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.

近年来，统计数据证实，老年人口数量不断增加。衰老总是对人类的健康产生重大影响；从生物学的角度来看，这个过程通常会导致几种类型的疾病，主要是由于机体的损伤。在这种背景下，医疗保健在治疗过程中发挥着重要作用，试图解决这些问题。衰老的后果之一是压疮（PU）的形成，这不仅从健康角度而且从心理角度对医院患者的生活质量产生负面影响。从这个意义上说，电子医疗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并且能够有效地防止此类问题。此外，所提出的解决方案通常是昂贵且具有侵入性的。在本文中，我们能够收集来自惯性传感器的数据，目的是根据以人为中心的计算 (HC) 范式，设计和实现非侵入式可穿戴传感器系统，通过深度学习来预防 PU技术。特别是，使用惯性传感器，我们能够估计患者的位置，并在他/她长时间保持同一位置时发送警报信号。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构建一个数据集，我们在这里展示结果，展示该技术的可行性以及我们能够达到的准确性水平，比较我们的模型与其他流行的机器学习方法。