Abstract

The world population is aging in the last few years and this trend is expected to increase in the future. The number of persons requiring assistance in their everyday life is also expected to rise. Luckily, smart homes are becoming a more and more compelling alternative to direct human supervision. Smart homes are equipped with sensors that, coupled with Artificial Intelligence (AI), can support their occupants whenever needed. At the heart of the problem is the recognition of activities. Human activity recognition is a complex problem due to the variety of sensors available, their impact on privacy, the high number of possible activities, and the several ways even a simple activity can be performed. This paper proposes a deep learning model combining LSTM and a tuned version of the EfficientNet model using transfer learning, data fusion, minimalist pre-processing as well as training for both activity and movement recognition using data from three ultra-wideband (UWB) radars. As regards activity recognition, experiments were conducted in a real and furnished apartment where 15 different activities were performed by 10 participants. Results showed an improvement of 18.63% over previous work on the same dataset with 65.59% in Top-1 accuracy using Leave-One-Subject-Out cross validation. Furthermore, the experiments that address movement recognition were conducted under the same conditions where a single participant was asked to perform four distinct arm movements with the three UWB radars positioned at two different heights. With an overall accuracy of 73% in Top-1, the detailed analysis of the results obtained showed that the proposed model was capable of recognizing accurately large and fine-grained movements. However, the medium-sized movements demonstrated a significant impact on the movement recognition due to an insufficient degree of variation between the four proposed movements.

中文翻译：

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超宽带数据作为用于人类活动识别的 EfficientNet 和 LSTM 组合架构的输入

摘要

过去几年世界人口正在老龄化，预计这种趋势在未来还会增加。预计日常生活中需要帮助的人数也会增加。幸运的是，智能家居正在成为一种越来越引人注目的替代直接人工监督的方法。智能家居配备了传感器，再加上人工智能 (AI)，可以在需要时为居住者提供支持。问题的核心是对活动的认可。人类活动识别是一个复杂的问题，因为可用的传感器种类繁多，它们对隐私的影响，大量可能的活动，以及甚至可以执行简单活动的多种方式。本文提出了一个深度学习模型，结合了 LSTM 和 EfficientNet 模型的调整版本，使用迁移学习、数据融合、使用来自三个超宽带 (UWB) 雷达的数据进行极简预处理以及活动和运动识别训练。至于活动识别，实验是在一个真实的、有家具的公寓中进行的，其中 10 名参与者进行了 15 种不同的活动。结果显示，使用 Leave-One-Subject-Out 交叉验证在同一数据集上的 Top-1 准确率比之前的工作提高了 18.63%，达到 65.59%。此外，涉及运动识别的实验是在相同的条件下进行的，其中要求单个参与者执行四个不同的手臂运动，三个 UWB 雷达位于两个不同的高度。在 Top-1 中的总体准确率为 73%，对所得结果的详细分析表明，所提出的模型能够准确识别大而细粒度的运动。然而，由于四个建议动作之间的变化程度不足，中型动作对动作识别产生了重大影响。