Recognizing human activities seamlessly and ubiquitously is now closer than ever given the myriad of sensors readily deployed on and around users. However, the training of recognition systems continues to be both time and resource-consuming, as datasets must be collected ad-hoc for each specific sensor setup a person may encounter in their daily life. This work presents an alternate approach based on transfer learning to opportunistically train new unseen or target sensor systems from existing or source sensor systems. The approach uses system identification techniques to learn a mapping function that automatically translates the signals from the source sensor domain to the target sensor domain, and vice versa. This can be done for sensor signals of the same or cross modality. Two transfer models are proposed to translate recognition systems based on either activity templates or activity models, depending on the characteristics of both source and target sensor systems. The proposed transfer methods are evaluated in a human–computer interaction scenario, where the transfer is performed in between wearable sensors placed at different body locations, and in between wearable sensors and an ambient depth camera sensor. Results show that a good transfer is possible with just a few seconds of data, irrespective of the direction of the transfer and for similar and cross sensor modalities.

鉴于无数传感器可以轻松地部署在用户及其周围，因此，无缝和无处不在的人类活动识别现在比以往任何时候都更加紧密。但是，识别系统的培训仍然既耗时又耗费资源，因为必须针对一个人在日常生活中可能遇到的每个特定传感器设置临时收集数据集。这项工作提出了一种基于转移学习的替代方法，以机会主义地从现有或源传感器系统中训练新的看不见的或目标的传感器系统。该方法使用系统识别技术来学习映射功能，该功能会自动将信号从源传感器域转换为目标传感器域，反之亦然。可以对相同或交叉模态的传感器信号执行此操作。根据源和目标传感器系统的特征，提出了两种传递模型来转换基于活动模板或活动模型的识别系统。在人机交互场景中评估了建议的传输方法，其中在放置于不同身体位置的可穿戴传感器之间以及在可穿戴传感器与环境深度相机传感器之间进行传输。结果表明，只需几秒钟的数据即可实现良好的传输，而与传输方向以及类似和交叉传感器的方式无关。其中在放置在不同身体位置的可穿戴传感器之间以及在可穿戴传感器和环境深度相机传感器之间执行转换。结果表明，只需几秒钟的数据即可实现良好的传输，而与传输方向以及类似和交叉传感器的方式无关。其中在放置在不同身体位置的可穿戴传感器之间以及在可穿戴传感器和环境深度相机传感器之间执行转换。结果表明，只需几秒钟的数据即可实现良好的传输，而与传输方向以及类似和交叉传感器的方式无关。