This paper presents a novel approach to predicting self-calibration in a pressure sensor using a proposed Levenberg Marquardt Back Propagation Artificial Neural Network (LMBP-ANN) model. The self-calibration algorithm should be able to fix major problems in the pressure sensor such as hysteresis, variation in gain and lack of linearity with high accuracy. The traditional calibration process for this kind of sensor is a time-consuming task because it is usually done through manual and repetitive identification. Furthermore, a traditional computational method is inadequate for solving the problem since it is extremely difficult to resolve the mathematical formula among multiple confounding pressure variables. Accordingly, this paper describes a new self-calibration methodology for nonlinear pressure sensors based on an LMBP-ANN model. The proposed method was achieved using a collected dataset from pressure sensors in real time. The load cell will be used as a reference for measuring the applied force. The proposed method was validated by comparing the output pressure of the trained network with the experimental target pressure (reference). This paper also shows that the proposed model exhibited a remarkable performance than traditional methods with a max mean square error of 0.17325 and an R-value over 0.99 for the total response of training, testing and validation. To verify the proposed model’s capability to build a self-calibration algorithm, the model was tested using an untrained input data set. As a result, the proposed LMBP-ANN model for self-calibration purposes is able to successfully predict the desired pressure over time, even the uncertain behaviour of the pressure sensors due to its material creep. This means that the proposed model overcomes the problems of hysteresis, variation in gain and lack of linearity over time. In return, this can be used to enhance the durability of the grasping mechanism, leading to a more robust and secure grasp for paralyzed hands. Furthermore, the exposed analysis approach in this paper can be a useful methodology for the user to evaluate the performance of any measurement system in a real-time environment.

The primary objective of this research investigation is to examine object detection within the specific environment of a nursery. The nursery environment presents a complex scene with a multitude of objects, varying in size and background. To simulate real-world conditions, we gathered data from a nursery. Our study is centered around the detection of small objects, particularly in nursery settings where objects that include stationery, toys, and small accessories are commonly present. These objects are of significant importance in facilitating cognition of the activities and interactions taking place within the confines of the room. Due to their small size and the possibility of occlusion by other objects or children, precisely detecting these objects is regrettably fraught with inherent challenges. This study introduces YOLOv8s-NE in an effort to enhance the detection of small objects found in the nursery. We improve the standard YOLOv8 by incorporating an extra detection head to effectively for small objects. We replace the C2f module with C2f_DCN to further improve the model’s ability to detect objects of varying sizes that can be deformed or occluded within the image. Furthermore, we introduce NAM attention to focus on the important features and ignore less informative ones, thereby improving the accuracy of our proposed model. We used the five-fold cross-validation approach to split the dataset in order to evaluate the performance of YOLOv8s-NE, thereby facilitating a more comprehensive model evaluation. Our model achieves $34.1\%$ of AP_s, $45.1\%$ of mAP_50:90, and $76.7\%$ of mAP₅₀ detection accuracy at 37.55 FPS on the nursery dataset. In terms of AP_s, mAP_50:90, and mAP₅₀ metrics, our proposed YOLOv8s-NE model outperforms the standard YOLOv8s model, with improvements of $4.6\%$, $4.7\%$, and $3.9\%$, respectively. We apply our proposed YOLOv8s-NE model as a safety system by developing an algorithm to detect objects on top of cabinets that could be potentially risky to children.

Journal of Medical and Bioengineering (JOMB)

Detection, tracking, and re-identification (ReID) of children wearing similar uniforms in a kindergarten environment is a very complex challenge for computer vision. Traditional surveillance systems or simple convolutional neural network (CNN) models often fail to distinguish children in crowds and occlusions. To address this challenge, this study proposes a novel hybrid framework combining YOLOv8 and Vision Transformer (ViT). Using YOLOv8 for detection and ViT for global feature extraction, we trained the model on a custom dataset of 31,521 images, achieving an overall accuracy of 93.75%, and the public benchmark MOT20 dataset of 28,630 images, achieving an overall accuracy of 96.02%. Our system showed remarkable success in tracking performance, where it achieved 86.7% MOTA and 99.7% IDF1 scores. This high IDF1 score proves that the model is highly effective in preventing identity switch. The main novelty of this study is the behavioral analysis of children beyond the boundaries of surveillance, where we measure walking distance and trajectory, and screen time. Finally, through cross-dataset comparison with the MOT20 public benchmark, we demonstrated that our proposed customized model is much more effective than current state-of-the-art methods in overcoming the domain gap in specific environments such as kindergarten.