A deep learning approach for pressure ulcer prevention using wearable computing
A deep learning approach for pressure ulcer prevention using wearable computing
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DOI：
10.1186/s13673-020-0211-8
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发表时间：
2020-02-03
影响因子：
6.6
通讯作者：
Puliafito, Antonio
中科院分区：
文献类型：
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作者：
Cicceri, Giovanni;De Vita, Fabrizio;Puliafito, Antonio
近年来，统计数据证实，老年人的数量正在增加。衰老总是对人类的健康有很大的影响；从生物学的角度来看，这个过程通常会导致几种疾病，主要是由于机体的损害。在这种情况下，医疗保健在治愈过程中发挥着重要作用，试图解决这些问题。老龄化的后果之一是形成压疮(PU)，这不仅从健康角度而且从心理上对医院患者的生活质量产生负面影响。从这个意义上说，电子健康提出了几种方法来处理这个问题，然而，这些方法并不总是非常准确和有能力有效地预防此类问题。此外，建议的解决方案通常昂贵且具有侵入性。在这篇论文中，我们能够收集来自惯性传感器的数据，目的是按照以人为中心的计算(HC)范式，设计和实现一个非侵入性的可穿戴传感器系统，通过深度学习技术预防PUS。特别是，使用惯性传感器，我们能够估计患者的位置，并在患者保持同一位置太长时间时发送警报信号。为了训练我们的系统，我们通过监测一组患者在住院期间的位置来建立一个数据集，我们在这里展示了结果，证明了这种技术的可行性和我们能够达到的精度水平，并将我们的模型与其他流行的机器学习方法进行了比较。
In recent years, statistics have confirmed that the number of elderly people is increasing. Aging always has a strong impact on the health of a human being; from a biological of point view, this process usually leads to several types of diseases mainly due to the impairment of the organism. In such a context, healthcare plays an important role in the healing process, trying to address these problems. One of the consequences of aging is the formation of pressure ulcers (PUs), which have a negative impact on the life quality of patients in the hospital, not only from a healthiness perspective but also psychologically. In this sense, e-health proposes several approaches to deal with this problem, however, these are not always very accurate and capable to prevent issues of this kind efficiently. Moreover, the proposed solutions are usually expensive and invasive. In this paper we were able to collect data coming from inertial sensors with the aim, in line with the Human-centric Computing (HC) paradigm, to design and implement a non-invasive system of wearable sensors for the prevention of PUs through deep learning techniques. In particular, using inertial sensors we are able to estimate the positions of the patients, and send an alert signal when he/she remains in the same position for too long a period of time. To train our system we built a dataset by monitoring the positions of a set of patients during their period of hospitalization, and we show here the results, demonstrating the feasibility of this technique and the level of accuracy we were able to reach, comparing our model with other popular machine learning approaches.