Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea.
Department of Industrial Design, School of Mechanical Engineering, Southeast University, Nanjing 211189, China.
Sensors (Basel). 2023 Oct 14;23(20):8459. doi: 10.3390/s23208459.
Falls represent a significant health concern for the elderly. While studies on deep learning-based preimpact fall detection have been conducted to mitigate fall-related injuries, additional efforts are needed for embedding in microcomputer units (MCUs). In this study, ConvLSTM, the state-of-the-art model, was benchmarked, and we attempted to lightweight it by leveraging features from image-classification models VGGNet and ResNet while maintaining performance for wearable airbags. The models were developed and evaluated using data from young subjects in the KFall public dataset based on an inertial measurement unit (IMU), leading to the proposal of TinyFallNet based on ResNet. Despite exhibiting higher accuracy (97.37% < 98.00%) than the benchmarked ConvLSTM, the proposed model requires lower memory (1.58 MB > 0.70 MB). Additionally, data on the elderly from the fall data of the FARSEEING dataset and activities of daily living (ADLs) data of the KFall dataset were analyzed for algorithm validation. This study demonstrated the applicability of image-classification models to preimpact fall detection using IMU and showed that additional tuning for lightweighting is possible due to the different data types. This research is expected to contribute to the lightweighting of deep learning models based on IMU and the development of applications based on IMU data.
跌倒对老年人来说是一个严重的健康问题。虽然已经有研究基于深度学习的预冲击跌倒检测来减轻与跌倒相关的伤害，但仍需要在微计算机单元（MCU）中嵌入该技术。在本研究中，我们基准测试了最先进的模型 ConvLSTM，并尝试通过利用图像分类模型 VGGNet 和 ResNet 的特征来实现轻量化，同时保持对可穿戴气囊的性能。该模型是使用 KFall 公共数据集（基于惯性测量单元（IMU））中的年轻受试者的数据开发和评估的，从而提出了基于 ResNet 的 TinyFallNet。尽管所提出的模型比基准的 ConvLSTM 具有更高的准确性（97.37%<98.00%），但需要的内存更少（1.58MB>0.70MB）。此外，还分析了 FARSEEING 数据集的老年人跌倒数据和 KFall 数据集的日常生活活动（ADL）数据，以验证算法。本研究证明了使用 IMU 的图像分类模型在预冲击跌倒检测中的适用性，并表明由于数据类型不同，可能进行额外的轻量化调整。这项研究有望为基于 IMU 的深度学习模型的轻量化和基于 IMU 数据的应用开发做出贡献。
