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A person's movement or relative positioning effectively generates rawelectrical signals that can be read by computing machines to apply variousmanipulative techniques for the classification of different human activities.In this paper, a stratified multi-structural approach based on a Residualnetwork ensembled with Residual MobileNet is proposed, termed as FusionActNet.The proposed method involves using carefully designed Residual blocks forclassifying the static and dynamic activities separately because they haveclear and distinct characteristics that set them apart. These networks aretrained independently, resulting in two specialized and highly accurate models.These models excel at recognizing activities within a specific superclass bytaking advantage of the unique algorithmic benefits of architecturaladjustments. Afterward, these two ResNets are passed through a weightedensemble-based Residual MobileNet. Subsequently, this ensemble proficientlydiscriminates between a specific static and a specific dynamic activity, whichwere previously identified based on their distinct feature characteristics inthe earlier stage. The proposed model is evaluated using two publiclyaccessible datasets; namely, UCI HAR and Motion-Sense. Therein, it successfullyhandled the highly confusing cases of data overlap. Therefore, the proposedapproach achieves a state-of-the-art accuracy of 96.71% and 95.35% in the UCIHAR and Motion-Sense datasets respectively.

Decoding Human Activities: Analyzing Wearable Accelerometer and Gyroscope Data for Activity Recognition