Knowledge transfer across sensing technology is a novel concept that has been recently explored in many application domains, including gesture-based human computer interaction. The main aim is to gather semantic or data driven information from a source technology to classify / recognize instances of unseen classes in the target technology. The primary challenge is the significant difference in dimensionality and distribution of feature sets between the source and the target technologies. In this paper, we propose TRANSFER, a generic framework for knowledge transfer between a source and a target technology. TRANSFER uses a language-based representation of a hand gesture, which captures a temporal combination of concepts such as handshape, location, and movement that are semantically related to the meaning of a word. By utilizing a pre-specified syntactic structure and tokenizer, TRANSFER segments a hand gesture into tokens and identifies individual components using a token recognizer. The tokenizer in this language-based recognition system abstracts the low-level technology-specific characteristics to the machine interface, enabling the design of a discriminator that learns technology-invariant features essential for recognition of gestures in both source and target technologies. We demonstrate the usage of TRANSFER for three different scenarios: a) transferring knowledge across technology by learning gesture models from video and recognizing gestures using WiFi, b) transferring knowledge from video to accelerometer, and d) transferring knowledge from accelerometer to WiFi signals.

翻译：摘要：跨传感技术的知识迁移是一个新颖概念，近期已在包括基于手势的人机交互在内的许多应用领域得到探索。其主要目标是从源技术中获取语义或数据驱动信息，以分类/识别目标技术中未见类别的实例。主要挑战在于源技术与目标技术之间特征集的维度与分布存在显著差异。本文提出TRANSFER——一种通用的源技术与目标技术间知识迁移框架。TRANSFER采用基于语言的手势表示方法，捕捉手型、位置和运动等与词语语义相关的概念在时间上的组合。通过利用预定义的句法结构和分词器，TRANSFER将手势分割为标记，并使用标记识别器识别各个组件。该基于语言的识别系统中的分词器将底层技术特定特征抽象为机器接口，从而设计出能够学习源目标技术中手势识别所需的技术不变特征的判别器。我们展示了TRANSFER在三种不同场景中的应用：a）通过从视频中学习手势模型并利用WiFi识别手势实现跨技术知识迁移；b）从视频到加速度计的知识迁移；以及d）从加速度计到WiFi信号的知识迁移。