A survey of activity recognition in egocentric lifelogging datasets

Asnaoui, Khalid El; Asayesh, Hamid; Aksasse, Brahim; Ouanan, Mohammed

doi:10.1109/wits.2017.7934659

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Other wearable sensors used in HAR include (but are not limited to) piezoelectric sensor [66,67] for converting changes in pressure, acceleration, temperature, strain, or force to electrical charge, barometric pressure sensor [68] for atmospheric pressure, temperature measurement [69], electroencephalography (EEG) for measuring brain activity [70], respiration sensors for breathing monitoring [71], ultraviolet (UV) sensors [72] for sun exposure assessment, GPS for location sensing, microphones for audio recording [39,73,74], and wearable cameras for image or video recording [55]. It is also important to note that the wearable camera market has drastically grown with cameras such as GoPro becoming mainstream [75][76][77][78] over the last few years. However, due to privacy concerns posed by participants related to video recording, utilizing wearable cameras for longitudinal activity recognition is not as prevalent as other sensors.…”

Section: Mechanomyography (Mmg)mentioning

confidence: 99%

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Zhang

et al. 2022

Sensors

216

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Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human–computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR.

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Section: Mechanomyography (Mmg)mentioning

confidence: 99%

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Zhang

et al. 2022

Sensors

216

View full text Add to dashboard Cite

show abstract

“…It is also important to note that the wearable camera market has drastically grown with cameras such as GoPro becoming mainstream [9,10,81,164] over the last few years. However, due to privacy concerns posed by participants related to video recording, utilizing wearable cameras for longitudinal activity recognition is not as prevalent as other sensors.…”

Section: Mechanomyography (Mmg)mentioning

confidence: 99%

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Zhang¹,

Li²,

Zhang³

et al. 2021

Preprint

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Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human-computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR.

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“…The survey paper presented in [17] focused on the analysis of hands from the egocentric vision perspective. Egocentric activity recognition in life-logging datasets has been reviewed in [18]. The authors of [19] have studied summarization of egocentric videos.…”

Section: Forecastingmentioning

confidence: 99%

Predicting the Future from First Person (Egocentric) Vision: A Survey

Rodin¹,

Furnari²,

Mavroedis³

et al. 2021

Preprint

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Egocentric videos can bring a lot of information about how humans perceive the world and interact with the environment, which can be beneficial for the analysis of human behaviour. The research in egocentric video analysis is developing rapidly thanks to the increasing availability of wearable devices and the opportunities offered by new large-scale egocentric datasets. As computer vision techniques continue to develop at an increasing pace, the tasks related to the prediction of future are starting to evolve from the need of understanding the present. Predicting future human activities, trajectories and interactions with objects is crucial in applications such as human-robot interaction, assistive wearable technologies for both industrial and daily living scenarios, entertainment and virtual or augmented reality. This survey summarises the evolution of studies in the context of future prediction from egocentric vision making an overview of applications, devices, existing problems, commonly used datasets, models and input modalities. Our analysis highlights that methods for future prediction from egocentric vision can have a significant impact in a range of applications and that further research efforts should be devoted to the standardisation of tasks and the proposal of datasets considering real-world scenarios such as the ones with an industrial vocation.

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A survey of activity recognition in egocentric lifelogging datasets

Cited by 6 publications

References 17 publications

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Predicting the Future from First Person (Egocentric) Vision: A Survey

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