The Role of Deep Learning and Gait Analysis in Parkinson’s Disease: A Systematic Review
Abstract
:1. Introduction
2. Materials and Methods
- Focus on PD with no known cause (idiopathic PD);
- Gait analysis with a clear description of the experimental protocol to study PD;
- Discussion on the details of the neural network used and the performance indices utilized.
- Non-English articles, book chapters, and reviews;
- Articles with unavailable full text;
- Articles related to a healthy control (HC);
- Articles unrelated to DL;
- Articles with an absence of focus on PD and an absence of gait analysis.
3. Results and Discussions
3.1. Characteristics of the Included Studies
3.2. Characteristics of the Datasets
3.3. Experimental Approaches Adopted
3.3.1. Wearable Sensors
3.3.2. Vision-Based Motion Capture System
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ALS | amyotrophic lateral sclerosis |
ARR | Anova with Recursive Reduction |
ASA | arm swing asymmetry |
AUC | Area Under the Curve |
CNN | Convolutional Neural Network |
CPM | Convolutional Pose Machine |
DL | Deep Learning |
DNN | Deep Neural Network |
FoG | Freezing of Gait |
FRL | frequency representation learning |
GAD | graph adaptive network block |
GCNN | graph convolutional neural network |
GRU | Gated Recurrent Unit |
HC | healthy control |
HD | Huntington’s Disease |
HPE | Human Pose Estimation |
LSTM | long short-term memory |
MDS-UPDRS | Movement Disorder Society-Unified Parkinson’s Disease Rating Scale |
ML | Machine Learning |
MS | Multiple Sclerosis |
NDD | neurodegenerative disease |
PD | Parkinson’s Disease |
P-P | peak-to-peak |
PRISMA | Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
RFdGAD | robust frequency-domain-based graph adaptive network |
RNN | Recurrent Neural Network |
TDPT-GT | Three-Dimensional Pose Tracker for Gait Test |
vGRF | Vertical Ground Reaction Force |
WM-STGCN | Weighted adjacency matrix with virtual connection and Multi-scale temporal convolution in a Spatiotemporal Graph Convolution Networkgraph convolutional neural network |
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---|---|---|---|---|---|---|---|---|
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Erdaş et al., 2023, [50] | Detect and assess the severity of PD, HD, and ALS using gait data and dynamics through various ML methods, including pure ML and the one-dimensional CNN method, along with ensemble techniques, like voting and stacking to enhance overall performance. | Public (The Gait Dynamics in Neuro-Degenerative Disease Database of PhysioNet) | 36\28 | HC 47, PD 61.5, HD 54, ALS 53 | One ground force sensor on each foot | Kinematic measures | Multi-layer perceptron, random forest, extra trees, and k-nearest neighbor as classification; voting and stacking, and 1-dimensional CNN as regression | Random forest: accuracy 58.61%; precision 58.42%; recall 58.49%; F1 58.45% 1D-CNN: Accuracy 68.11%; Precision 69.05%; Recall 68.16%; F1 67.77% |
Lin et al., 2020, [51] | Develop aDL-based algorithm (CNN) for detecting NDD disorders (PD, HD, ALS) using a recurrence plot derived from vertical ground reaction force signals. | Public (The Gait Dynamics in Neuro-Degenerative Disease Database of PhysioNet) | 36\28 | HC 47, PD 61.5, HD 54, ALS 53 | One ground force sensor on each foot | Time–frequency spectrograms | AlexNet CNN | Accuracy > 95%; sensitivity > 90%; specificity > 90%; AUC > 90% |
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Vásquez-Correa et al., 2019, [53] | Multimodal analysis of motor abilities in patients with PD through the use of DL architectures based on TFR and CNN by integrating information from vocal, writing, and gait signals. The proposed method aimed to model the difficulty patients face in initiating and stopping movements of the upper and lower limbs, as well as in language. | Private | 47\36 | 54.5 | eGaIT system | Time–frequency spectrograms and Spatio-temporal features | Individual CNNs are trained for each modality | Accuracy: 97.6%; AUC: 98.8% (with the fusion of the three bio-signals) |
Carvajal et al., 2022, [54] | Classify subjects with PD compared with HCs using three different DL architectures: CNN, GRU, and a combination of CNN and GRU, which are considered state-of-the-art in gait analysis. Two subgroups of HC were included: elderly (EHC) and young (YHC). | Private | 68\66 | 52 | eGaIT system | Spatio-temporal gait features (segment of 3 s of raw time series) | CNN, GRU, and CNN + GRU | Accuracy: CNN 82.7% (YHC group), 82.4% (EHC group); 82.7% (classification of PD vs. EHC), 92.1% (classification of PD vs. YHC); CNN and GRU 83.7%; 92.7% (classification of PD vs. EHC/YHC) Sensitivity > 70%; specificity > 72%; AUC > 80% |
Ma et al., 2023, [55] | Develop of an explainable learning architecture (XGBoost and CNN) that integrates mechanisms of DL and ML, including data selection, feature evaluation, and data balancing, for gait detection in patients with PD. | Public (Ga, Ju, and Si datasets) | 68\98 | 63.3 | 8 ground force sensor on each foot | The force domain, the peak domain (mean, standard deviation, max and min value of peak data) and the abnormality domain | XGBoost and CNN | XGBoost: 97.32%; CNN: 98.4% |
Zhong et al., 2023, [56] | Develop of a robust and innovative graphical adaptive network based on the frequency domain (RFdGAD) to identify PD through gait information, specifically vertical ground reaction force signals recorded by foot sensors. | Public (Ga, Ju, and Si datasets) | 68\98 | 117 < 70 yo, 49 > 70 yo | 8 ground force sensor on each foot | Time and frequency domain features | RFdGAD | Accuracy > 75%, F1 > 70% |
Aşuroğlu et al., 2022, [57] | Develop a hybrid DL model to predict the severity of PD. In this combined DL approach, the temporal and frequency features of ground reaction force sensors are converted and used as input for the CNN + LWRF architecture. | Public (Physionet Gait in Parkinson’s Disease) | 68\98 | 63.3 | 8 ground force sensor on each foot | Time and frequency domain features | CNN and LWRF | Accuracy: 99.5%; Sensitivity: 98.7%; Specificity: 99.1% |
Setiawan et al., 2021, [58] | Develop an innovative algorithm for detecting and classifying the severity of PD using DL approaches and relying on signals of vertical ground reaction force. Various types of CNNs were employed as classifiers. | Public (Ga, Ju, and Si datasets) | 68\98 | 63.3 | 8 ground force sensor on each foot | Time–frequency spectrograms | CNN, AlexNet, ResNet-50, ResNet-101, and GoogLeNet | Multi-class classification: accuracy 98.16%, 98.24%, 98.27% (Ga, Ju, Si datasets); sensitivity 98.15%, 98.06%, 97.73% (Ga, Ju, Si datasets), specificity 98.16%, 98.38%, 98.76% (Ga, Ju, Si datasets), AUC 98% (Ga, Ju, Si datasets); Two-class classification: accuracy 99.11%, 99.01%, 98.56% (Ga, Ju, Si datasets); sensitivity 99.77%, 98.94%, 98.85% (Ga, Ju, Si datasets), specificity 98.80%, 99.04%, 98.41% (Ga, Ju, Si datasets), AUC 99% (Ga, Ju, Si datasets) |
Xia et al., 2020, [59] | Implement of a gait assessment method to provide a binary classification between PD-associated and normal walks, as well as the severity level of the disease. The proposed system adopts a dual-modal model based on DL, where both left and right walks are separately modeled using a CNN, followed by a LSTM network. | Public (Ga, Ju, and Si datasets) | 68\98 | 63.3 | 8 ground force sensor on each foot | Force vs. time curve | CNN-LSTM | Predict PD gaits (Ga dataset): accuracy 99.31, sensitivity 99.35%; specificity 99.23%; Classify PD patients with different H&Y scores (Si dataset): accuracy 99.01% |
El Maachi et al., 2020, [60] | Develop an advanced PD detection system based on DL techniques to analyze gait information. The approach of 1D-CNN was adopted to build a classifier. The model processes 18 1D vertical ground reaction force signals from foot sensors. | Public (Physionet Gait in Parkinson’s Disease) | 68\98 | 63.3 | 8 ground force sensor on each foot | Spatio-temporal gait features | 1D-ConvNet | Predict PD: accuracy 98.7%; sensitivity 98.1%; specificity 100%; Predict Parkinson’s severity: accuracy 85.3%; precision 87.3% |
Reference | Aim of the Study | Type of Dataset | Number of Subjects (Female\Male) | Mean Age of the Subjects | Acquisition Device | Features | Analytical Methods | Main Results |
---|---|---|---|---|---|---|---|---|
Eguchi et al., 2023, [61] | Propose a CNN to estimate UPDRS severity scores and subscores of axial symptoms, bradykinesia, rigidity, and tremor. | Private | 44\30 | 63.4 ± 8.2 | Video camera | Spatio-temporal gait features | ECO-Lite CNN | The goodness of the model the coefficient of determination was evaluated. In particular, axial symptoms, bradykinesia, rigidity, and tremor: 0.59, 0.77, 0.56, and 0.46, respectively |
Rupprechter et al., 2021, [62] | Investigate a markerless motion capture system using videos as a component of routine gait assessments to evaluate the motor performances of PD patients. | Private | Not specified | Not specified | Video camera | Spatio-temporal gait features and arm swing | OpenPose | Correlation coefficient 0.80 |
Zanela et al., 2022, [63] | Evaluate gait impairments and assessing the disease burden by employing human estimation pose system OpenPose and a stereoscopic device. | Private | 4\6 | 62.7 ± 13.2 | Video camera | Spatial coordinates | OpenPose | The authors demonstrated good effectiveness of the proposed system in extracting the main features concerning the PD patients’ gaits |
Abe et al., 2022, [64] | Investigate the peak-to-peak data regarding the left and right arm swing in PD patients using OpenPose-based gait analysis and video acquired by a smartphone camera. | Private | 28 (6\13 PD) | not specified | Smartphone or consumer video camera | P-P (peak-to-peak) Left, P-P right, ASA (arm swing asymmetry) | OpenPose | P-P = 72.7% and ASA = 82.4% of accuracy, respectively |
Guayacán et al., 2022, [65] | Proposing a markerless strategy, DensePose CNN, for the analysis of body segment kinematics to obtain PD characterisation during walking, captured in sagittal video sequences using a single camera. | Private | 10\12 | 72.3 ± 7.4 | Videocamera | Kinematic measures | DensePose | Accuracy: 99.6% for lower-limbs |
Zhang et al., 2023, [66] | Propose a method for recognising the gait of PD patients. First, skeletal features were extracted from the videos using OpenPose. Then, they used a weighted adjacency matrix with virtual connection and multi-scale temporal convolution in a spatiotemporal graph convolution network graph convolutional neural network (WM-STGCN), which provides an efficient mechanism for direct learning of joint trajectories. | Private | 50 | Not specified | Smartphone accelerometer and gyroscope sensors (Samsung) | Spatio-temporal gait features | OpenPose + WM–STGCN | Accuracy: 87.1%, sensitivity: 86.7%, specificity: 87.5%, precision: 86.7% |
Michael H. Li et al., 2018, [67] | Propose a DL-based pose estimation algorithm for a CPM for extracting 15 gait kinematic features in order to train an ML model for detecting and estimating the severity of levodopa-induced dyskinesia and parkinsonisms. | Private | 9 (4\5) | 64 | A single camera | Kinematic measures | CPM and random forest classifier | Unified Dyskinesia Rating Scale (UDysRS) and Unified Parkinson’s Disease Rating Scale (UPDRS) scores were predicted with r = 0.741 and 0.530, respectively |
Sato et al., 2019, [68] | Propose a method for quantifying gait features and detecting FOG events by extracting the cadence from normal and parkinsonian gait movies recorded with a home video camera. | Public (CASIA Dataset-B) | 119 | HC 20, PD 65 | Video camera | Spatio-temporal gait features | OpenPose | Comparison between the cadence laterally viewed movie and the frontally viewed movie of the same gait. Good consistency between them: R = 0.754, RMSE = 7.24, and MAE = 6.05 |
Hu et al., 2019, [69] | Propose a promised novel graph convolutional neural network (GCNN) using the FoG detection method. | Private | 45 | Not specified | Video camera | Spatial-temporal gait features | GCNN | Accuracy > 87%, sensitivity > 80%, specificity > 79%, AUC > 0.80 |
Kaur et al., 2022, [70] | Investigate the effectiveness of a vision-based model for classifying gait strides in persons with different neurological disorders. By segmenting the gait steps and identifying heel strikes, several DL algorithms, such as CNN and RNN, were trained. | Private | 14\19 | 66 ± 5 MS, 68 ± 9 PD, 63 ± 9 HC | Video camera | Segmentation of the gait steps and heel strike | 4 convolutional architectures (CNN, ResNet, MSResNet, TCN), 3 recurrent architectures (RNN, LSTM, GRU) | In single task, the RNN resulted in the highest accuracy and AUC of 78.1% and 0.87, and the CNN had highest accuracy of 75% in dual trials. |
Gül et al., 2023, [71] | Propose a hybrid system based on CNN and use videos of 28 patients taken from front, back, and both sides during walking in order to distinguish different neurological disorders. | Private | 28 | Not specified | Video camera | Joint coordinates | CNN | Accuracy, sensitivity, specificity > 80% |
Iseki et al., 2023, [72] | Propose a markerless motion capture system (Three-Dimensional Pose Tracker for Gait Test (TDPT-GT)) based on ML combined with an iPhone camera to distinguish a pathological gait from a control gait. | Private | 131\143 | PD: 74.5 ± 7.8, HC: 72.9 ± 11.1 | TDPT-GT (iPhone camera) | Spatio-temporal gait features | Light GBM | Accuracy > 70%; sensitvity > 63%, specificity > 72%, AUC > 0.77 |
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Franco, A.; Russo, M.; Amboni, M.; Ponsiglione, A.M.; Di Filippo, F.; Romano, M.; Amato, F.; Ricciardi, C. The Role of Deep Learning and Gait Analysis in Parkinson’s Disease: A Systematic Review. Sensors 2024, 24, 5957. https://doi.org/10.3390/s24185957
Franco A, Russo M, Amboni M, Ponsiglione AM, Di Filippo F, Romano M, Amato F, Ricciardi C. The Role of Deep Learning and Gait Analysis in Parkinson’s Disease: A Systematic Review. Sensors. 2024; 24(18):5957. https://doi.org/10.3390/s24185957
Chicago/Turabian StyleFranco, Alessandra, Michela Russo, Marianna Amboni, Alfonso Maria Ponsiglione, Federico Di Filippo, Maria Romano, Francesco Amato, and Carlo Ricciardi. 2024. "The Role of Deep Learning and Gait Analysis in Parkinson’s Disease: A Systematic Review" Sensors 24, no. 18: 5957. https://doi.org/10.3390/s24185957
APA StyleFranco, A., Russo, M., Amboni, M., Ponsiglione, A. M., Di Filippo, F., Romano, M., Amato, F., & Ricciardi, C. (2024). The Role of Deep Learning and Gait Analysis in Parkinson’s Disease: A Systematic Review. Sensors, 24(18), 5957. https://doi.org/10.3390/s24185957