Wearables are poised to transform health and wellness through automation of cost-effective, objective, and real-time health monitoring. However, machine learning models for these systems are designed based on labeled data collected, and feature representations engineered, in controlled environments. This approach has limited scalability of wearables because (i) collecting and labeling sufficiently large amounts of sensor data is a labor-intensive and expensive process; and (ii) wearables are deployed in highly dynamic environments of the end-users whose context undergoes consistent changes. We introduce TransNet , a deep learning framework that minimizes the costly process of data labeling, feature engineering, and algorithm retraining by constructing a scalable computational approach. TransNet learns general and reusable features in lower layers of the framework and quickly reconfigures the underlying models from a small number of labeled instances in a new domain, such as when the system is adopted by a new user or when a previously unseen event is to be added to event vocabulary of the system. Utilizing TransNet on four activity datasets, TransNet achieves an average accuracy of 88.1% in cross-subject learning scenarios using only one labeled instance for each activity class. This performance improves to an accuracy of 92.7% with five labeled instances.

中文翻译：

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跨网

可穿戴设备有望通过经济高效、客观和实时的健康监测自动化来改变健康状况。然而，这些系统的机器学习模型是基于在受控环境中收集的标记数据和设计的特征表示而设计的。这种方法对可穿戴设备的可扩展性有限，因为 (i) 收集和标记足够多的传感器数据是一个劳动密集型且成本高昂的过程；(ii) 可穿戴设备部署在终端用户的高度动态环境中，其上下文不断变化。我们介绍跨网，一个深度学习框架，通过构建可扩展的计算方法，最大限度地减少数据标记、特征工程和算法再训练的昂贵过程。TransNet 在框架的较低层学习通用和可重用的特征，并从新域中的少量标记实例中快速重新配置底层模型，例如当系统被新用户采用时或以前未见过的事件要发生时添加到系统的事件词汇表中。在四个活动数据集上使用 TransNet，TransNet 在跨学科学习场景中的平均准确率达到 88.1%，每个活动类别仅使用一个标记实例。使用五个标记实例，此性能提高到 92.7%。