An efficient sign language recognition (SLR) system would help speech and hearing-impaired people to communicate with normal people. This work aims to develop a SLR system for Indian sign language using data acquired from multichannel surface electromyogram, tri-axis accelerometers and tri-axis gyroscopes placed on both the forearms of signers. A novel ensemble-based transfer learning algorithm called Trbaggboost is proposed, which uses small amount of labeled data from a new subject along with labelled data from other subjects to train an ensemble of learners for predicting unlabeled data from the new subject. Conventional machine learning algorithms such as decision tree, support vector machine and random forest (RF) are used as base learners. The results for classification of signs using Trbaggboost are compared with commonly used transfer learning algorithms such as TrAdaboost, TrResampling, TrBagg, and simple bagging approach such as RF. Average accuracy for classification of signs performed by a new subject is achieved as 69.56% when RF is used without transfer learning. When just two observations of labeled data from a new subject are integrated with training data of an existing SLR system, average classification accuracy for TrAdaboost, TrResampling, TrBagg and RF are 71.07%, 72.92%, 76.10% and 76.79%, respectively. However, for the same number of labelled data from the new subject, Trbaggboost yields an average classification accuracy of 80.44%, indicating the effectiveness of the algorithm. Moreover, the classification accuracy for Trbaggboost improves up to 97.04% as the number of labelled data from the new user increase.

高效的手语识别（SLR）系统将帮助语音和听力受损的人与正常人进行交流。这项工作的目的是使用从多通道表面肌电图，三轴加速度计和三轴陀螺仪获取的数据开发印度手语的SLR系统，该信号放置在两手签名者的前臂上。一种基于整体的新型迁移学习算法 Trbaggboost 提出了一种建议，其使用来自新学科的少量标记数据以及来自其他学科的标记数据来训练一组学习者，以预测来自该新学科的未标记数据。常规的机器学习算法（例如决策树，支持向量机和随机森林（RF））用作基础学习器。将使用Trbaggboost对标志进行分类的结果与常用的转移学习算法（例如TrAdaboost，TrResampling，TrBagg）和简单的装袋方法（例如RF）进行了比较。当在不进行转移学习的情况下使用RF时，新对象对标志进行分类的平均准确度可达到69.56％。当仅将来自新主题的标记数据的两个观测值与现有SLR系统的训练数据集成在一起时，TrAdaboost的平均分类准确性，TrResampling，TrBagg和RF分别为71.07％，72.92％，76.10％和76.79％。但是，对于来自新主题的相同数量的标记数据，Trbaggboost产生的平均分类准确度为80.44％，表明该算法有效。此外，随着来自新用户的标记数据数量的增加，Trbaggboost的分类准确性提高了97.04％。