Optimization of Spatial and Temporal Configuration of a Pressure Sensing Array to Predict Posture and Mobility in Lying.
Optimization of Spatial and Temporal Configuration of a Pressure Sensing Array to Predict Posture and Mobility in Lying.
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DOI：
10.3390/s23156872
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发表时间：
2023-08-02
期刊：
影响因子：
--
通讯作者：
Worsley P
中科院分区：
文献类型：
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作者：
Caggiari S;Jiang L;Filingeri D;Worsley P
已经开发了商业压力监测系统，以评估有患压疮风险的个人的床垫/坐垫之间的界面状况。最近，它们被用作长时间姿势和运动监测的替代品。然而，这些系统通常由高分辨率传感阵列组成，以超过1赫兹的频率采样数据。这不可避免地会导致大量数据，其中许多数据可能是冗余的。我们的研究旨在评估准确预测卧床时姿势和活动能力的最佳传感器数量和采集频率。使用连续压力监测仪(ForeSitePT，Xensors，加拿大卡尔加里)，以1赫兹的频率采样5664个传感器，评估在两种不同床垫(泡沫床垫和空气床垫)上进行卧姿的健康志愿者的界面压力。这些数据在空间和时间域进行了下采样。对于每种结构，评估压力参数，并使用接收器操作特征曲线(AUC)下的面积来确定它们区分姿势变化事件的能力。使用卷积神经网络(CNN)预测静态姿势。空间和时间向下采样的AUC值均呈非线性下降。结果表明，当采集频率低于0.3赫兹时，AUC降低。对于一些参数，例如压力梯度，传感器数量越少，AUC越高。姿势预测显示，与商业配置相比，泡沫床垫和空气床垫的准确率分别为63−71%和84−87%。这项研究表明，在传感器数量和采样频率相对较低的情况下，可以实现对姿势和运动事件的准确检测。
Commercial pressure monitoring systems have been developed to assess conditions at the interface between mattress/cushions of individuals at risk of developing pressure ulcers. Recently, they have been used as a surrogate for prolonged posture and mobility monitoring. However, these systems typically consist of high-resolution sensing arrays, sampling data at more than 1 Hz. This inevitably results in large volumes of data, much of which may be redundant. Our study aimed at evaluating the optimal number of sensors and acquisition frequency that accurately predict posture and mobility during lying. A continuous pressure monitor (ForeSitePT, Xsensor, Calgary, Canada), with 5664 sensors sampling at 1 Hz, was used to assess the interface pressures of healthy volunteers who performed lying postures on two different mattresses (foam and air designs). These data were down sampled in the spatial and temporal domains. For each configuration, pressure parameters were estimated and the area under the Receiver Operating Characteristic curve (AUC) was used to determine their ability in discriminating postural change events. Convolutional Neural Network (CNN) was employed to predict static postures. There was a non-linear decline in AUC values for both spatial and temporal down sampling. Results showed a reduction of the AUC for acquisition frequencies lower than 0.3 Hz. For some parameters, e.g., pressure gradient, the lower the sensors number the higher the AUC. Posture prediction showed a similar accuracy of 63−71% and 84−87% when compared to the commercial configuration, on the foam and air mattress, respectively. This study revealed that accurate detection of posture and mobility events can be achieved with a relatively low number of sensors and sampling frequency.
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DOI：
10.3390/s21010206
发表时间：
2020-12-31
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Hudec R;Matúška S;Kamencay P;Benco M
通讯作者：
Benco M
影响因子：
3.8
作者：
Coleman, Susanne;Nelson, E. Andrea;Nixon, Jane
通讯作者：
Nixon, Jane
DOI：
10.1682/jrrd.2007.03.0046
发表时间：
2008-01-01
影响因子：
--
作者：
Bogie, Kath;Wang, Xiaofeng;Sun, Jiayang
通讯作者：
Sun, Jiayang
影响因子：
2.9
作者：
Guest JF;Fuller GW;Vowden P
通讯作者：
Vowden P
影响因子：
4
作者：
通讯作者：
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