Faculty of Information Science and Technology, Multimedia University, Malacca, Malaysia.
PLoS One. 2024 Aug 13;19(8):e0304655. doi: 10.1371/journal.pone.0304655. eCollection 2024.
Recognising human activities using smart devices has led to countless inventions in various domains like healthcare, security, sports, etc. Sensor-based human activity recognition (HAR), especially smartphone-based HAR, has become popular among the research community due to lightweight computation and user privacy protection. Deep learning models are the most preferred solutions in developing smartphone-based HAR as they can automatically capture salient and distinctive features from input signals and classify them into respective activity classes. However, in most cases, the architecture of these models needs to be deep and complex for better classification performance. Furthermore, training these models requires extensive computational resources. Hence, this research proposes a hybrid lightweight model that integrates an enhanced Temporal Convolutional Network (TCN) with Gated Recurrent Unit (GRU) layers for salient spatiotemporal feature extraction without tedious manual feature extraction. Essentially, dilations are incorporated into each convolutional kernel in the TCN-GRU model to extend the kernel's field of view without imposing additional model parameters. Moreover, fewer short filters are applied for each convolutional layer to alleviate excess parameters. Despite reducing computational cost, the proposed model utilises dilations, residual connections, and GRU layers for longer-term time dependency modelling by retaining longer implicit features of the input inertial sequences throughout training to provide sufficient information for future prediction. The performance of the TCN-GRU model is verified on two benchmark smartphone-based HAR databases, i.e., UCI HAR and UniMiB SHAR. The model attains promising accuracy in recognising human activities with 97.25% on UCI HAR and 93.51% on UniMiB SHAR. Since the current study exclusively works on the inertial signals captured by smartphones, future studies will explore the generalisation of the proposed TCN-GRU across diverse datasets, including various sensor types, to ensure its adaptability across different applications.
使用智能设备识别人类活动在医疗、安全、体育等各个领域催生了无数发明。基于传感器的人体活动识别（HAR），特别是基于智能手机的 HAR，由于轻量级计算和用户隐私保护，在研究界变得流行。深度学习模型是开发基于智能手机的 HAR 的最受欢迎的解决方案，因为它们可以自动从输入信号中捕获显著和独特的特征，并将它们分类到各自的活动类别中。然而，在大多数情况下，为了获得更好的分类性能，这些模型的架构需要很深很复杂。此外，训练这些模型需要大量的计算资源。因此，本研究提出了一种混合的轻量级模型，该模型将增强的时间卷积网络（TCN）与门控循环单元（GRU）层集成在一起，用于提取显著的时空特征，而无需繁琐的手动特征提取。本质上，在 TCN-GRU 模型中的每个卷积核中都加入了扩张，以在不增加额外模型参数的情况下扩展核的视野。此外，对于每个卷积层，应用较少的短滤波器来减轻过多的参数。尽管降低了计算成本，但所提出的模型利用扩张、残差连接和 GRU 层来进行更长时间的时间依赖性建模，通过在整个训练过程中保留输入惯性序列的更长隐式特征，为未来预测提供足够的信息。在两个基于智能手机的 HAR 基准数据库，即 UCI HAR 和 UniMiB SHAR 上验证了 TCN-GRU 模型的性能。该模型在 UCI HAR 上达到了 97.25%的准确率，在 UniMiB SHAR 上达到了 93.51%的准确率。由于本研究仅在智能手机捕获的惯性信号上进行，因此未来的研究将探索所提出的 TCN-GRU 在不同数据集（包括各种传感器类型）中的推广，以确保其在不同应用中的适应性。
