Continuous frame motion sensitive self-supervised collaborative network for video representation learning

Bi, Shuai; Hu, Zhengping; Zhao, Mengyao; Zhang, Hehao; Di, Jirui; Sun, Zhe

doi:10.1016/j.aei.2023.101941

Cited by 5 publications

(1 citation statement)

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“…Sensor-based methods [25,26] through discrete event analysis represent a valuable alternative that can be advantageous because it is not perceived as intrusive [27]. These methods provide information about the environment and home conditions at the time the activity takes place [28] or about the person's postural parameters [29,30].…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Method to Recognise Human Activity at Home Using Non-Intrusive Sensors

Gómez-Ramos,

Duque-Domingo,

Zalama

et al. 2023

Electronics

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As people get older, living at home can expose them to potentially dangerous situations when performing everyday actions or simple tasks due to physical, sensory or cognitive limitations. This could compromise the residents’ health, a risk that in many cases could be reduced by early detection of the incidents. The present work focuses on the development of a system capable of detecting in real time the main activities of daily life that one or several people can perform at the same time inside their home. The proposed approach corresponds to an unsupervised learning method, which has a number of advantages, such as facilitating future replication or improving control and knowledge of the internal workings of the system. The final objective of this system is to facilitate the implementation of this method in a larger number of homes. The system is able to analyse the events provided by a network of non-intrusive sensors and the locations of the residents inside the home through a Bluetooth beacon network. The method is built upon an accurate combination of two hidden Markov models: one providing the rooms in which the residents are located and the other providing the activity the residents are carrying out. The method has been tested with the data provided by the public database SDHAR-HOME, providing accuracy results ranging from 86.78% to 91.68%. The approach presents an improvement over existing unsupervised learning methods as it is replicable for multiple users at the same time.

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