile the model. The model’s training was carried out on 100 epochs with the patience set to 10 epochs. The second phase involved augmentation of the dataset images and adding a dropout layer to the custom CNN model. Moreover, fine-tuned hyperparameters and advanced regularization methods were integrated to prevent overfitting. A comparative analysis of the proposed model with conventional models was performed on the dataset both before and after the data augmentation. The results validate that the proposed custom CNN model significantly overtakes pre-existing models, achieving the highest validation accuracy of 99.53% after data augmentation while maintaining the lowest validation loss of 0.0238. Its precision, recall, and F1 score remained consistently high across all classes, with perfect scores for the Moderate Demented and Non-Demented categories after augmentation, indicating superior classification capability.

Brain tumour detection and classification are critical for improving patient prognosis and treatment planning. However, manual identification from magnetic resonance imaging (MRI) scans is time-consuming, error-prone, and reliant on expert interpretation. The increasing complexity of tumour characteristics necessitates automated solutions to enhance accuracy and efficiency. This study introduces a novel ensemble deep learning model, boosted deformable and residual convolutional network with bi-directional convolutional long short-term memory (BDefRCNLSTM), for the classification and segmentation of brain tumours. The proposed framework integrates entropy-based local binary pattern (ELBP) for extracting spatial semantic features and employs the enhanced sooty tern optimisation (ESTO) algorithm for optimal feature selection. Additionally, an improved X-Net model is utilised for precise segmentation of tumour regions. The model is trained and evaluated on Figshare, Brain MRI, and Kaggle datasets using multiple performance metrics. Experimental results demonstrate that the proposed BDefRCNLSTM model achieves over 99% accuracy in both classification and segmentation, outperforming existing state-of-the-art approaches. The findings establish the proposed approach as a clinically viable solution for automated brain tumour diagnosis. The integration of optimised feature selection and advanced segmentation techniques improves diagnostic accuracy, potentially assisting radiologists in making faster and more reliable decisions.