Position independent activity recognition using shallow neural architecture and empirical modeling

Saha, Swapnil Sayan; Rahman, Shafizur; Haque, Zarin Rezwana Ridita; Hossain, Tahera; Inoue, Sozo; Ahad, Md. Atiqur Rahman

doi:10.1145/3341162.3345572

Cited by 8 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The classifier for activity recognition is implemented as a fully-connected neural network with two hidden layers, which is based on the recent studies on similar types of data [42], [56], [35].…”

Section: Implementation and Parameter Tuningmentioning

confidence: 99%

Continual Activity Recognition with Generative Adversarial Networks

Nakwijit

Schiemer

et al. 2021

ACM Trans. Internet Things

View full text Add to dashboard Cite

Continual learning is an emerging research challenge in human activity recognition (HAR). As an increasing number of HAR applications are deployed in real-world environments, it is important and essential to extend the activity model to adapt to the change in people’s activity routine. Otherwise, HAR applications can become obsolete and fail to deliver activity-aware services. The existing research in HAR has focused on detecting abnormal sensor events or new activities, however, extending the activity model is currently under-explored. To directly tackle this challenge, we build on the recent advance in the area of lifelong machine learning and design a continual activity recognition system, called HAR-GAN , to grow the activity model over time. HAR-GAN does not require a prior knowledge on what new activity classes might be and it does not require to store historical data by leveraging the use of Generative Adversarial Networks (GAN) to generate sensor data on the previously learned activities. We have evaluated HAR-GAN on four third-party, public datasets collected on binary sensors and accelerometers. Our extensive empirical results demonstrate the effectiveness of HAR-GAN in continual activity recognition and shed insight on the future challenges.

show abstract

Section: Implementation and Parameter Tuningmentioning

confidence: 99%