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User-adaptive models for activity and emotion recognition using deep transfer learning and data augmentation

机译：使用深度传输学习和数据增强的活动和情感识别的用户 - 自适应模型

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摘要

Building predictive models for human-interactive systems is a challenging task. Every individual has unique characteristics and behaviors. A generic human-machine system will not perform equally well for each user given the between-user differences. Alternatively, a system built specifically for each particular user will perform closer to the optimum. However, such a system would require more training data for every specific user, thus hindering its applicability for real-world scenarios. Collecting training data can be time consuming and expensive. For example, in clinical applications it can take weeks or months until enough data is collected to start training machine learning models. End users expect to start receiving quality feedback from a given system as soon as possible without having to rely on time consuming calibration and training procedures. In this work, we build and test user-adaptive models (UAM) which are predictive models that adapt to each users' characteristics and behaviors with reduced training data. Our UAM are trained using deep transfer learning and data augmentation and were tested on two public datasets. The first one is an activity recognition dataset from accelerometer data. The second one is an emotion recognition dataset from speech recordings. Our results show that the UAM have a significant increase in recognition performance with reduced training data with respect to a general model. Furthermore, we show that individual characteristics such as gender can influence the models' performance.
机译：建立人类交互系统的预测模型是一个具有挑战性的任务。每个人都有独特的特征和行为。在用户之间的差异，每个用户，通用人机系统不会同样对其执行。或者，专为每个特定用户而构建的系统将更接近最佳。然而，这种系统需要更多特定用户的培训数据，从而阻碍了其对现实世界场景的适用性。收集培训数据可能是耗时和昂贵的。例如，在临床应用中，它可能需要数周或几个月，直到收集足够的数据来开始训练机器学习模型。最终用户希望尽快开始从给定系统接收质量反馈，而无需依赖耗时的校准和培训程序。在这项工作中，我们构建和测试用户 - 自适应模型（UAM），其是适应每个用户的特征和行为的预测模型，其具有减少的训练数据。我们的UAM使用深度传输学习和数据增强培训，并在两个公共数据集上进行了测试。第一个是来自加速度计数据的活动识别数据集。第二个是语音记录的情感识别数据集。我们的结果表明，UAM对综合模型的培训数据降低了识别性能显着增加。此外，我们表明，性别等个人特征可以影响模型的性能。

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