HARGRURNN: Human activity recognition using inertial body sensor gated recurrent units recurrent neural network
Abstract
Internet of Things (IoT), wearable devices, and smartphones are more ubiquitous and available at a reasonable cost. Fitbit, Microsoft Band, Apple Watch, MI band, and Smartphone apps namely Strava and Runkeeper are some of the commercial products which are already available in the market. These products are embedded with the sensors that facilitate them to regularly sense and capture the environmental, physiological, functional data for applications in healthcare, wellbeing, and sports. These sensing devices have mediocre computing capabilities for data processing and transfer. The size of devices is so compact that can be worn on the body put in the pocket or locate in the house. This large scale recorded a wealth of physiological data require an efficient and meaningful interpretation as well as the proper and proficient method of analysis and the classification of data.Human Activity Recognition is classified into two features say Shallow and Deep features. Shallow features are extracted conventionally with the help of a simple machine learning approach. The large-scale time series situ data which require high computation power, processing capabilities, activities, and real-time classification, Deep Learning (DL) is the promising technique to deal with these activities.Weexercise the triaxial Accelerometers and triaxial Gyroscope to capture the data for HAR. The data set measured by the inertial sensor is then divided into the segments of 4 to 10s. The performance will be shown on the parameters of precision (%), Recall (%) and Accuracy (%).Parameter optimization and extraction of the segments are the two areas where we can carry forward our research.
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[201] utilizes attention with a 1D CNN GRU, achieving HAR performances of 96.5% ± 1.0%, 93.1% ± 2.2%, and 89.3% ± 1.3% on Heterogeneous, Skoda, and PAMAP2 datasets, respectively.
“[201] utilizes attention in combination with a 1D CNN Gated Recurrent Units (GRUs), achieving HAR performances of 96.5% ± 1.0%, 93.1% ± 2.2%, and 89.3% ± 1.3% on Heterogeneous [86], Skoda [103], and PAMAP2 [91] datasets, respectively.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [201]
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Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
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“[135] utilizes attention in combination with a 1D CNN Gated Recurrent Units (GRUs), achieving HAR performances of 96.”
BackgroundMethod
- Authors
- Jangir, M. K., & Singh, K.
- Affiliations
- Jawaharlal Nehru University (IN)
- Corresponding author
- Jangir, Mahendra Kumar
- ORCID
- Singh, Karan
- Venue
- Journal of Discrete Mathematical Sciences and Cryptography
- Publisher
- Taru Publications
- Published
- 2019-11-17
- Volume
- 22
- Issue
- 8
- Pages
- 1577-1587
- ISSN
- 0972-0529 (ISSN-L)
- 2169-0065
- OpenAlex
- W2998763852
- Cited by
- 11
- References
- 23
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 2 · 2022: 2 · 2021: 4 · 2020: 1
- FWCI
- 0.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Discrete Mathematical Sciences and Cryptography (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Keywords
- human activity recognition, inertial sensors, wearable devices, gated recurrent unit, recurrent neural networks, triaxial accelerometer, triaxial gyroscope, time series classification, Internet of Things
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