## 3 Methodology
### Data Acquisition and Preprocessing  
In this study, we utilize the publicly available Fall Detection Dataset, hosted on Kaggle, and compiled by [PERSON] [28]. This dataset was selected for its relevance to human activity recognition in healthcare applications, particularly fall detection, and is compatible with object detection pipelines using bounding box annotations in YOLOformat. All images and labels are used in full compliance with the licensing terms specified by the original uploader. The dataset consists of 463 annotated image-label pairs, categorized into three primary activity classes: Fall Detected, Walking, and Sitting. These are distributed into 374 images for training and 89 images for validation. Each image has a corresponding annotation file stored in YOLO format, consisting of a single class identifier followed by four normalized bounding box coordinates that localize human subjects within the image. This format facilitates object detection tasks by providing the spatial information necessary for region-based CNN models. Annotations were originally generated using the makesense.ai online annotation platform, as stated in the Kaggle dataset description. Bounding boxes were created around individuals performing various activities and assigned one of the three activity labels. For visualization, the dataset includes color-coded annotation overlays: violet for \"Fall Detected,\" blue for \"Walking,\" and green for \"Sitting.\" An example of this annotation schema is shown in Figure 2. To prepare the data for classification, the following preprocessing steps were applied:  
* Bounding boxes were used to extract cropped regions around human subjects. * Image resizing was performed, resizing each extracted crop to a fixed dimension of 128 \(\times\) 128 pixels. * Normalization was applied to scale pixel values to the range [0, 1] [0, 1] [0, 1]. * Fall = 0;
- NoFall = 1 (aggregating the Walking and Sitting categories). The resulting dataset was split using stratified sampling to ensure balanced representation of the fall and non-fall categories across all subsets:  
* 80% for training (370 samples);
* 10% for validation (46 samples);  
Figure 2: Annotated image from the publicly available Fall Detection Dataset on Kaggle, illustrating “Fall Detected” and “Sitting” activities, using labeled bounding boxes. The dataset was not created or manually annotated by the authors; all individuals and annotations originate in the published dataset. Usage complies with the dataset’s open-access licensing terms. * 10% for testing (47 samples). The dataset offers a controlled yet realistic set of indoor activity images that simulate common scenarios encountered in fall-detection use cases. While the individuals appear to be healthy adults and some activities are likely staged, the variety of poses, lighting, and spatial layouts present meaningful challenges for deep learning models. Additionally, the dataset supports both multi-class classification and binary fall vs. non-fall scenarios, making it sufficiently flexible for different model configurations. Importantly, since the dataset is pre-published and publicly distributed, ethical concerns such as informed consent, data privacy, or manual annotation consistency fall under the responsibility of the original dataset creators. The authors collected no identifiable personal data, and no modifications were made to the original dataset beyond preprocessing steps (e.g., resizing, normalization, and train/val split). This dataset was chosen over manual data collection or synthetic data generation due to its public availability, annotation quality, and ease of integration with standard deep learning pipelines. Its structure and licensing make it an ideal resource for reproducible fall-detection research in privacy-aware contexts such as federated learning. This example illustrates the multi-object and multi-class nature of the dataset, where different human actions are simultaneously captured within a single frame.