Department of Rehabilitation Medical Engineering, Daegu Haany University, Gyeongsan-si 38610, Republic of Korea.
Department of Geriatric Rehabilitation, Daegu Haany University, Gyeongsan-si 38610, Republic of Korea.
Sensors (Basel). 2023 Jan 6;23(2):683. doi: 10.3390/s23020683.
The study of human activity recognition concentrates on classifying human activities and the inference of human behavior using modern sensing technology. However, the issue of domain adaptation for inertial sensing-based human activity recognition (HAR) is still burdensome. The existing requirement of labeled training data for adapting such classifiers to every new person, device, or on-body location is a significant barrier to the widespread adoption of HAR-based applications, making this a challenge of high practical importance. We propose the semi-supervised HAR method to improve reconstruction and generation. It executes proper adaptation with unlabeled data without changes to a pre-trained HAR classifier. Our approach decouples VAE with adversarial learning to ensure robust classifier operation, without newly labeled training data, under changes to the individual activity and the on-body sensor position. Our proposed framework shows the empirical results using the publicly available benchmark dataset compared to state-of-art baselines, achieving competitive improvement for handling new and unlabeled activity. The result demonstrates SAA has achieved a 5% improvement in classification score compared to the existing HAR platform.
人体活动识别的研究集中在使用现代传感技术对人体活动进行分类和推断人类行为。然而，基于惯性传感器的人体活动识别（HAR）的领域自适应问题仍然很棘手。现有的要求是为每个新的人、设备或佩戴位置对这些分类器进行标记训练数据的适应，这是 HAR 应用广泛采用的一个重大障碍，这是一个具有重要实际意义的挑战。我们提出了半监督 HAR 方法来改进重建和生成。它可以在不对 HAR 分类器进行预训练的情况下，使用未标记的数据进行适当的自适应。我们的方法通过对抗性学习将 VAE 分离，以确保在个体活动和佩戴传感器位置发生变化时，分类器能够在没有新的标记训练数据的情况下稳健运行。与现有的基准线相比，我们提出的框架使用公开的基准数据集展示了实证结果，在处理新的和未标记的活动方面取得了有竞争力的改进。结果表明，与现有的 HAR 平台相比，SAA 的分类得分提高了 5%。
