Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experiment
2.1.1. Motion and Experimental Protocol
2.1.2. Participants
2.1.3. Equipment and Data
2.1.4. Output Data Description
2.2. Methodology of the Proposed Method
2.2.1. Preprocess
Noise Depression, Normalization, and Zero-Padding
Data Down-Sampling
- From the first person in Task 1, an n-point fixed Fourier transform was applied to each of the 72 sensor data outputs from the eight IMUs, and amplitudes from first to the n/2th were extracted.
- For each person, the amplitude values of all sensors were summed for each frequency component. The accumulated amplitude value for each N Hz frequency was calculated, where N = {1, 2, 3…50}. The accumulated amplitude for each frequency was divided by the sum of the amplitudes up to 50 Hz, which is the sum of all frequency components, and multiplied by 100 to obtain the percentage (%). Thereafter, the average percentage of the accumulated data for each frequency for all the subjects were calculated.
- Processes 1–2 were repeated until Task 14, and the average of all tasks in terms of the percentage of accumulated data/information were calculated for each frequency.
- The trend of the accumulated information was observed for each frequency, and a frequency having a small increase was selected. To restore up to the corresponding frequency component, the sampling rate was set to twice the frequency component based on the Nyquist sampling theory [30].
Data Augmentation Using the Over-Sampling Technique
- The class set of the scores was . The number of k samples with the closest Euclidean distance to a random sample, , is . can be obtained using the k-nearest neighbor algorithm.
- The number of new samples between and is , and the rule for generating is given by Equation (1):
- Steps 1 and 2 are repeated, so that the amount of class data in each class ) becomes N.
2.2.2. Classification Model
1D-CNN Head and GRU Head
1D-CNN, GRU Stacking Ensemble Model
Training and Evaluation
3. Results and Discussion
3.1. Improving Model Efficiency through a Data Down-Sampling Process
3.2. Classification Model
3.3. Improvement in Model Performance through Data Augmentation
3.4. Comparison with Previous Study
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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No. | Task Description |
---|---|
1 | Sitting to standing |
2 | Standing unsupported |
3 | Sitting unsupported |
4 | Standing to sitting |
5 | Transfers |
6 | Standing with eyes closed |
7 | Standing with feet together |
8 | Reaching forward with outstretched arms |
9 | Retrieving object from floor |
10 | Turning to look behind |
11 | Turning 360° |
12 | Placing alternate foot on stool |
13 | Standing with one foot in front |
14 | Standing on one foot |
Model | C | G | DC | TC | C-G | C+G | DC+G |
---|---|---|---|---|---|---|---|
Mean accuracy (%) | 94.9 | 95.6 | 95.6 | 95.3 | 95.3 | 95.9 | 96.1 |
Standard deviation of accuracy (%) | 4.4 | 4.1 | 4.0 | 4.4 | 4.7 | 4.1 | 3.8 |
Max accuracy (%) | 99.8 | 99.8 | 100 | 99.8 | 100 | 100 | 100 |
Min accuracy (%) | 87.1 | 87.4 | 87.6 | 86.4 | 85.7 | 87.2 | 88.8 |
Mean epoch | 64.9 | 80.6 | 69.9 | 63.8 | 80.1 | 71.7 | 78.8 |
Mean training time (s) | 5.172 | 21.351 | 8.383 | 10.270 | 13.304 | 21.729 | 26.551 |
Epoch time (s) | 0.081 | 0.265 | 0.120 | 0.161 | 0.166 | 0.303 | 0.337 |
Evaluation time (s) | 0.099 | 0.073 | 0.142 | 0.153 | 0.115 | 0.095 | 0.129 |
Study | Badura’s Study | Kim’s Study | This Study |
---|---|---|---|
Classification model | Multi-layer perceptron (MLP) | Support vector Machine (SVM) | Double head 1D-CNN and single head GRU stacking ensemble |
Feature extraction | Manual (Frequency and time domain feature, Feature selection: Fisher’s linear discriminant) | Manual (Frequency domain and energy feature, Feature selection: KPCA) | Automatic in deep learning |
Sampling rate of data (Hz) | 100 | 100 | 20 (Introduce data down-sampling) |
Data imbalance problem | Yes | Yes | No (Introduce data augmentation) |
Amount of experimental data | 63 | 53 | 78 |
Evaluation method | Random split Training: Test = 7:3 | Random split Training: Test = 7:3 | Mean accuracy of 10-fold cross validation |
Task | Badura’s MLP Accuracy (%) | Kim’s SVM Accuracy (%) | DC+G Accuracy (%) |
---|---|---|---|
1 | 87.5 | 100 | 98.5 |
2 | 92.2 | 100 | 98.5 |
3 | 100 | 100 | 99.6 |
4 | 89.1 | 87.5 | 99.0 |
5 | 70.3 | 76.5 | 96.7 |
6 | 89.1 | 100 | 97.9 |
7 | 76.6 | 100 | 99.0 |
8 | 76.6 | 92.9 | 98.9 |
9 | 89.1 | 100 | 97.8 |
10 | 70.3 | 78.6 | 98.2 |
11 | 78.1 | 100 | 97.8 |
12 | 79.7 | 80.0 | 98.2 |
13 | 62.5 | 90.0 | 98.1 |
14 | 67.2 | 100 | 99.1 |
Average | 80.6 | 93.2 | 98.4 |
Standard deviation | 10.9 | 9.1 | 0.7 |
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Kim, Y.-W.; Joa, K.-L.; Jeong, H.-Y.; Lee, S. Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model. Sensors 2021, 21, 7628. https://doi.org/10.3390/s21227628
Kim Y-W, Joa K-L, Jeong H-Y, Lee S. Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model. Sensors. 2021; 21(22):7628. https://doi.org/10.3390/s21227628
Chicago/Turabian StyleKim, Yeon-Wook, Kyung-Lim Joa, Han-Young Jeong, and Sangmin Lee. 2021. "Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model" Sensors 21, no. 22: 7628. https://doi.org/10.3390/s21227628