PDF(Pubmed)
Abstract：
BACKGROUND: In-home rehabilitation systems are a promising, potential alternative to conventional therapy for stroke survivors. Unfortunately, physiological differences between participants and sensor displacement in wearable sensors pose a significant challenge to classifier performance, particularly for people with stroke who may encounter difficulties repeatedly performing trials. This makes it challenging to create reliable in-home rehabilitation systems that can accurately classify gestures.
METHODS: Twenty individuals who suffered a stroke performed seven different gestures (mass flexion, mass extension, wrist volar flexion, wrist dorsiflexion, forearm pronation, forearm supination, and rest) related to activities of daily living. They performed these gestures while wearing EMG sensors on the forearm, as well as FMG sensors and an IMU on the wrist. We developed a model based on prototypical networks for one-shot transfer learning, K-Best feature selection, and increased window size to improve model accuracy. Our model was evaluated against conventional transfer learning with neural networks, as well as subject-dependent and subject-independent classifiers: neural networks, LGBM, LDA, and SVM.
RESULTS: Our proposed model achieved 82.2% hand-gesture classification accuracy, which was better (P<0.05) than one-shot transfer learning with neural networks (63.17%), neural networks (59.72%), LGBM (65.09%), LDA (63.35%), and SVM (54.5%). In addition, our model performed similarly to subject-dependent classifiers, slightly lower than SVM (83.84%) but higher than neural networks (81.62%), LGBM (80.79%), and LDA (74.89%). Using K-Best features improved the accuracy in 3 of the 6 classifiers used for evaluation, while not affecting the accuracy in the other classifiers. Increasing the window size improved the accuracy of all the classifiers by an average of 4.28%.
CONCLUSIONS: Our proposed model showed significant improvements in hand-gesture recognition accuracy in individuals who have had a stroke as compared with conventional transfer learning, neural networks and traditional machine learning approaches. In addition, K-Best feature selection and increased window size can further improve the accuracy. This approach could help to alleviate the impact of physiological differences and create a subject-independent model for stroke survivors that improves the classification accuracy of wearable sensors.
BACKGROUND: The study was registered in Chinese Clinical Trial Registry with registration number CHiCTR1800017568 in 2018/08/04.
METHODS: Twenty individuals who suffered a stroke performed seven different gestures (mass flexion, mass extension, wrist volar flexion, wrist dorsiflexion, forearm pronation, forearm supination, and rest) related to activities of daily living. They performed these gestures while wearing EMG sensors on the forearm, as well as FMG sensors and an IMU on the wrist. We developed a model based on prototypical networks for one-shot transfer learning, K-Best feature selection, and increased window size to improve model accuracy. Our model was evaluated against conventional transfer learning with neural networks, as well as subject-dependent and subject-independent classifiers: neural networks, LGBM, LDA, and SVM.
RESULTS: Our proposed model achieved 82.2% hand-gesture classification accuracy, which was better (P<0.05) than one-shot transfer learning with neural networks (63.17%), neural networks (59.72%), LGBM (65.09%), LDA (63.35%), and SVM (54.5%). In addition, our model performed similarly to subject-dependent classifiers, slightly lower than SVM (83.84%) but higher than neural networks (81.62%), LGBM (80.79%), and LDA (74.89%). Using K-Best features improved the accuracy in 3 of the 6 classifiers used for evaluation, while not affecting the accuracy in the other classifiers. Increasing the window size improved the accuracy of all the classifiers by an average of 4.28%.
CONCLUSIONS: Our proposed model showed significant improvements in hand-gesture recognition accuracy in individuals who have had a stroke as compared with conventional transfer learning, neural networks and traditional machine learning approaches. In addition, K-Best feature selection and increased window size can further improve the accuracy. This approach could help to alleviate the impact of physiological differences and create a subject-independent model for stroke survivors that improves the classification accuracy of wearable sensors.
BACKGROUND: The study was registered in Chinese Clinical Trial Registry with registration number CHiCTR1800017568 in 2018/08/04.
摘要：
背景：家庭康复系统很有前途，中风幸存者常规治疗的潜在替代方法。不幸的是,参与者之间的生理差异和可穿戴传感器中的传感器位移对分类器性能构成了重大挑战，特别是对于反复进行试验时可能遇到困难的卒中患者.这使得创建能够准确分类手势的可靠的家庭康复系统具有挑战性。
方法：20名中风患者进行了7种不同的手势（质量屈曲，质量扩展,手腕屈指,手腕背屈,前臂旋前,前臂旋回,和休息)与日常生活活动有关。他们在前臂上戴着EMG传感器时做出了这些手势，以及FMG传感器和手腕上的IMU。我们开发了一个基于原型网络的一次性迁移学习模型，K-Best特征选择,并增加窗口大小以提高模型精度。我们的模型与传统的神经网络迁移学习进行了评估，以及与主题相关和与主题无关的分类器：神经网络，LGBM,LDA,和SVM。
结果：我们提出的模型实现了82.2%的手势分类准确率，（P<0.05）优于神经网络的一次性迁移学习（63.17％），神经网络(59.72%)，LGBM（65.09%），LDA（63.35%），和SVM(54.5%)。此外,我们的模型与主题相关分类器的性能相似，略低于SVM(83.84%)，但高于神经网络(81.62%)，LGBM（80.79%），和LDA（74.89%）。使用K-Best特征提高了用于评估的6个分类器中的3个的准确性，而不影响其他分类器的准确性。增加窗口大小使所有分类器的准确度平均提高了4.28%。
结论：我们提出的模型显示，与传统迁移学习相比，中风患者的手势识别准确性有了显著提高。神经网络和传统的机器学习方法。此外,K-Best特征选择和增加的窗口大小可以进一步提高精度。这种方法可以帮助减轻生理差异的影响，并为中风幸存者创建独立于受试者的模型，从而提高可穿戴传感器的分类精度。
背景：该研究于2018/08/04在中国临床试验注册中心注册，注册号为CHiCTR1800017568。
方法：20名中风患者进行了7种不同的手势（质量屈曲，质量扩展,手腕屈指,手腕背屈,前臂旋前,前臂旋回,和休息)与日常生活活动有关。他们在前臂上戴着EMG传感器时做出了这些手势，以及FMG传感器和手腕上的IMU。我们开发了一个基于原型网络的一次性迁移学习模型，K-Best特征选择,并增加窗口大小以提高模型精度。我们的模型与传统的神经网络迁移学习进行了评估，以及与主题相关和与主题无关的分类器：神经网络，LGBM,LDA,和SVM。
结果：我们提出的模型实现了82.2%的手势分类准确率，（P<0.05）优于神经网络的一次性迁移学习（63.17％），神经网络(59.72%)，LGBM（65.09%），LDA（63.35%），和SVM(54.5%)。此外,我们的模型与主题相关分类器的性能相似，略低于SVM(83.84%)，但高于神经网络(81.62%)，LGBM（80.79%），和LDA（74.89%）。使用K-Best特征提高了用于评估的6个分类器中的3个的准确性，而不影响其他分类器的准确性。增加窗口大小使所有分类器的准确度平均提高了4.28%。
结论：我们提出的模型显示，与传统迁移学习相比，中风患者的手势识别准确性有了显著提高。神经网络和传统的机器学习方法。此外,K-Best特征选择和增加的窗口大小可以进一步提高精度。这种方法可以帮助减轻生理差异的影响，并为中风幸存者创建独立于受试者的模型，从而提高可穿戴传感器的分类精度。
背景：该研究于2018/08/04在中国临床试验注册中心注册，注册号为CHiCTR1800017568。
