Classification of User Movement Data
Abstract
In this paper, a comprehensive analysis of various classification techniques is used on user movement data. The dataset used here utilizes a Nexus One Android smartphone, equipped with accelerometer sensor devices. The data was transformed and applied to two deep learning techniques: Convolutional Neural Network (CNN) and Long Short Term Memory-Recurrent Neural Network (LSTM-RNN). The results obtained via CNN and LSTM-RNN are compared with the traditional classifiers such as k-Nearest Neighbors (k-NN) and Feed Forward Neural Network (FFNN). The results show that the deep learning approaches were outperformed by the traditional classifiers on the applied dataset. While deep learning techniques reached a maximum accuracy of 84% utilizing LSTM, k-NN obtained an accuracy of 99.6%.
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The accuracy of a TensorFlow-based Convolution Neural Network in classifying movements, such as walking patterns, limps and foot placement, was 84%.
“The accuracy of a TensorFlow-based Convolution Neural Network (CNN) in classifying movements, such as walking patterns, limps and foot placement, was 84% [166].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [166]
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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
- Arias, P., Kelley, C., Mason, J., Bryant, K., & Roy, K.
- Affiliations
- North Carolina Agricultural and Technical State University (US)
- ORCID
- Mason, Janelle
- Venue
- Proceedings of the 2nd International Conference on Digital Signal Processing
- Publisher
- ACM
- Published
- 2018-02-25
- Pages
- 156-160
- OpenAlex
- W2804848393
- Cited by
- 6
- References
- 19
- Citations by year
- 2022: 1 · 2021: 3 · 2019: 2
- FWCI
- 0.38
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Digital Signal Processing (DSP) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Keywords
- smartphone accelerometer, activity recognition, convolutional neural networks, LSTM, k-nearest neighbors, feedforward neural networks, wearable sensor data
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