Virtual rehabilitation environments may afford greater patient personalization if they could harness the patient's affective state. Four states: anxiety, pain, engagement and tiredness (either physical or psychological), were hypothesized to be inferable from observable metrics of hand location and gripping strength-relevant for rehabilitation. Contributions are; (a) multiresolution classifier built from Semi-Naïve Bayesian classifiers, and (b) establishing predictive relations for the considered states from the motor proxies capitalizing on the proposed classifier with recognition levels sufficient for exploitation. 3D hand locations and gripping strength streams were recorded from 5 post-stroke patients whilst undergoing motor rehabilitation therapy administered through virtual rehabilitation along 10 sessions over 4 weeks. Features from the streams characterized the motor dynamics, while spontaneous manifestations of the states were labelled from concomitant videos by experts for supervised classification. The new classifier was compared against baseline support vector machine (SVM) and random forest (RF) with all three exhibiting comparable performances. Inference of the aforementioned states departing from chosen motor surrogates appears feasible, expediting increased personalization of virtual motor neurorehabilitation therapies.

中文翻译：

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从上肢运动不显眼地推断虚拟康复中的情感状态：一项可行性研究

如果虚拟康复环境能够利用患者的情感状态，它们可能会提供更大的患者个性化。四种状态：焦虑、疼痛、参与和疲倦（无论是身体上的还是心理上的），被假设为可从手部位置的可观察指标和与康复相关的握力推断出来。贡献是；(a) 从半朴素贝叶斯分类器构建的多分辨率分类器，以及 (b) 利用建议的具有足够识别水平的分类器，从运动代理中建立对所考虑状态的预测关系。5 名中风后患者在 4 周内通过虚拟康复进行 10 次运动康复治疗时，记录了 3D 手部位置和握力流。来自流的特征表征了运动动力学，而状态的自发表现由专家从伴随的视频中标记出来以进行监督分类。新分类器与基线支持向量机 (SVM) 和随机森林 (RF) 进行了比较，所有三个都表现出可比的性能。从选定的运动替代品推断上述状态似乎是可行的，加速了虚拟运动神经康复疗法的个性化。