ArticleSensors (Basel, Switzerland)2021
A Novel Hybrid Deep Learning Model for Human Activity Recognition Based on Transitional Activities.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Sensor Modality Versus Pretraining for Recognising Bed-Exit-Relevant Postural Transitions: A Gyroscope-Focused Feasibility Study on Public Wearable-IMU Data.Sensors (Basel, Switzerland) · 2026Article
- Telemedicine to Improve Medical Care of Fishermen in Pelagic Fisheries.Healthcare (Basel, Switzerland) · 2025Review
- Classification of User Behavior Patterns for Indoor Navigation Problem.Sensors (Basel, Switzerland) · 2025Article
- A two-phase hybrid clustering framework exploring transitional activities in HAR.Discover artificial intelligence · 2025Article
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Authors and funding
5 authors.
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Abstract
In recent years, a plethora of algorithms have been devised for efficient human activity recognition. Most of these algorithms consider basic human activities and neglect postural transitions because of their subsidiary occurrence and short duration. However, postural transitions assume a significant part in the enforcement of an activity recognition framework and cannot be neglected. This work proposes a hybrid multi-model activity recognition approach that employs basic and transition activities by utilizing multiple deep learning models simultaneously. For final classification, a dynamic decision fusion module is introduced. The experiments are performed on the publicly available datasets. The proposed approach achieved a classification accuracy of 96.11% and 98.38% for the transition and basic activities, respectively. The outcomes show that the proposed method is superior to the state-of-the-art methods in terms of accuracy and precision.
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