A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
3.1. Data
- 0—Not part of the experiment; user performed activities are unrelated to the experimental protocol while the sensors were installed.
- 1—Experiment; no FOG.
- 2—FOG.
3.2. Proposed Data Preparation Method
3.3. Preprocessing
3.3.1. Removing Unrelated Data
3.3.2. Calculating Magnitude of Acceleration for all three axis
3.4. Data Augmentation
3.4.1. Signal Segmentation
3.4.2. Labeling PreFOG class
- 0—Non FOG
- 1—FOG
- 2—Pre FOG
3.4.3. SMOTE Oversampling
3.5. Feature Extraction
- = Moving window extracted from signal .
- = Manually extracted feature set from .
- = Recurrence Plot representation of .
- = Short Time Fourier Transform representation of .
- = Discrete Wavelet Transform representation of .
- = Pseudo Wigner Ville Distribution representation of .
3.5.1. Time and Frequency Domain Features
3.5.2. Recurrence Plots
3.5.3. Short Time Fourier Transform
3.5.4. Discrete Wavelet Transform
3.5.5. Pseudo Wigner Ville Distribution
3.6. Model Structure
3.6.1. Basic Convolutional Neural Network
3.6.2. Basic Bidirectional LSTM
3.6.3. Ensemble Architectures
Stacked Ensemble Model—M7
Average Ensemble Model—M8
Majority Voting—M9
4. Results
4.1. Evaluation Criteria
4.1.1. Detection Accuracy
4.1.2. Precision, Recall/Sensitivity, Specificity, Score
- is the predictions for class k.
- is the occurrences for class k.
- is the predictions for samples not in class k.
- is the occurrences for samples not in class k.
- k represents a class in range , in our case .
4.1.3. Matthews Correlation Coefficient
- , the number of occurrences of class k.
- , the number of predictions for class k.
- , total correct predictions.
- , total number of samples.
4.2. K-Fold Cross Validation
4.3. Normalization
4.4. Experimental Setup
4.5. Metric Scores and Discussion
5. Application of Trained Model on Data Collected from APDM™ Sensors
5.1. Data Collection and Processing
Sensor Types and Locations
- Accelerometer.
- Magnetometer.
- Gyroscope.
- Barometer.
- Temperature.
5.2. Workflow and Challenges
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
FOG | Freezing of Gait |
PD | Parkinson’s Disease |
RAS | Rhythmic Auditory Stimulation |
RP | Recurrence Plot |
STFT | Short Time Fourier Transform |
DWT | Discreet Wavelet Transform |
PWVD | Pseudo Wigner Ville Distribution |
DL | Deep Learning |
ML | Machine Learning |
LSTM | Long Short Term Memory |
CNN | Concolutional Neural Network |
RNN | Recurrent Neural Network |
ADL | Activities of Daily Living |
HAR | Human Activity Recognition |
WB | Walking Band |
FB | Freezing Band |
FTH | Freezing Threshold |
PTH | Power Threshold |
FI | Freeze Index |
PI | Power Index |
RF | Random Forest |
KNN | K Nearest Neighbours |
AUC | Area Under Curve |
ROC | Receiver Operating Characteristic |
SVM | Support Vector Machine |
LOSO | Leave One Subject Out |
GB | Gradient Boosting |
RBF | Radial Basis Function |
IMU | Inertial Measurement Unit |
SQA | Speech Quality Assesment |
MFCC | Mel Frequency Cepstral Coefficients |
EER | Equal Error Rate |
FFT | Fast Fourier Transform |
MCC | Matthews Correlation Coefficient |
TP | True Positive |
FP | False Positive |
TN | True Negative |
FN | False Negative |
2AFC | Two-Alternative-Forced-Choice |
DSCQS | Double Stimulus Continuous Quality Scale |
U-AMS | Activity Monitoring System |
GM | Geometric Mean |
SQA | Speech Quality Assessment |
HMM | Hidden Markov Model |
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Authors | Technology Used |
---|---|
Han et al. [48] | U-AMS (Activity Monitoring System) using wavelet power features for discrimination of abnormal movement. |
Moore et al. [49] | Threshold based method for FOG detection by defining the Freeze Index (FI), achieving True Positive Rate |
Bachlin et al. [28] | Added Power Index (PI) to the method proposed by Moore et al. [49] |
Bachlin et al. [28] | Two thresholds (FTH and PTH) given and , achieving sensitivity and specificity |
Mazilu et al. [50] | Additional Features along with Bachlin et al. [28] features used with ML models RF, Naive Bayes and KNN, achieving sensitivity and specificity |
Tripoliti et al. [51] | Data imputation (interpolation), band-pass filtering, entropy calculation, and automatic classification (Naïve Bayes, RF, Decision Trees and Random Tree), resulting in sensitivity, specificity, accuracy and AUC |
Moore et al. [52] | Assessed seven sensors placed in different locations and concluded that the shank and back were the most convenient places for the sensors. Using all the seven sensors achieved best performance with sensitivity and specificity |
Zack et al. [53] | presented a threshold based FOG detection technique following Moore [52] using ROC to determine a global FI threshold to distinguish between FOG and non-FOG episodes for different tasks, achieving sensitivity of and specificity of |
Rodríguez et al. [54] | used ML techniques and 55 FOG related features from 21 PD patients with SVM. reporting a sensitivity of and specificity of for user independent evaluation, and a sensitivity of and specificity of for LOSO evaluation |
Sama et al. [55] | Decreased the number of features to 28 for the Rodríguez et al. [54] dataset and used 8 different classifiers with greedy subset selection process, 10-fold cross-validation and different window sizes, achieving and for sensitivity and specificity respectively |
Orphanidou et al. [56] | Extracted features and presented to 7 machine learning classifiers, with SVM achieving the highest performance. The classification algorithm was applied to 5 s windows using 18 features, obtaining balanced accuracy (the mean value of sensitivity and specificity) of , , and over the Walk, FOG and Transition classes, respectively |
Orphanidou et al. [56] | Focused on the early detection, through classification of the transition class using varying size time windows and time/frequency contrary to the majority of previous studies that recognized FOG only when it had occurred by including another class label called ‘transition’ that showed the episodes before FOG occurrence. 5 classifiers were used, including Gradient Boosting (GB), Extreme Gradient Boosting, SVM, RF and Neural Networks. SVM with RBF kernels has the best performance with sensitivity of , , and specificity values of , and , for FOG, transition and normal activity classes, respectively |
Camps et al. [57] | Proposed 1D Convolutions Neural Network (CNN) with 8 layers, trained using a novel spectral data representation strategy that considers information from both the previous and current signal windows showing a performance of for the GM, an AUC of , a sensitivity of and a sensibility of |
San-Segundo et al. [58] | Evaluated the robustness of different feature sets and ML algorithms for FOG detection using body-worn accelerometers with 4 feature sets: (Mazilu et al. [50], HAR, MFCCs, and SQA) using 4 classification methods (RF, multi-layer perceptron, HMM and deep neural networks).The current window and three previous windows, with the feature set composed of Mazilu features [50] and MFCCs [59] achieved best scores. Deep convolutions neural network achieved an AUC of and an Equal Error Rate (EER) of . |
Sigcha et al. [60] | Reproduced Mazilu features [50], MFCCs [59], and FFT) to establish a baseline using RF classifier with 10-fold cross-validation (R10fold) and LOSO. Further, tested multiple DL approaches include: A denoiser autoencoder, a deep neural network with CNN and a combination of CNN and LSTM layers and compared with shallow algorithms such as one-class SVM (OC-SVM), SVM, AdaBoost and RF, achieving AUC |
Our Proposed Method | Using all three sensors from Daphnet [28] dataset, used moving windows extracted from the original signal, handcrafted feature set and time frequency visualization techniques including RP, STFT, DWT and PWVD alongside a CNN and LSTM architecture. Evaluated the performance and used trained models to create 3 ensemble architectures which further improve the performance. The proposed method is capable of both FOG detection and prediction and the evaluation scores show better performance when compared to existing approaches. |
Time Domain Features | Description |
---|---|
Min, Max | Minimum and Maximum value of the signal |
Range | Difference between the minimum and maximum value of the signal |
Mean | Average value of signal |
Median | Median value of the signal |
Mode | Modal value of the signal |
Trimmed Mean | Trimmed/Truncated mean of the signal |
Standard Deviation | Deviation of a signal compared to its mean |
Variance | Square root of the standard deviation of the signal |
Root mean square | Square root of the mean of the squared signal |
Mean absolute value | Mean of absolute value of the signal |
Median absolute deviation | Median over the absolute deviations from the median |
25th Percentile | 25th percentile value of the signal |
75th Percentile | 75th percentile value of the signal |
Interquantile range | Difference between the 75th and 25th percentile of the signal |
Normalized Signal Magnitude Area | Sum of standardized acceleration magnitude normalized by window length |
Skewness | The degree of asymmetry in the signal |
Kurtosis | The degree of peakedness in the signal, signals with high kurtosis have more outliers |
Mean Crossing Rate | The number of times the signals goes from above average value to below average value normalized by the window length |
Signal Vector Magnitude | Sum of euclidean norm over the window normalized by window length |
Peak of Fourier Transform | Maximum magnitude of Discrete Fourier Transform of the signal normalized by the window length |
Frequency Domain Features | Description |
Entropy | Measure of random distribution of frequency |
Energy | Sum of squared magnitude of FFT of the signal divided by window length |
Peak Frequency | Maximum frequency value in the power spectrum |
Freeze Band Power | The sum of power in Freeze band of frequencies divided by sampling frequency |
Locomotion Band Power | The sum of power in Locomotion band of frequencies divided by sampling frequency |
Freeze Index | Power of signal in freeze band (3-8Hz) divided by its Power in locomotion band(0.5-3Hz) |
Band Power | Sum of the power in freeze band and in locomotion band |
Model | Window (s) | Sensitivity | Specificity | Score |
---|---|---|---|---|
Mazilu et al. [62] (Unsupervised—20 Features) | 3 | 76.86 | 86.21 | 81.56 |
Mazilu et al. [62] (Supervised—20 Features) | 3 | 66.65 | 88.74 | 78.27 |
Mazilu et al. [62] (Unsupervised—25 Features) | 3 | 76.86 | 85.52 | 80.82 |
Mazilu et al. [62] (Supervised—25 Features) | 3 | 67.58 | 88.52 | 78.65 |
Decision Tree [64] | 4 | 96.70 | 98.92 | - |
Random Forest [64] | 4 | 98.91 | 99.44 | - |
AdaBoost [64] | 4 | 97.99 | 99.56 | - |
KNN [64] | 4 | 94.61 | 97.38 | - |
SVM [64] | 4 | 97.54 | 98.64 | - |
ProtoNN [64] | 4 | 95.25 | 99.66 | - |
Bonsai [64] | 4 | 92.9 | 98.36 | - |
Data Type | Window (s) | Accuracy | Precision | Sensitivity | Specificity | Score | MCC | Runtime (min) |
---|---|---|---|---|---|---|---|---|
(3) | 2 | 0.894 ± 0.021 | 0.929 ± 0.006 | 0.894 ± 0.021 | 0.933 ± 0.007 | 0.904 ± 0.016 | 0.687 ± 0.039 | 52.05 |
3 | 0.832 ± 0.023 | 0.905 ± 0.011 | 0.832 ± 0.023 | 0.926 ± 0.010 | 0.849 ± 0.021 | 0.648 ± 0.035 | 17:41 | |
4 | 0.864 ± 0.030 | 0.901 ± 0.020 | 0.864 ± 0.030 | 0.925 ± 0.020 | 0.873 ± 0.027 | 0.695 ± 0.058 | 11:52 | |
(3) | 2 | 0.891 ± 0.019 | 0.929 ± 0.008 | 0.891 ± 0.019 | 0.937 ± 0.008 | 0.902 ± 0.016 | 0.684 ± 0.037 | 57:31 |
3 | 0.873 ± 0.005 | 0.915 ± 0.006 | 0.873 ± 0.005 | 0.937 ± 0.006 | 0.883 ± 0.005 | 0.702 ± 0.014 | 22:41 | |
4 | 0.840 ± 0.022 | 0.895 ± 0.013 | 0.840 ± 0.022 | 0.930 ± 0.014 | 0.853 ± 0.020 | 0.664 ± 0.037 | 9:25 | |
(3) | 2 | 0.926 ± 0.013 | 0.942 ± 0.007 | 0.926 ± 0.013 | 0.944 ± 0.009 | 0.930 ± 0.011 | 0.755 ± 0.033 | 64:58 |
3 | 0.876 ± 0.021 | 0.921 ± 0.008 | 0.876 ± 0.021 | 0.924 ± 0.008 | 0.889 ± 0.017 | 0.649 ± 0.038 | 15:24 | |
4 | 0.849 ± 0.000 | 0.911 ± 0.000 | 0.849 ± 0.000 | 0.934 ± 0.000 | 0.863 ± 0.000 | 0.667 ± 0.000 | 9:59 |
Data Type | Window (s) | Accuracy | Precision | Sensitivity | Specificity | Score | MCC | Runtime (min) |
---|---|---|---|---|---|---|---|---|
(3) | 2 | 0.678 ± 0.016 | 0.893 ± 0.008 | 0.678 ± 0.0176 | 0.860 ± 0.007 | 0.742 ± 0.011 | 0.407 ± 0.014 | 68.11 |
3 | 0.799 ± 0.030 | 0.883 ± 0.010 | 0.799 ± 0.030 | 0.897 ± 0.012 | 0.819 ± 0.026 | 0.585 ± 0.038 | 18:41 | |
4 | 0.781 ± 0.030 | 0.877 ± 0.005 | 0.781 ± 0.030 | 0.905 ± 0.010 | 0.803 ± 0.025 | 0.588 ± 0.026 | 11:22 | |
(3) | 2 | 0.719 ± 0.023 | 0.889 ± 0.009 | 0.719 ± 0.023 | 0.864 ± 0.017 | 0.762 ± 0.019 | 0.452 ± 0.035 | 127:59 |
3 | 0.815 ± 0.025 | 0.902 ± 0.006 | 0.815 ± 0.025 | 0.921 ± 0.008 | 0.834 ± 0.021 | 0.633 ± 0.030 | 21:02 | |
4 | 0.746 ± 0.036 | 0.865 ± 0.010 | 0.746 ± 0.036 | 0.894 ± 0.013 | 0.770 ± 0.031 | 0.550 ± 0.032 | 8:55 | |
(3) | 2 | 0.781 ± 0.010 | 0.899 ± 0.002 | 0.781 ± 0.010 | 0.894 ± 0.003 | 0.813 ± 0.008 | 0.516 ± 0.008 | 57:19 |
3 | 0.831 ± 0.037 | 0.911 ± 0.013 | 0.831 ± 0.037 | 0.914 ± 0.019 | 0.854 ± 0.030 | 0.586 ± 0.060 | 14:36 | |
4 | 0.816 ± 0.005 | 0.905 ± 0.004 | 0.816 ± 0.005 | 0.927 ± 0.003 | 0.836 ± 0.004 | 0.629 ± 0.004 | 8:19 |
Data Type | Window (s) | Accuracy | Precision | Sensitivity | Specificity | Score | MCC | Runtime (min) |
---|---|---|---|---|---|---|---|---|
(3) | 2 | 0.939 ± 0.002 | 0.948 ± 0.004 | 0.939 ± 0.002 | 0.947 ± 0.009 | 0.942 ± 0.002 | 0.785 ± 0.013 | 67.44 |
3 | 0.922 ± 0.031 | 0.940 ± 0.019 | 0.922 ± 0.031 | 0.949 ± 0.017 | 0.926 ± 0.028 | 0.796 ± 0.068 | 22:39 | |
4 | 0.906 ± 0.022 | 0.923 ± 0.018 | 0.906 ± 0.022 | 0.941 ± 0.017 | 0.910 ± 0.021 | 0.766 ± 0.050 | 13:48 | |
(3) | 2 | 0.923 ± 0.005 | 0.941 ± 0.001 | 0.923 ± 0.005 | 0.944 ± 0.006 | 0.928 ± 0.004 | 0.748 ± 0.003 | 127:32 |
3 | 0.940 ± 0.010 | 0.951 ± 0.006 | 0.940 ± 0.010 | 0.963 ± 0.003 | 0.943 ± 0.009 | 0.834 ± 0.020 | 26:43 | |
4 | 0.930 ± 0.020 | 0.940 ± 0.015 | 0.930 ± 0.020 | 0.958 ± 0.014 | 0.932 ± 0.019 | 0.817 ± 0.044 | 16:47 | |
(3) | 2 | 0.946 ± 0.015 | 0.954 ± 0.010 | 0.946 ± 0.015 | 0.952 ± 0.010 | 0.949 ± 0.013 | 0.811 ± 0.042 | 55:17 |
3 | 0.938 ± 0.019 | 0.949 ± 0.012 | 0.938 ± 0.019 | 0.952 ± 0.008 | 0.941 ± 0.017 | 0.784 ± 0.048 | 22:12 | |
4 | 0.943 ± 0.008 | 0.953 ± 0.005 | 0.943 ± 0.008 | 0.971 ± 0.003 | 0.945 ± 0.008 | 0.839 ± 0.019 | 12:39 |
Data Type | Window (s) | Accuracy | Precision | Sensitivity | Specificity | Score | MCC | Runtime (min) |
---|---|---|---|---|---|---|---|---|
(3) | 2 | 0.831 ± 0.023 | 0.906 ± 0.006 | 0.831 ± 0.023 | 0.902 ± 0.011 | 0.852 ± 0.018 | 0.571 ± 0.035 | 74.00 |
3 | 0.865 ± 0.015 | 0.907 ± 0.009 | 0.865 ± 0.015 | 0.930 ± 0.010 | 0.876 ± 0.014 | 0.681 ± 0.031 | 32:34 | |
4 | 0.825 ± 0.024 | 0.888 ± 0.010 | 0.825 ± 0.024 | 0.919 ± 0.010 | 0.839 ± 0.021 | 0.641 ± 0.035 | 17:46 | |
(3) | 2 | 0.811 ± 0.011 | 0.901 ± 0.005 | 0.811 ± 0.011 | 0.897 ± 0.009 | 0.836 ± 0.009 | 0.545 ± 0.021 | 131:00 |
3 | 0.871 ± 0.020 | 0.912 ± 0.005 | 0.871 ± 0.020 | 0.930 ± 0.005 | 0.881 ± 0.016 | 0.695 ± 0.026 | 28:58 | |
4 | 0.831 ± 0.025 | 0.895 ± 0.010 | 0.831 ± 0.025 | 0.923 ± 0.013 | 0.846 ± 0.021 | 0.657 ± 0.032 | 16:27 | |
(3) | 2 | 0.805 ± 0.011 | 0.900 ± 0.010 | 0.805 ± 0.011 | 0.887 ± 0.017 | 0.832 ± 0.010 | 0.531 ± 0.036 | 68:28 |
3 | 0.842 ± 0.033 | 0.908 ± 0.009 | 0.842 ± 0.033 | 0.917 ± 0.013 | 0.861 ± 0.026 | 0.590 ± 0.052 | 29:24 | |
4 | 0.864 ± 0.025 | 0.916 ± 0.010 | 0.864 ± 0.025 | 0.946 ± 0.012 | 0.877 ± 0.021 | 0.687 ± 0.040 | 15:50 |
Data Type | Window (s) | Accuracy | Precision | Sensitivity | Specificity | Score | MCC | Runtime (min) |
---|---|---|---|---|---|---|---|---|
(3) | 2 | 0.797 ± 0.106 | 0.896 ± 0.027 | 0.797 ± 0.106 | 0.917 ± 0.048 | 0.827 ± 0.082 | 0.527 ± 0.149 | 302.21 |
3 | 0.784 ± 0.124 | 0.903 ± 0.018 | 0.784 ± 0.124 | 0.939 ± 0.053 | 0.817 ± 0.092 | 0.597 ± 0.152 | 149:31 | |
4 | 0.695 ± 0.178 | 0.832 ± 0.053 | 0.695 ± 0.178 | 0.774 ± 0.074 | 0.715 ± 0.155 | 0.441 ± 0.168 | 73:22 | |
(3) | 2 | 0.705 ± 0.106 | 0.877 ± 0.012 | 0.705 ± 0.106 | 0.836 ± 0.034 | 0.747 ± 0.088 | 0.418 ± 0.091 | 121:56 |
3 | 0.894 ± 0.021 | 0.929 ± 0.006 | 0.894 ± 0.021 | 0.933 ± 0.007 | 0.904 ± 0.016 | 0.687 ± 0.039 | 96.09 | |
4 | 0.360 ± 0.380 | 0.315 ± 0.432 | 0.360 ± 0.380 | 0.806 ± 0.134 | 0.312 ± 0.419 | 0.246 ± 0.351 | 52:05 | |
(3) | 2 | 0.715 ± 0.270 | 0.880 ± 0.071 | 0.715 ± 0.270 | 0.800 ± 0.120 | 0.744 ± 0.239 | 0.478 ± 0.314 | 199:10 |
3 | 0.778 ± 0.142 | 0.903 ± 0.032 | 0.778 ± 0.142 | 0.930 ± 0.060 | 0.814 ± 0.111 | 0.531 ± 0.181 | 105:22 | |
4 | 0.441 ± 0.312 | 0.517 ± 0.372 | 0.441 ± 0.312 | 0.728 ± 0.059 | 0.411 ± 0.305 | 0.111 ± 0.157 | 74:20 |
Data Type | Window (s) | Accuracy | Precision | Sensitivity | Specificity | Score | MCC | Runtime (min) |
---|---|---|---|---|---|---|---|---|
(3) | 2 | 0.846 ± 0.034 | 0.912 ± 0.015 | 0.846 ± 0.034 | 0.902 ± 0.023 | 0.865 ± 0.028 | 0.600 ± 0.067 | 179.36 |
3 | 0.815 ± 0.020 | 0.896 ± 0.007 | 0.815 ± 0.020 | 0.923 ± 0.009 | 0.834 ± 0.017 | 0.620 ± 0.026 | 91:14 | |
4 | 0.803 ± 0.033 | 0.875 ± 0.010 | 0.803 ± 0.033 | 0.893 ± 0.009 | 0.821 ± 0.028 | 0.597 ± 0.040 | 49:37 | |
(3) | 2 | 0.822 ± 0.013 | 0.907 ± 0.008 | 0.822 ± 0.013 | 0.904 ± 0.012 | 0.845 ± 0.010 | 0.563 ± 0.025 | 147:41 |
3 | 0.812 ± 0.011 | 0.897 ± 0.001 | 0.812 ± 0.011 | 0.917 ± 0.002 | 0.832 ± 0.008 | 0.620 ± 0.011 | 89:48 | |
4 | 0.783 ± 0.070 | 0.859 ± 0.042 | 0.783 ± 0.070 | 0.855 ± 0.043 | 0.801 ± 0.062 | 0.557 ± 0.122 | 47:42 | |
(3) | 2 | 0.840 ± 0.007 | 0.907 ± 0.014 | 0.840 ± 0.007 | 0.898 ± 0.025 | 0.859 ± 0.006 | 0.579 ± 0.039 | 190:06 |
3 | 0.773 ± 0.022 | 0.900 ± 0.001 | 0.773 ± 0.022 | 0.897 ± 0.004 | 0.807 ± 0.017 | 0.519 ± 0.019 | 61:44 | |
4 | 0.770 ± 0.019 | 0.888 ± 0.013 | 0.770 ± 0.019 | 0.909 ± 0.019 | 0.797 ± 0.017 | 0.560 ± 0.035 | 40:11 |
Data Type | Window (s) | Accuracy | Precision | Sensitivity | Specificity | Score | MCC | Runtime (min) |
---|---|---|---|---|---|---|---|---|
(3) | 2 | 0.971 ± 0.007 | 0.972 ± 0.007 | 0.971 ± 0.007 | 0.956 ± 0.012 | 0.971 ± 0.007 | 0.885 ± 0.027 | 200.32 |
3 | 0.979 ± 0.002 | 0.979 ± 0.002 | 0.979 ± 0.002 | 0.977 ± 0.005 | 0.979 ± 0.002 | 0.934 ± 0.006 | 157:30 | |
4 | 0.967 ± 0.005 | 0.967 ± 0.007 | 0.967 ± 0.005 | 0.967 ± 0.012 | 0.967 ± 0.006 | 0.905 ± 0.018 | 108:52 | |
(3) | 2 | 0.967 ± 0.008 | 0.968 ± 0.008 | 0.967 ± 0.008 | 0.954 ± 0.013 | 0.967 ± 0.008 | 0.870 ± 0.032 | 200:29 |
3 | 0.980 ± 0.002 | 0.980 ± 0.002 | 0.980 ± 0.002 | 0.977 ± 0.005 | 0.980 ± 0.002 | 0.938 ± 0.006 | 132:45 | |
4 | 0.965 ± 0.011 | 0.965 ± 0.011 | 0.965 ± 0.011 | 0.968 ± 0.014 | 0.965 ± 0.011 | 0.899 ± 0.032 | 118:05 | |
(3) | 2 | 0.972 ± 0.009 | 0.973 ± 0.009 | 0.972 ± 0.009 | 0.956 ± 0.013 | 0.972 ± 0.009 | 0.889 ± 0.036 | 324:54 |
3 | 0.971 ± 0.006 | 0.971 ± 0.006 | 0.971 ± 0.006 | 0.960 ± 0.008 | 0.971 ± 0.006 | 0.882 ± 0.024 | 196:53 | |
4 | 0.967 ± 0.009 | 0.971 ± 0.007 | 0.967 ± 0.009 | 0.979 ± 0.003 | 0.968 ± 0.009 | 0.900 ± 0.026 | 178:51 |
Data Type | Window (s) | Accuracy | Precision | Sensitivity | Specificity | Score | MCC | Runtime (min) |
---|---|---|---|---|---|---|---|---|
(3) | 2 | 0.979 ± 0.008 | 0.978 ± 0.009 | 0.979 ± 0.008 | 0.958 ± 0.013 | 0.978 ± 0.009 | 0.913 ± 0.034 | 103.09 |
3 | 0.980 ± 0.005 | 0.980 ± 0.005 | 0.980 ± 0.005 | 0.976 ± 0.007 | 0.980 ± 0.005 | 0.938 ± 0.015 | 43:36 | |
4 | 0.967 ± 0.005 | 0.967 ± 0.006 | 0.967 ± 0.005 | 0.967 ± 0.012 | 0.967 ± 0.006 | 0.905 ± 0.018 | 32:09 | |
(3) | 2 | 0.973 ± 0.008 | 0.973 ± 0.008 | 0.973 ± 0.008 | 0.956 ± 0.013 | 0.973 ± 0.008 | 0.893 ± 0.032 | 107:14 |
3 | 0.978 ± 0.003 | 0.978 ± 0.002 | 0.978 ± 0.003 | 0.976 ± 0.003 | 0.978 ± 0.003 | 0.931 ± 0.008 | 51:31 | |
4 | 0.969 ± 0.008 | 0.970 ± 0.008 | 0.969 ± 0.008 | 0.969 ± 0.013 | 0.969 ± 0.008 | 0.911 ± 0.024 | 24:04 | |
(3) | 2 | 0.983 ± 0.006 | 0.983 ± 0.006 | 0.983 ± 0.006 | 0.960 ± 0.012 | 0.983 ± 0.006 | 0.932 ± 0.026 | 64:56 |
3 | 0.975 ± 0.005 | 0.975 ± 0.005 | 0.975 ± 0.005 | 0.962 ± 0.009 | 0.975 ± 0.005 | 0.900 ± 0.019 | 32:55 | |
4 | 0.976 ± 0.008 | 0.978 ± 0.007 | 0.976 ± 0.008 | 0.983 ± 0.003 | 0.976 ± 0.008 | 0.925 ± 0.024 | 38:40 |
Data Type | Window (s) | Accuracy | Precision | Sensitivity | Specificity | Score | MCC | Runtime (min) |
---|---|---|---|---|---|---|---|---|
(3) | 2 | 0.981 ± 0.007 | 0.980 ± 0.007 | 0.981 ± 0.007 | 0.951 ± 0.015 | 0.980 ± 0.007 | 0.921 ± 0.029 | < 1 |
3 | 0.985 ± 0.003 | 0.985 ± 0.003 | 0.985 ± 0.003 | 0.979 ± 0.006 | 0.985 ± 0.003 | 0.953 ± 0.010 | < 1 | |
4 | 0.969 ± 0.006 | 0.969 ± 0.007 | 0.969 ± 0.006 | 0.967 ± 0.012 | 0.969 ± 0.007 | 0.911 ± 0.019 | < 1 | |
(3) | 2 | 0.977 ± 0.008 | 0.977 ± 0.008 | 0.977 ± 0.008 | 0.958 ± 0.012 | 0.977 ± 0.008 | 0.907 ± 0.032 | < 1 |
3 | 0.973 ± 0.008 | 0.975 ± 0.006 | 0.973 ± 0.008 | 0.974 ± 0.002 | 0.973 ± 0.007 | 0.917 ± 0.020 | < 1 | |
4 | 0.971 ± 0.008 | 0.972 ± 0.008 | 0.971 ± 0.008 | 0.967 ± 0.008 | 0.971 ± 0.008 | 0.917 ± 0.023 | < 1 | |
(3) | 2 | 0.983 ± 0.007 | 0.983 ± 0.007 | 0.983 ± 0.007 | 0.960 ± 0.012 | 0.983 ± 0.007 | 0.932 ± 0.030 | < 1 |
3 | 0.977 ± 0.003 | 0.977 ± 0.004 | 0.977 ± 0.003 | 0.962 ± 0.008 | 0.976 ± 0.004 | 0.905 ± 0.015 | < 1 | |
4 | 0.976 ± 0.011 | 0.978 ± 0.009 | 0.976 ± 0.011 | 0.979 ± 0.004 | 0.976 ± 0.010 | 0.925 ± 0.032 | < 1 |
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Ashfaque Mostafa, T.; Soltaninejad, S.; McIsaac, T.L.; Cheng, I. A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction. Sensors 2021, 21, 6446. https://doi.org/10.3390/s21196446
Ashfaque Mostafa T, Soltaninejad S, McIsaac TL, Cheng I. A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction. Sensors. 2021; 21(19):6446. https://doi.org/10.3390/s21196446
Chicago/Turabian StyleAshfaque Mostafa, Tahjid, Sara Soltaninejad, Tara L. McIsaac, and Irene Cheng. 2021. "A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction" Sensors 21, no. 19: 6446. https://doi.org/10.3390/s21196446
APA StyleAshfaque Mostafa, T., Soltaninejad, S., McIsaac, T. L., & Cheng, I. (2021). A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction. Sensors, 21(19), 6446. https://doi.org/10.3390/s21196446