Architecture and Computer Technology Department, ETSII-EPS, University of Seville, 41004 Sevilla, Spain.
Robotics and Technology of Computers Laboratory, University of Seville, 41004 Sevilla, Spain.
Sensors (Basel). 2021 Mar 8;21(5):1889. doi: 10.3390/s21051889.
Falls are one of the leading causes of permanent injury and/or disability among the elderly. When these people live alone, it is convenient that a caregiver or family member visits them periodically. However, these visits do not prevent falls when the elderly person is alone. Furthermore, in exceptional circumstances, such as a pandemic, we must avoid unnecessary mobility. This is why remote monitoring systems are currently on the rise, and several commercial solutions can be found. However, current solutions use devices attached to the waist or wrist, causing discomfort in the people who wear them. The users also tend to forget to wear the devices carried in these positions. Therefore, in order to prevent these problems, the main objective of this work is designing and recollecting a new dataset about falls, falling risks and activities of daily living using an ankle-placed device obtaining a good balance between the different activity types. This dataset will be a useful tool for researchers who want to integrate the fall detector in the footwear. Thus, in this work we design the fall-detection device, study the suitable activities to be collected, collect the dataset from 21 users performing the studied activities and evaluate the quality of the collected dataset. As an additional and secondary study, we implement a simple Deep Learning classifier based on this data to prove the system's feasibility.
跌倒 是老年人永久性伤害和/或残疾的主要原因之一。当这些人独自生活时，方便的做法是由护理人员或家庭成员定期探望他们。然而，当老年人独自一人时，这些探望并不能防止跌倒。此外，在特殊情况下，如大流行期间，我们必须避免不必要的移动。这就是为什么远程监控系统目前正在兴起，并且可以找到几种商业解决方案。然而，当前的解决方案使用附在腰部或手腕上的设备，这会给佩戴者带来不适。用户也往往会忘记佩戴这些设备。因此，为了防止这些问题，这项工作的主要目标是设计和收集一个新的数据集，该数据集使用脚踝放置设备关于跌倒、跌倒风险和日常生活活动，在不同活动类型之间取得良好的平衡。该数据集将是研究人员的有用工具，他们希望将跌倒探测器集成到鞋类中。因此，在这项工作中，我们设计了跌倒检测设备，研究了要收集的合适活动，从 21 名执行研究活动的用户那里收集了数据集，并评估了收集数据集的质量。作为一项额外的次要研究，我们基于该数据实现了一个简单的深度学习分类器，以证明系统的可行性。
