Continual Learning for Activity Recognition

Sah, Ramesh Kumar; Mirzadeh, Seyed Iman; Ghasemzadeh, Hassan

doi:10.1109/embc48229.2022.9871690

Cited by 2 publications

(3 citation statements)

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“…Sah et al [ 107 ] investigated the performance of three recent continual learning approaches (A-GEM [ 121 ], ER-Ring [ 122 ], and MC-SGD [ 123 ]) for addressing the catastrophic forgetting in wearable sensors for activity recognition. In a wearable sensor system, it is necessary to have the capability to monitor and recognize activities across users.…”

Section: Advances In Continual Learning Methods For Time Series Modelingmentioning

confidence: 99%

“…This survey will follow the taxonomy of Lange et al [17] and Mundt et al [103]. [93,94,[106][107][108][109][110][111][112][113][114][115][116] (note: if a paper uses two methods separately with similar satisfactory results, the paper will be listed under both groups).…”

Section: Advances In Continual Learning Methods For Time Series Modelingmentioning

confidence: 99%

“… Tree diagram of the taxonomy of the continual learning methods for sensor time series classification and forecasting [ 93 , 94 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 ] (note: if a paper uses two methods separately with similar satisfactory results, the paper will be listed under both groups). …”

Section: Figurementioning

confidence: 99%

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Continual Deep Learning for Time Series Modeling

Ao¹,

Fayek

2023

Sensors

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The multi-layer structures of Deep Learning facilitate the processing of higher-level abstractions from data, thus leading to improved generalization and widespread applications in diverse domains with various types of data. Each domain and data type presents its own set of challenges. Real-world time series data may have a non-stationary data distribution that may lead to Deep Learning models facing the problem of catastrophic forgetting, with the abrupt loss of previously learned knowledge. Continual learning is a paradigm of machine learning to handle situations when the stationarity of the datasets may no longer be true or required. This paper presents a systematic review of the recent Deep Learning applications of sensor time series, the need for advanced preprocessing techniques for some sensor environments, as well as the summaries of how to deploy Deep Learning in time series modeling while alleviating catastrophic forgetting with continual learning methods. The selected case studies cover a wide collection of various sensor time series applications and can illustrate how to deploy tailor-made Deep Learning, advanced preprocessing techniques, and continual learning algorithms from practical, real-world application aspects.

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Section: Advances In Continual Learning Methods For Time Series Modelingmentioning

confidence: 99%