Smartphone sensor-based Human Activity Recognition (HAR) is a significant facilitator for many real-world applications such as smart homes, personal healthcare, and illness detection because it has the potential to discover activity patterns in daily life. While solutions for detecting sequential activities like walking or running are already matured, however recognizing additional forms of complex, concurrent, interleaved, and heterogeneous human activities remains a research challenge. Furthermore, several solutions have been developed for specific types of activity complexity. According to the best of our knowledge, there is no common solution for detecting all types of action-based activities. In this paper, we address the problem of recognizing all types of action-based human activities i.e. sequential, complex, concurrent, interleaved, and heterogeneous activities. The intention behind this paper is to present a generic methodology for HAR by leveraging transfer learning and evaluating the performance on a plethora of HAR datasets. In this regard, we have proposed a model called RecurrentHAR, which stands for Recurrent layers for HAR, a novel adversarial knowledge transfer approach that uses the Gated Recurrent Units (GRUs) architecture for smartphone sensor-based HAR. The performance of the proposed model is evaluated through extensive experiments using three public datasets namely WISDM (sequential activities), PAMAP2 (complex, concurrent, and interleaved activities), and KU-HAR (Heterogeneous activities), and the RecurrentHAR model has outperformed compared with other state-of-the-art approaches. The proposed model achieved 96.26%, 94.77%, and 98.98% of F1-score for WISDM, PAMAP2, and KU-HAR datasets, respectively. The experimental results provide insight into the applicability of the proposed model and future research possibilities.

基于智能手机传感器的人体活动识别 (HAR) 是智能家居、个人医疗保健和疾病检测等许多现实应用的重要促进者，因为它有可能发现日常生活中的活动模式。虽然检测步行或跑步等连续活动的解决方案已经成熟，但识别其他形式的复杂、并发、交错和异构人类活动仍然是一个研究挑战。此外，针对特定类型的活动复杂性开发了多种解决方案。据我们所知，没有通用的解决方案可以检测所有类型的基于操作的活动。在本文中，我们解决了识别所有类型的基于行动的人类活动的问题，即顺序的、复杂的、并发的、交错的和异构的活动。本文的目的是通过利用迁移学习并评估大量 HAR 数据集的性能，提出一种 HAR 通用方法。在这方面，我们提出了一种名为 RecurrentHAR 的模型，它代表 HAR 的循环层，这是一种新颖的对抗性知识转移方法，它使用基于智能手机传感器的 HAR 的门控循环单元 (GRU) 架构。所提出模型的性能通过使用三个公共数据集 WISDM（顺序活动）、PAMAP2（复杂、并发和交错活动）和 KU-HAR（异构活动）进行大量实验进行评估，并且 RecurrentHAR 模型的性能优于其他最先进的方法。所提出的模型达到了 96。WISDM、PAMAP2 和 KU-HAR 数据集的 F1 分数分别为 26%、94.77% 和 98.98%。实验结果提供了对所提出模型的适用性和未来研究可能性的见解。