Smartphone IMU Sensors for Human Identification through Hip Joint Angle Analysis
Abstract
:1. Introduction
2. Theory
2.1. Human Gait Analysis
2.2. IMUs and MARG in Smartphones
2.3. The Sensor Fusion and Signal Processing
3. Methods
3.1. Participants
3.2. Comparison of Measurements
3.3. Hip Joint Identification
3.3.1. Data Acquisition
3.3.2. Feature Extraction and Classification Techniques
4. Results and Discussion
4.1. Comparison of the Measuring Systems
4.2. Hip Joint Angles
4.3. Classification Analysis
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
MCS | motion capture system |
EMG | electromyography |
ML | machine learning |
SVM | support vector machine |
NN | neural network |
LSTM NN | long short-term memory neural network |
RNN | recurrent neural network |
NB | naive Bayes |
LDA | linear discriminant analysis |
HCNN | hybrid convolutional neural network |
IMU | inertial measurement unit |
MARG | magnetic, angular rate, and gravity |
MEMS | micro-electro-mechanical system |
SFA | sensor fusion algorithm |
LCF | linear complementary filter |
NCF | nonlinear complementary filter |
LKF | linear Kalman filter |
EKF | extended Kalman filter |
CKF | complementary Kalman filter |
SRUKF | square root unscented Kalman filter |
SRCKF | Square Root Cubature Kalman Filter |
AHRS | attitude heading reference system |
SD | standard deviation |
RMSE | root mean square error |
MV | mean value |
M | median |
COV | covariance |
VAR | variance |
KUR | kurtosis |
SKE | skewness |
PCA | principal component analysis |
WEKA | Waikato Environment for Knowledge Analysis |
BayesNet | Bayesian network |
NB | naive Bayesian |
SMO | sequential minimal optimization |
LWL | locally weighted learning |
kNN | K-nearest neighbor |
IBk | instance-based K |
JRip | Java repeated incremental pruning |
RIPPER | repeated incremental pruning to produce error reduction error reduction |
PART | partial decision tree |
LMT | logistic model tree |
REPTree | reduced error pruning tree |
CA | classification accuracy |
CI | confidence interval |
ROC | receiver operating characteristic |
AUC | area under the curve |
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Feature | Description | Mathematical Definition |
---|---|---|
Mean Value (MV) | The average of all angles in the sequence. | |
Median (M) | The middle angle value in the ordered sequence. | |
Maximum Angle | The largest angle observed. | |
Covariance (COV) | Indicates how two angle variables vary together. | |
Minimum Angle | The smallest angle observed. | |
Variance (VAR) | Measures the dispersion around the mean angle. | |
Standard Deviation (SD) | Shows the amount of variation or dispersion of angle values. | |
Kurtosis (KUR) | Describes the sharpness or flatness of the angle distribution. | |
Skewness (SKE) | Shows the asymmetry in the angle distribution. |
No. | Category | Algorithms |
---|---|---|
1 | Bayesian Classifiers | BayesNet |
NB | ||
2 | Function Classifiers | Logistic-R |
MultiPerceptron | ||
SMO | ||
Simple Logistic | ||
ClassViRegression | ||
3 | Lazy Classifiers | KStar |
LWL | ||
IBk | ||
4 | Rule Classifiers | JRip |
PART | ||
5 | Tree Classifiers | J48 |
LMT | ||
RF | ||
REPTree |
Principal Component | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
---|---|---|---|---|---|---|---|---|---|
Explained Variance (%) | 91.1337 | 8.4542 | 0.2717 | 0.1325 | 0.0073 | 0.0005 | 0.0001 | 0.0000 | 0.0000 |
No. | Classification Model | CA % | Av. ROC | Av. CI |
---|---|---|---|---|
1 | BayesNet [80] | 84.26 ± 1.2 | 0.975 ± 0.002 | [0.829 0.859] |
2 | NB [81] | 85.5 ± 2.1 | 0.988 ± 0.003 | [0.856 0.884] |
3 | Logistic-R [82] | 87.1 ± 1.6 | 0.986 ± 0.001 | [0.865 0.892] |
4 | MultiPerceptron [83] | 88.9 ± 1.3 | 0.965 ± 0.003 | [0.874 0.899] |
5 | SMO [84] | 84.9 ± 1.2 | 0.976 ± 0.002 | [0.847 0.875] |
6 | SimpleLogistic [85] | 88.4 ± 2.3 | 0.989 ±0.002 | [0.861 0.907] |
7 | ClassViRegression [82] | 85.4 ± 1.8 | 0.985 ± 0.004 | [0.847 0.875] |
8 | KStar [86] | 86.1 ± 2.6 | 0.987 ± 0.003 | [0.849 0.887] |
9 | LWL [87] | 63.4 ± 1.6 | 0.937 ± 0.001 | [0.622 0.661] |
10 | IBk [86] | 84.1 ± 1.1 | 0.917 ± 0.001 | [0.830 0.852] |
11 | JRip [86] | 80.1 ± 1.1 | 0.947 ± 0.003 | [0.786 0.818] |
12 | PART [88] | 83.4 ± 1.5 | 0.932 ± 0.001 | [0.819 0.849] |
13 | J48 [86] | 84.9 ± 1.4 | 0.937 ± 0.005 | [0.814 0.844] |
14 | LMT [74] | 88.2 ± 1.4 | 0.989 ± 0.001 | [0.868 0.896] |
15 | RF [89] | 86.9 ± 1.1 | 0.903 ± 0.001 | [0.863 0.889] |
16 | REPTree [90] | 82.9 ± 1.2 | 0.960 ± 0.001 | [0.813 0.843] |
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Andersson, R.; Bermejo-García, J.; Agujetas, R.; Cronhjort, M.; Chilo, J. Smartphone IMU Sensors for Human Identification through Hip Joint Angle Analysis. Sensors 2024, 24, 4769. https://doi.org/10.3390/s24154769
Andersson R, Bermejo-García J, Agujetas R, Cronhjort M, Chilo J. Smartphone IMU Sensors for Human Identification through Hip Joint Angle Analysis. Sensors. 2024; 24(15):4769. https://doi.org/10.3390/s24154769
Chicago/Turabian StyleAndersson, Rabé, Javier Bermejo-García, Rafael Agujetas, Mikael Cronhjort, and José Chilo. 2024. "Smartphone IMU Sensors for Human Identification through Hip Joint Angle Analysis" Sensors 24, no. 15: 4769. https://doi.org/10.3390/s24154769
APA StyleAndersson, R., Bermejo-García, J., Agujetas, R., Cronhjort, M., & Chilo, J. (2024). Smartphone IMU Sensors for Human Identification through Hip Joint Angle Analysis. Sensors, 24(15), 4769. https://doi.org/10.3390/s24154769