Human Activity Recognition from RGB Video Streams Using 1D-CNNs

Srimath, Sivanvita; Yang, Yunliu; Sarker, Krishanu; Sunderraman, Rajshekhar; Ji, Shihao

doi:10.1109/swc50871.2021.00048

Cited by 2 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, joint angles can be obtained starting from body joint positions ( Jegham et al, 2020 ). Notably, in the context of AAL, it is common practice to store and analyze the skeleton tracking data instead of RGB or depth data to prioritize privacy preservation ( Gasparrini et al, 2014 ; Tu et al, 2018 ; Li and Sun, 2021 ; Srimath et al, 2021 ). The pre-processing stage aims at noise reduction, data normalization and segmentation.…”

Section: Har Processing Chainmentioning

confidence: 99%

Ambient assisted living for frail people through human activity recognition: state-of-the-art, challenges and future directions

Guerra,

Torti,

Marenzi

et al. 2023

Front. Neurosci.

View full text Add to dashboard Cite

Ambient Assisted Living is a concept that focuses on using technology to support and enhance the quality of life and well-being of frail or elderly individuals in both indoor and outdoor environments. It aims at empowering individuals to maintain their independence and autonomy while ensuring their safety and providing assistance when needed. Human Activity Recognition is widely regarded as the most popular methodology within the field of Ambient Assisted Living. Human Activity Recognition involves automatically detecting and classifying the activities performed by individuals using sensor-based systems. Researchers have employed various methodologies, utilizing wearable and/or non-wearable sensors, and employing algorithms ranging from simple threshold-based techniques to more advanced deep learning approaches. In this review, literature from the past decade is critically examined, specifically exploring the technological aspects of Human Activity Recognition in Ambient Assisted Living. An exhaustive analysis of the methodologies adopted, highlighting their strengths and weaknesses is provided. Finally, challenges encountered in the field of Human Activity Recognition for Ambient Assisted Living are thoroughly discussed. These challenges encompass issues related to data collection, model training, real-time performance, generalizability, and user acceptance. Miniaturization, unobtrusiveness, energy harvesting and communication efficiency will be the crucial factors for new wearable solutions.

show abstract