Customized static orthoses in rehabilitation clinics often cause side effects, such as discomfort and skin damage due to excessive local contact pressure. Currently, clinicians adjust orthoses to reduce high contact pressure based on subjective feedback from patients. However, the adjustment is inefficient and prone to variability due to the unknown contact pressure distribution as well as differences in discomfort due to pressure across patients. This paper proposed a new method to predict a threshold of contact pressure (pressure limit) associated with moderate discomfort at each critical spot under hand orthoses. A new pressure sensor skin with 13 sensing units was configured from FEA results of pressure distribution simulated with hand geometry data of six healthy participants. It was used to measure contact pressure under two types of customized orthoses for 40 patients with bone fractures. Their subjective perception of discomfort was also measured using a 6 scores discomfort scale. Based on these data, five critical spots were identified that correspond to high discomfort scores (>1) or high pressure magnitudes (>0.024 MPa). An artificial neural network was trained to predict contact pressure at each critical spot with orthosis type, gender, height, weight, discomfort scores and pressure measurements as input variables. The neural networks show satisfactory prediction accuracy with ${R}^{{2}}$ values over 0.81 of regression between network outputs and measurements. This new method predicts a set of pressure limits at critical locations under the orthosis that the clinicians can use to make orthosis adjustment decisions.

中文翻译：

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柔软的压力传感器皮肤可预测手部矫正下的接触压力极限

康复诊所中定制的静态矫形器通常会引起副作用，例如由于局部接触压力过大而引起的不适和皮肤损伤。当前，临床医生根据患者的主观反馈来调整矫形器以降低高接触压力。然而，由于未知的接触压力分布以及由于患者之间的压力所引起的不适感的差异，调节效率低下并且易于变化。本文提出了一种预测手部矫正下每个关键部位与中等不适感相关的接触压力阈值（压力极限）的新方法。根据用6位健康参与者的手部几何数据模拟的压力分布的FEA结果，配置了具有13个传感单元的新型压力传感器皮肤。它用于在两种类型的定制矫形器中测量40例骨折患者的接触压力。他们的主观不适感也使用6分不适量表进行测量。根据这些数据，确定了五个临界点，分别对应于高不适感评分（> 1）或高压力幅度（> 0.024 MPa）。训练了一个人工神经网络，以矫形器类型，性别，身高，体重，不适感评分和压力测量值作为输入变量来预测每个关键点的接触压力。神经网络显示出令人满意的预测精度，确定了五个临界点，分别对应于高不适感评分（> 1）或高压幅度（> 0.024 MPa）。训练了一个人工神经网络，以矫形器类型，性别，身高，体重，不适感评分和压力测量值作为输入变量来预测每个关键点的接触压力。神经网络显示出令人满意的预测精度，确定了五个临界点，分别对应于高不适感评分（> 1）或高压幅度（> 0.024 MPa）。训练了一个人工神经网络，以矫形器类型，性别，身高，体重，不适感评分和压力测量值作为输入变量来预测每个关键点的接触压力。神经网络显示出令人满意的预测精度， $ {R} ^ {{2}} $ 网络输出和测量之间的回归值超过0.81。这种新方法可以预测矫形器下关键部位的压力极限，临床医生可以使用这些压力极限做出矫形器调整决策。