Hammann Torsten, Valič Jakob, Slapničar Gašper, Luštrek Mitja
Heidelberg University of Education, Heidelberg, Germany.
Jožef Stefan Institute, Ljubljana, Slovenia.
Int J Dev Disabil. 2022 Dec 15;70(5):887-903. doi: 10.1080/20473869.2022.2154928. eCollection 2024.
The preferences of people with profound intellectual and multiple disabilities (PIMD) often remain unfulfilled since it stays challenging to decode their idiosyncratic behavior resulting in a negative impact on their quality of life (QoL). Physiological data (i.e. heart rate (variability) and motion data) might be the missing piece for identifying emotions of people with PIMD, which positively affects their QoL. Machine learning (ML) processes and statistical analyses are integrated to discern and predict the potential relationship between physiological data and emotional states (i.e. numerical emotional states, descriptive emotional states and emotional arousal) in everyday interactions and activities of two participants with PIMD. Emotional profiles were created enabling a differentiation of the individual emotional behavior. Using ML classifiers and statistical analyses, the results regarding the phases partially confirm previous research, and the findings for the descriptive emotional states were good and even better for the emotional arousal. The results show the potential of the emotional profiles especially for practitioners and the possibility to get a better insight into the emotional experience of people with PIMD including physiological data. This seems to be the missing piece to better recognize emotions of people with PIMD with a positive impact on their QoL.
重度智力和多重残疾（PIMD）患者的偏好往往难以实现，因为解读他们独特的行为具有挑战性，这会对他们的生活质量（QoL）产生负面影响。生理数据（即心率（变异性）和运动数据）可能是识别PIMD患者情绪的关键因素，这对他们的生活质量有积极影响。机器学习（ML）过程和统计分析相结合，以识别和预测两名PIMD参与者在日常互动和活动中生理数据与情绪状态（即数字情绪状态、描述性情绪状态和情绪唤醒）之间的潜在关系。创建了情绪档案，以便区分个体的情绪行为。使用ML分类器和统计分析，各阶段的结果部分证实了先前的研究，描述性情绪状态的结果良好，情绪唤醒的结果更好。结果显示了情绪档案的潜力，特别是对从业者而言，以及深入了解PIMD患者情绪体验（包括生理数据）的可能性。这似乎是更好地识别PIMD患者情绪的关键因素，对他们的生活质量有积极影响。
