Human Activity Recognition Using 1D Convolutional Neural Network

Banjarey, Khushboo; Sahu, Satya Prakash; Dewangan, Deepak Kumar

doi:10.1007/978-981-16-5157-1_54

Cited by 12 publications

(1 citation statement)

References 38 publications

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“…A 1D-CNN-based stacking ensemble structure model [ 35 , 36 , 37 , 38 ] that exhibits good performance and efficiency in an inertial sensor-based HAR algorithm is used as the predictor. Structurally, it consists of a simple dense layer classifier after a double-head 1D-CNN, and the kernel sizes of the two heads are taken to be 1 and 3 to extract different features.…”

Section: Methodsmentioning

confidence: 99%

Data Valuation Algorithm for Inertial Measurement Unit-Based Human Activity Recognition

Kim

Lee

2022

Sensors

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This paper proposes a data valuation algorithm for inertial measurement unit-based human activity recognition (IMU-based HAR) data based on meta reinforcement learning. Unlike previous studies that received feature-level input, the algorithm in this study added a feature extraction structure to the data valuation algorithm, and it can receive raw-level inputs and achieve excellent performance. As IMU-based HAR data are multivariate time-series data, the proposed algorithm incorporates an architecture capable of extracting both local and global features by inserting a transformer encoder after the one-dimensional convolutional neural network (1D-CNN) backbone in the data value estimator. In addition, the 1D-CNN-based stacking ensemble structure, which exhibits excellent efficiency and performance on IMU-based HAR data, is used as a predictor to supervise model training. The Berg balance scale (BBS) IMU-based HAR dataset and the public datasets, UCI-HAR, WISDM, and PAMAP2, are used for performance evaluation in this study. The valuation performance of the proposed algorithm is observed to be excellent on IMU-based HAR data. The rate of discovering corrupted data is higher than 96% on all datasets. In addition, classification performance is confirmed to be improved by the suppression of discovery of low-value data.

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