A person's movement or relative positioning effectively generates raw electrical signals that can be read by computing machines to apply various manipulative techniques for the classification of different human activities. In this paper, a stratified multi-structural approach based on a Residual network ensembled with Residual MobileNet is proposed, termed as FusionActNet. The proposed method involves using carefully designed Residual blocks for classifying the static and dynamic activities separately because they have clear and distinct characteristics that set them apart. These networks are trained independently, resulting in two specialized and highly accurate models. These models excel at recognizing activities within a specific superclass by taking advantage of the unique algorithmic benefits of architectural adjustments. Afterward, these two ResNets are passed through a weighted ensemble-based Residual MobileNet. Subsequently, this ensemble proficiently discriminates between a specific static and a specific dynamic activity, which were previously identified based on their distinct feature characteristics in the earlier stage. The proposed model is evaluated using two publicly accessible datasets; namely, UCI HAR and Motion-Sense. Therein, it successfully handled the highly confusing cases of data overlap. Therefore, the proposed approach achieves a state-of-the-art accuracy of 96.71% and 95.35% in the UCI HAR and Motion-Sense datasets respectively.

翻译：人体运动或相对定位有效生成原始电信号，可通过计算设备读取并运用多种操控技术进行分类。本文提出一种基于残差网络与残差MobileNet集成的分层多结构方法，命名为FusionActNet。该方法采用精心设计的残差模块分别对静态与动态活动进行分类，因其具有截然不同的特征属性。两类网络独立训练形成两个专用高精度模型，通过架构调整的独特算法优势，在各自超类内实现卓越的活动识别。随后，两个残差网络通过加权集成残差MobileNet融合，该集成模型能精准区分前期基于特征差异识别的特定静态与动态活动。采用UCI HAR与Motion-Sense两个公开数据集评估模型，成功处理了高度混淆的数据重叠情况。所提方法在UCI HAR和Motion-Sense数据集上分别取得96.71%与95.35%的先进准确率。