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Radar-based recognition of human activities of daily living has been a focus of research for over a decade. Current techniques focus on generalized motion recognition of any person and rely on massive amounts of data to characterize generic human activity. However, human gait is actually a person-specific biometric, correlated with health and agility, which depends on a person’s mobility ethogram. This paper proposes a multi-input multi-task deep learning framework for jointly learning a person’s agility and activity. As a proof of concept, we consider three categories of agility represented by slow, fast and nominal motion articulations and show that joint consideration of agility and activity can lead to improved activity classification accuracy and estimation of agility. To the best of our knowledge, this work represents the first work considering personalized motion recognition and agility characterization using radar.
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RF sensing of personalized mobility: accounting for temporal variability in ethogram-based classification
Human activity recognition (HAR) with radar-based technologies has become a popular research area in the past decade. However, the objective of these studies are often to classify human activity for anyone; thus, models are trained using data spanning as broad a swath of people and mobility profiles as possible. In contrast, applications of HAR and gait analysis to remote health monitoring require characterization of the person-specific qualities of a person’s activities and gait, which greatly depends on age, health and agility. In fact, the speed or agility with which a person moves can be an important health indicator. In this study, we propose a multi-input multi-task deep learning framework to simultaneously learn a person’s activity and agility. In this initial study, we consider three different agility states: slow, nominal, and fast. It is shown that joint learning of agility and activity improves the classification accuracy for both activity and agility recognition tasks. To the best of our knowledge, this study is the first work considering both agility characterization and personalized activity recognition using RF sensing.
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- PAR ID:
- 10523221
- Editor(s):
- Hedden, Abigail S; Mazzaro, Gregory J
- Publisher / Repository:
- SPIE
- Date Published:
- ISBN:
- 9781510674141
- Page Range / eLocation ID:
- 30
- Subject(s) / Keyword(s):
- deep learning radar human activity recognition micro-doppler spectrograms
- Format(s):
- Medium: X
- Location:
- National Harbor, United States
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1117/12.3013747
