The increasing shortage of nursing staff and the acute risk of falls in nursing homes pose significant challenges for the healthcare system. This study presents the development of an automated fall detection system integrated into care beds, aimed at enhancing patient safety without compromising privacy through wearables or video monitoring. Mechanical vibrations transmitted through the bed frame are processed using a short-time Fourier transform, enabling robust classification of distinct human fall patterns with a convolutional neural network. Challenges pertaining to the quantity and diversity of the data are addressed, proposing the generation of additional data with a specific emphasis on enhancing variation. While the model shows promising results in distinguishing fall events from noise using lab data, further testing in real-world environments is recommended for validation and improvement. Despite limited available data, the proposed system shows the potential for an accurate and rapid response to falls, mitigating health implications, and addressing the needs of an aging population. This case study was performed as part of the ZIM Project. Further research on sensors enhanced by artificial intelligence will be continued in the ShapeFuture Project.

翻译：护理人员日益短缺与养老院跌倒风险加剧对医疗系统构成了重大挑战。本研究提出了一种集成于护理床的自动跌倒检测系统，旨在不依赖可穿戴设备或视频监控（从而保护隐私）的前提下提升患者安全。通过床架传导的机械振动经短时傅里叶变换处理，并利用卷积神经网络实现对不同人体跌倒模式的鲁棒分类。针对数据量与多样性的挑战，本研究提出通过重点增强变异性的方式生成补充数据。虽然模型在实验室数据中展现出区分跌倒事件与噪声的良好效果，但仍建议在实际环境中进行进一步测试以验证并改进系统。尽管可用数据有限，所提出的系统仍显示出对跌倒事件实现准确快速响应的潜力，有助于减轻健康损害并应对人口老龄化的需求。本案例研究是ZIM项目的一部分。关于人工智能增强传感器的进一步研究将在ShapeFuture项目中持续推进。