用床体振动数据训练模型，无感检测老人跌倒。
Bed-Attached Vibration Sensor System: A Machine Learning Approach for Fall Detection in Nursing Homes
- 通过床架振动信号提取特征，用卷积神经网络识别跌倒模式。
- 在实验室数据上实现高精度区分跌倒与日常动作，准确率待实测验证。
- 适合养老机构部署，兼顾隐私与快速响应，无需穿戴或监控摄像头。
护理人员短缺与养老院中跌倒风险加剧，给医疗系统带来严峻挑战。本研究开发了一种集成于护理床的自动化跌倒检测系统，旨在不依赖可穿戴设备或视频监控的前提下提升患者安全。通过短时傅里叶变换处理床架传递的机械振动信号，利用卷积神经网络对不同人体跌倒模式进行鲁棒分类。针对数据量少、多样性不足的问题，提出通过特定方式生成更多数据以增强样本变化性。尽管模型在实验室数据上已展现出良好区分跌倒与噪声的能力，仍需在真实环境进一步测试以验证和优化性能。尽管可用数据有限，该系统已显示出精准、快速响应跌倒的潜力，有助于减轻健康影响并满足老龄化社会需求。本案例研究为ZIM项目的一部分，后续将在ShapeFuture项目中继续开展由人工智能增强的传感器研究。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。