Romaniszyn-Kania Patrycja, Pollak Anita, Kania Damian, Mitas Andrzej W
Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800, Zabrze, Poland.
Institute of Psychology, University of Silesia in Katowice, Grażyńskiego 53, 40-126, Katowice, Poland.
Sci Rep. 2025 Mar 11;15(1):8382. doi: 10.1038/s41598-025-92368-z.
Postural defects are one of the main diseases reported to be at the top of the list of diseases of civilisation. The present study aimed to develop a novel approach to defining a set of measurable physiological biomarkers and psychological characteristics with identifiable information content and data analysis, enabling the determination of the adaptation period and conditioning the effectiveness of the treatment in personalised rehabilitation. During the rehabilitation, multimodal physiological signals (electrodermal activity, blood volume pulse) and psychological data (anxiety as a state and as a trait, temperament) were recorded on a group of 20 subjects over a period of three months (120 measurement sessions). Preprocessing of the physiological signals and psychological data was performed. A stepwise forward regression method was used to determine a set of successive statistically significant predictors of the model. For each group, a matrix of coefficients for fitting a linear regression of changes in the value of a given predictor in subsequent measurement was determined. Adaptive Boosting was chosen to develop a mathematical model of the patient. The analysis of the results of the psychological tests enabled the participants to be divided into five new, previously undefined subgroups, which were both labels for the classifier. Using the dimensionality reduction method, 8 significant, statistically important features were extracted. AdaBoost classifier allowed the creation of a prediction model for therapy parameters with 84% accuracy, and the Pseudo-Random Number Generator was used to check the validity of it. The AdaBoost algorithm was used again to check the dynamics of changes in regression coefficients for individual groups-a set of psychophysiological characteristics identified as the basis for personalised therapeutic interventions. Each individual requires time to adapt to a new situation, conditioned by their characteristics. An appropriate interdisciplinary approach to professional rehabilitation influences the therapeutic process's quality, duration, and effectiveness. Physiological features determine the patient's involvement in the rehabilitation process, allowing robust personalisation of therapy in a closed feedback loop. The fusion of psychophysiological data and multimodal measurements enables the development of a unique behavioral-physiological profile of the patient undergoing rehabilitation.
姿势缺陷是据报道在文明病列表中名列前茅的主要疾病之一。本研究旨在开发一种新方法，通过可识别的信息内容和数据分析来定义一组可测量的生理生物标志物和心理特征，从而确定适应期并调节个性化康复治疗的效果。在康复期间，对一组20名受试者在三个月的时间内（120次测量 session）记录了多模态生理信号（皮肤电活动、血容量脉搏）和心理数据（状态焦虑和特质焦虑、气质）。对生理信号和心理数据进行了预处理。使用逐步向前回归方法来确定模型的一组连续的具有统计学意义的预测因子。对于每个组，确定了用于拟合后续测量中给定预测因子值变化的线性回归的系数矩阵。选择自适应增强算法来开发患者的数学模型。对心理测试结果的分析使参与者能够被分为五个新的、以前未定义的亚组，这些亚组都是分类器的标签。使用降维方法，提取了8个显著的、具有统计学意义的特征。AdaBoost分类器允许创建一个治疗参数预测模型，准确率为84%，并使用伪随机数生成器来检查其有效性。再次使用AdaBoost算法来检查各个组回归系数的变化动态——一组被确定为个性化治疗干预基础的心理生理特征。每个人都需要时间来适应新情况，这取决于他们的特征。一种适当的跨学科专业康复方法会影响治疗过程的质量、持续时间和效果。生理特征决定了患者参与康复过程的程度，从而在封闭的反馈回路中实现治疗的强大个性化。心理生理数据和多模态测量的融合能够为正在接受康复治疗的患者开发独特的行为生理特征。
