The global trend of increasing life expectancy introduces new challenges with far-reaching implications. Among these, the risk of falls among older adults is particularly significant, affecting individual health and the quality of life, and placing an additional burden on healthcare systems. Existing fall detection systems often have limitations, including delays due to continuous server communication, high false-positive rates, low adoption rates due to wearability and comfort issues, and high costs. In response to these challenges, this work presents a reliable, wearable, and cost-effective fall detection system. The proposed system consists of a fit-for-purpose device, with an embedded algorithm and an Inertial Measurement Unit (IMU), enabling real-time fall detection. The algorithm combines a Threshold-Based Algorithm (TBA) and a neural network with low number of parameters based on a Transformer architecture. This system demonstrates notable performance with 95.29% accuracy, 93.68% specificity, and 96.66% sensitivity, while only using a 0.38% of the trainable parameters used by the other approach.

预期寿命增加的全球趋势带来了具有深远影响的新挑战。其中，老年人跌倒的风险尤为显着，影响个人健康和生活质量，并给医疗保健系统带来额外负担。现有的跌倒检测系统通常存在局限性，包括由于连续服务器通信而导致的延迟、高误报率、由于耐磨性和舒适性问题而导致的采用率低以及成本高。为了应对这些挑战，这项工作提出了一种可靠、可穿戴且经济高效的跌倒检测系统。所提出的系统由一个适合用途的设备组成，具有嵌入式算法和惯性测量单元（IMU），可实现实时跌倒检测。该算法结合了基于阈值的算法 (TBA) 和基于 Transformer 架构的低参数神经网络。该系统表现出显着的性能，准确度为 95.29%，特异性为 93.68%，灵敏度为 96.66%，而仅使用其他方法所用可训练参数的 0.38%。