  As the global population ages, the number of fall-related incidents is on the
rise. Effective fall detection systems, specifically in healthcare sector, are
crucial to mitigate the risks associated with such events. This study evaluates
the role of visual context, including background objects, on the accuracy of
fall detection classifiers. We present a segmentation pipeline to
semi-automatically separate individuals and objects in images. Well-established
models like ResNet-18, EfficientNetV2-S, and Swin-Small are trained and
evaluated. During training, pixel-based transformations are applied to
segmented objects, and the models are then evaluated on raw images without
segmentation. Our findings highlight the significant influence of visual
context on fall detection. The application of Gaussian blur to the image
background notably improves the performance and generalization capabilities of
all models. Background objects such as beds, chairs, or wheelchairs can
challenge fall detection systems, leading to false positive alarms. However, we
demonstrate that object-specific contextual transformations during training
effectively mitigate this challenge. Further analysis using saliency maps
supports our observation that visual context is crucial in classification
tasks. We create both dataset processing API and segmentation pipeline,
available at https://github.com/A-NGJ/image-segmentation-cli.
