Vectors of temporally correlated snippets for temporal action detection

Murtaza, Fiza; Yousaf, Muhammad Haroon; Velastín, Sergio A.; Yu, Qian

doi:10.1016/j.compeleceng.2020.106654

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…In this regard, lots of AR methodologies have been developed and implemented in the past few years using different sensing modalities. Various researchers developed camerabased activity detection/recognition systems for identifying the activities of interest [5][6][7][8]. However, camera-based AR approaches are subjected to privacy constraints.…”

Section: Introductionmentioning

confidence: 99%

Daily Living Activity Recognition In-The-Wild: Modeling and Inferring Activity-Aware Human Contexts

et al. 2022

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Advancement in smart sensing and computing technologies has provided a dynamic opportunity to develop intelligent systems for human activity monitoring and thus assisted living. Consequently, many researchers have put their efforts into implementing sensor-based activity recognition systems. However, recognizing people’s natural behavior and physical activities with diverse contexts is still a challenging problem because human physical activities are often distracted by changes in their surroundings/environments. Therefore, in addition to physical activity recognition, it is also vital to model and infer the user’s context information to realize human-environment interactions in a better way. Therefore, this research paper proposes a new idea for activity recognition in-the-wild, which entails modeling and identifying detailed human contexts (such as human activities, behavioral environments, and phone states) using portable accelerometer sensors. The proposed scheme offers a detailed/fine-grained representation of natural human activities with contexts, which is crucial for modeling human-environment interactions in context-aware applications/systems effectively. The proposed idea is validated using a series of experiments, and it achieved an average balanced accuracy of 89.43%, which proves its effectiveness.

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Section: Introductionmentioning

confidence: 99%