Instituto Superior Técnico, Unviersidade de Lisboa, 1049-001 Lisboa, Portugal.
Institute for Systems and Robotics, 1049-001 Lisboa, Portugal.
Sensors (Basel). 2023 Jan 19;23(3):1146. doi: 10.3390/s23031146.
More than 37 million falls that require medical attention occur every year, mainly affecting the elderly. Besides the natural consequences of falls, most aged adults with a history of falling are likely to develop a fear of falling, leading to a decrease in their mobility level and impacting their overall quality of life. Previous wrist-based datasets revealed limitations such as unrealistic recording set-ups, lack of proper documentation and, most importantly, the absence of elderly people's movements. Therefore, this work proposes a new wrist-based dataset to tackle this problem. With this dataset, exhaustive research is carried out with the low computational FS-1 feature set (maximum, minimum, mean and variance) with various machine learning methods. This work presents an accelerometer-only fall detector streaming data at 50 Hz, using the low computational FS-1 feature set to train a 3NN algorithm with Euclidean distance, with a window size of 9 s. This work had battery and memory limitations in mind. It also developed a learning version that boosts the fall detector's performance over time, achieving no single false positives or false negatives over four days.
每年有超过 3700 万次需要医疗关注的跌倒事件发生，主要影响老年人。除了跌倒的自然后果外，大多数有跌倒史的老年人很可能会产生跌倒恐惧，从而降低他们的活动水平，并影响他们的整体生活质量。以前基于手腕的数据集存在一些局限性，例如不切实际的记录设置、缺乏适当的文档记录，最重要的是，缺乏老年人的运动数据。因此，这项工作提出了一个新的基于手腕的数据集来解决这个问题。有了这个数据集，我们使用计算量低的 FS-1 特征集（最大值、最小值、平均值和方差）和各种机器学习方法进行了详尽的研究。这项工作提出了一个仅使用加速度计的跌倒检测流媒体数据，以 50Hz 的频率运行，使用计算量低的 FS-1 特征集来训练 3NN 算法，使用欧几里得距离作为距离度量，窗口大小为 9 秒。这项工作考虑了电池和内存的限制。它还开发了一个学习版本，可以随着时间的推移提高跌倒检测器的性能，在四天内没有出现单个误报或漏报。
