Recognizing human activity plays a significant role in the advancements of human-interaction applications in healthcare, personal fitness, and smart devices. Many papers presented various techniques for human activity representation that resulted in distinguishable progress. In this study, we conduct an extensive literature review on recent, top-performing techniques in human activity recognition based on wearable sensors. Due to the lack of standardized evaluation and to assess and ensure a fair comparison between the state-of-the-art techniques, we applied a standardized evaluation benchmark on the state-of-the-art techniques using six publicly available data-sets: MHealth, USCHAD, UTD-MHAD, WISDM, WHARF, and OPPORTUNITY. Also, we propose an experimental, improved approach that is a hybrid of enhanced handcrafted features and a neural network architecture which outperformed top-performing techniques with the same standardized evaluation benchmark applied concerning MHealth, USCHAD, UTD-MHAD data-sets.
翻译:认识到人类活动在保健、个人健康和智能设备方面的人类相互作用应用的进步中起着重要作用。许多论文介绍了人类活动代表的各种技术,取得了显著的进展。在本研究中,我们广泛研究了基于可磨损传感器的人类活动认识方面最近最优秀的技术。由于缺乏标准化的评价,并且为了评估和确保对最新技术进行公平的比较,我们利用六个公开的数据集,即:MHealth、USCHAD、UTD-MHAD、WISDM、WHARF和机会,对最新先进技术进行了标准化评价基准。我们还提出了一个实验性、改进的方法,这是一种强化手工艺特征的混合体和神经网络结构,该结构优于最佳技术,在MHealth、USCHAD、UTD-MHAD数据集方面采用了同样的标准化评价基准。