Recognition of Complex Human Activities for Wellness Management from Smartwatch using Deep Residual Neural Network
Sakorn Mekruksavanich, Ponnipa Jantawong, Anuchit Jitpattanakul · 2022 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON) · 2022
Activity-based wellbeing administration integrating health apps on intelligent wearables is becoming increasingly prominent. The accessibility of multimodal sensors in everyday wearable devices makes it feasible to measure human behavior and provide wellness services that are aware of the user's environment. In smartphones, smartwatches, and many other wearables, integrated sensors provide a wealth of information that can be utilized to recognize human movement. This research shows how effective deep learning algorithms can extract abstract information for human movement categorization in daily life. We came up with an approach to deal with this issue is what we name one-dimensional deep residual neural network (1D-ResNet). With an overall accuracy of 94.15%, we observed that the proposed 1D-ResNet outperforms standard deep learning models in categorizing complicated human activities such as reading, writing, eating, and other hand-oriented tasks.