Human action recognition using wearable sensors and neural networks
Abstract
Accurate recognition of daily activities could be useful in many fields including health, sports, childcare, and homes for the elderly, etc. In this paper, we propose a human action recognition method using data acquired from wearable sensors and learned using a Neural Network. The data collected from the sensors is processed for features using the Akamatsu transform. The Akamatsu Transform is a signal processing technique that given point, P(i) in a signal, N data points before and after the selected point are used to derive the integral and differential transforms, The Akamatsu Integration is an average of the N data points while the differential is the difference between the integral and the original value. Recently, wearable sensors are emerging as an indispensable method to recognize human actions.
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Human action recognition utilising wearable sensors and a neural network based on the Akamatsu Transform employing CNN reached an accuracy of more than 85%.
“It reached an accuracy of more than 85% [174].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [174]
Cites
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“More recently, accelerometers have gained traction in action recognition with many researcher proposing several learning based methods: Support Vector Machines [11][12], Boosted decision Tree [13], Hidden Markov Models [14], Neural Network [15], Fuzzy Rule Based [16], etc.”
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Review of Wearable Devices and Data Collection Considerations for Connected Health
Vijayan, V., Connolly, J. P., Condell, J., McKelvey, N., & Gardiner, P. (2021) · Sensors · doi:10.3390/s21165589 · Cited by 435 · see citing works
- Authors
- Karungaru, S.
- Affiliations
- Tokushima University (JP)
- Corresponding author
- Karungaru, Stephen
- ORCID
- Karungaru, Stephen
- Venue
- 2015 10th Asian Control Conference (ASCC)
- Publisher
- IEEE
- Published
- 2015-05
- Volume
- 59
- Pages
- 1-4
- OpenAlex
- W1507874087
- Cited by
- 4
- References
- 16
- Citations by year
- 2021: 1 · 2020: 1 · 2018: 1 · 2016: 1
- FWCI
- 0.27
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Asian Control Conference (ASCC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- human action recognition, wearable sensors, artificial neural networks, daily activity recognition, signal feature extraction, elderly care, health monitoring
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