As one of the main threats to people’s health, especially for the elderly, falls have caused a large number of accidents. Detecting falls in time can minimize the severity of the injury and save lives. Therefore, designing fall detection algorithms to assist people’s daily life has been a hot research topic. In the last decade, different fall detection approaches based on wearable sensors have been proposed. However, since the hardware resources of wearable sensors are very limited, designing accurate but energy-efficient fall detection algorithms remains an open challenge. To deal with this, an accurate but low-cost fall detection algorithm is proposed in this paper. Particularly, a novel cascade and parallel method that efficiently employs the characteristics of human falls and the advanced modeling ability of the neural network is proposed. Also, a novel design of a lightweight convolutional neural network with self-attention is proposed to achieve the best recognition/numerical complexity tradeoff. The proposed method, namely CMFALL, is evaluated together with a multitude of state-of-the-art models on a large dataset, where it performs the best with an F1-score exceeding 99%. Meanwhile, its computational cost and model size are only a few thousandths of other models. Such low computational cost and small size not only enable to embed it in a wearable sensor but also make the system power requirements quite low, which can enhance the autonomy of the wearable fall detector.

中文翻译：

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CMFALL：使用腰装三轴加速度计信号的级联和并行多状态跌倒检测算法

跌倒作为危害人们健康的主要威胁之一，尤其是对老年人而言，已经引发了大量事故。及时发现跌倒可以最大限度地减少伤害的严重程度并挽救生命。因此，设计跌倒检测算法来辅助人们的日常生活一直是一个热门的研究课题。在过去的十年中，已经提出了基于可穿戴传感器的不同跌倒检测方法。然而，由于可穿戴传感器的硬件资源非常有限，设计准确但节能的跌倒检测算法仍然是一个开放的挑战。为了解决这个问题，本文提出了一种准确但成本低的跌倒检测算法。特别地，提出了一种新的级联和并行方法，有效地利用了人类跌倒的特点和神经网络的先进建模能力。此外，还提出了一种具有自注意力的轻量级卷积神经网络的新颖设计，以实现最佳的识别/数字复杂度权衡。所提出的方法，即 CMFALL，在大型数据集上与众多最先进的模型一起进行评估，在 F1 分数超过 99% 时表现最佳。同时，其计算成本和模型大小仅为其他模型的千分之几。如此低的计算成本和小尺寸不仅使其能够嵌入到可穿戴传感器中，而且使系统功率要求非常低，这可以增强可穿戴跌倒检测器的自主性。在大型数据集上与众多最先进的模型一起进行评估，在 F1 分数超过 99% 时表现最佳。同时，其计算成本和模型大小仅为其他模型的千分之几。如此低的计算成本和小尺寸不仅使其能够嵌入到可穿戴传感器中，而且使系统功率要求非常低，这可以增强可穿戴跌倒检测器的自主性。在大型数据集上与众多最先进的模型一起进行评估，在 F1 分数超过 99% 时表现最佳。同时，其计算成本和模型大小仅为其他模型的千分之几。如此低的计算成本和小尺寸不仅使其能够嵌入到可穿戴传感器中，而且使系统功率要求非常低，这可以增强可穿戴跌倒检测器的自主性。