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AI for Gait-Based Neurodegenerative Disease Diagnosis (AI4NDD)

A professionally curated list of resources (papers, data, etc.) on AI4NDD, which comprehensively and systematically summarize the recent advances of this filed of research.

We will continuously update this list with the latest resources. Should you find any missed resources or errors, please feel free to open an issue or contribute a pull request.

Survey Paper

A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis (Arxiv)

By Haocong Rao, Minlin Zeng, Xuejiao Zhao, and Chunyan Miao.

Archives and Resources

• Overview of Worldwide Effects of NDs (No. of Patients)

• Overview of Worldwide Research (No. of Studies)

• Framework Overview

• Overview of 164 Included Studies (AI Types, NDs Types, Sample Sizes, Gait Data Types, etc.)

• Statistics of Different Types of NDs Studies and AI Models

• Key Information Summary of Different Gait-Based NDs Studies

  • Key Information Summary of Gait-Based PD Studies
  • Key Information Summary of Gait-Based AD Studies)
  • Key Information Summary of Gait-Based ALS Studies)
  • Key Information Summary of Gait-Based HD Studies)
  • Key Information Summary of Gait-Based MS Studies
  • Key Information Summary of Gait-Based Combinatorial Studies (PD, ALS, HD)

• Studies by Different Neurodegenerative Diseases

  • Parkinson's Disease (PD) (Papers with Quality Scores larger than 0.6)
    • Studies by Different AI Model Types in PD Diagnosis
      • Conventional Machine Learning Models (C-ML)
      • Conventional Deep Learning Models (C-DL)
      • Advanced Deep Learning Models (A-DL)
  • Huntington’s Disease (HD)
  • Multiple Sclerosis (MS)
  • Alzheimer’s Disease (AD)
  • Amyotrophic Lateral Sclerosis (ALS)
  • Combinatorial Diagnoses of Different Diseases

• Studies by Different Years

  • 2023
  • 2022
  • 2021
  • 2020
  • 2019
  • 2018
  • 2017
  • 2016
  • 2015
  • 2014
  • 2013
  • 2012

• List of All Included Papers (164 Papers)

• Taxonomy of Gait Data

• Research Vision on 3D Skeleton based NDs Diagnosis

Overview of Worldwide Effects

Overview of Worldwide Studies

Framework Overview

Overview of All Included Studies

Statistics of Different Types of NDs Studies and AI Models

Number of studies related to different NDs (PD, HD, MS, AD, ALS) and different model types (C-ML, C-DL, A-DL). “Combinatorial” denotes studies that simultaneously contain diagnosis of multiple NDs including PD,
ALS, and HD. S_{Q} >0.6 indicates the number of high-quality studies.

Key Summary of Different Gait-Based Neurodegenerative Diseases (NDs) Studies

Key Summary of Gait-Based PD Studies

Key Summary of Gait-Based AD Studies

Key Summary of Gait-Based ALS Studies

Key Summary of Gait-Based HD Studies

Key Summary of Gait-Based MS Studies

Key Summary of Combinatorial Studies (PD, ALS, HD)

Studies by Different Neurodegenerative Diseases

Parkinson's Disease (PD)

Studies by Different AI Model Types in PD Diagnosis

Conventional Machine Learning Models (C-ML)

• Hmm for classification of parkinson’s disease based on the raw gait data (Journal of medical systems, 2014)
• Motion tracking and gait feature estimation for recognising parkinson’s disease using ms kinect (Biomedical engineering online, 2015)
• Parkinson’s disease classification using gait analysis via deterministic learning (Neuroscience letters, 2016)
• Detection of parkinson’s disease by shifted one dimensional local binary patterns from gait (Expert Systems with Applications, 2016)
• Vertical ground reaction force marker for parkinson’s disease (PloS one, 2017)
• An automatic non-invasive method for Parkinson’s disease classification (Computer methods and programs in biomedicine, 2017)
• Comparative motor pre-clinical assessment in Parkinson’s disease using supervised machine learning approaches (Annals of biomedical engineering, 2018)
• A validation study of freezing of gait (FOG) detection and machine-learning-based FOG prediction using estimated gait characteristics with a wearable accelerometer (Sensors, 2018)
• A computer-aided diagnosis system for the early detection of neurodegenerative diseases using linear and non-linear analysis (2018 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME), IEEE, 2018)
• Data-driven based approach to aid Parkinson’s disease diagnosis (Sensors, 2019)
• Parkinson’s disease detection from gait patterns (2019 E-Health and Bioengineering Conference (EHB), IEEE, 2019)
• Classification of gait patterns between patients with Parkinson’s disease and healthy controls using phase space reconstruction (PSR), empirical mode decomposition (EMD) and neural networks (Neural Networks, 2019)
• Models of Parkinson’s disease patient gait (IEEE Journal of Biomedical and Health Informatics, 2019)
• Unsupervised pre-trained models from healthy ADLs improve Parkinson’s disease classification of gait patterns (2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, 2020)
• Supervised machine learning based gait classification system for early detection and stage classification of Parkinson’s disease (Applied Soft Computing, 2020)
• Data-driven gait analysis for diagnosis and severity rating of Parkinson’s disease (Medical Engineering & Physics, 2021)
• Inter-limb time-varying singular value: a new gait feature for Parkinson’s disease detection and stage classification (Measurement, 2021)
• Design and implementation of ML model for early diagnosis of Parkinson’s disease using gait data analysis in IoT environment (International Journal of Advanced Computer Science and Applications, 2022)
• Gait classification for early detection and severity rating of Parkinson’s disease based on hybrid signal processing and machine learning methods (Cognitive Neurodynamics, 2022)

Conventional Deep Learning Models (C-DL)

• Parkinson's disease classification using wavelet transform based feature extraction of gait data (2017 International Conference on Circuit Power and Computing Technologies (ICCPCT), IEEE, 2017)
• Parkinson’s disease monitoring from gait analysis via foot-worn sensors (Biocybernetics and Biomedical Engineering, 2018)
• A hybrid spatio-temporal model for detection and severity rating of Parkinson’s disease from gait data (Neurocomputing, 2018)
• LSTM and convolution networks exploration for Parkinson’s diagnosis (2019 IEEE Colombian Conference on Communications and Computing (COLCOM), IEEE, 2019)
• A dual-modal attention-enhanced deep learning network for quantification of Parkinson’s disease characteristics (IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019)
• A novel computer vision based gait analysis technique for normal and parkinson’s gaits classification (2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC / PiCom / CBDCom / CyberSciTech, IEEE, 2020)
• Deep 1d-convnet for accurate Parkinson disease detection and severity prediction from gait (Expert Systems with Applications, 2020)
• Parkinson’s disease diagnosis and severity assessment using ground reaction forces and neural networks (Frontiers in Physiology, 2020)
• Classification of Parkinson’s disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data driven approach (Journal of neuroengineering and rehabilitation, 2020)
• Biomechanical parameters assessment for the classification of Parkinson disease using bidirectional long short-term memory (2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, 2020)
• Deep learning identifies digital biomarkers for self-reported Parkinson’s disease (Patterns, 2020)
• Detection of Parkinson’s disease from gait using neighborhood representation local binary patterns (Biomedical Signal Processing and Control, 2020)
• Automatic and non-invasive Parkinson’s disease diagnosis and severity rating using LSTM network (Applied Soft Computing, 2021)
• Classification of gait in Parkinson’s disease using single sensors (2021 International Conference on Electrical Computer and Energy Technologies (ICECET), IEEE, 2021)
• Spatiotemporal ground reaction force analysis using convolutional neural networks to analyze Parkinsonian gait (arXiv preprint arXiv:2102.00628, 2021)
• Accelerating diagnosis of Parkinson’s disease through risk prediction (BMC Neurology, 2021)
• Classification of parkinson’s disease patients—a deep learning strategy (Electronics, 2022)
• Gait detection from a wrist-worn sensor using machine learning methods: a daily living study in older adults and people with Parkinson’s disease (Sensors, 2022)
• AI computing as ubiquitous healthcare solution: Predict Parkinson’s for large masses in society (IEEE Transactions on Computational Social Systems, 2022)
• Explainable deep learning architecture for early diagnosis of Parkinson’s disease (Soft Computing, 2023)
• A novel plantar pressure analysis method to signify gait dynamics in Parkinson’s disease (Mathematical Biosciences and Engineering, 2023)

Advanced Deep Learning Models (A-DL)

• Recognizing parkinsonian gait pattern by exploiting fine-grained movement function features (ACM Transactions on Intelligent Systems and Technology (TIST), 2016)
• Q-backpropagated Time Delay Neural Network (Q-BTDNN) for predicting gait disturbances in PD (Journal of biomedical informatics, 2016)
• Deep residual learning for image recognition (Proceedings of the IEEE conference on computer vision and pattern recognition 2016)
• Tensor decomposition of gait dynamics in Parkinson’s disease (IEEE Transactions on Biomedical Engineering, 2017)
• Spatial temporal graph convolutional networks for skeleton-based action recognition (Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2018)
• Gait spatiotemporal signal analysis for Parkinson’s disease detection and severity rating (IEEE Sensors Journal, 2020)
• Classification of parkinson’s disease - associated gait patterns (Research in Intelligent and Computing in Engineering: Select Proceedings of RICE 2020, Springer, 2021)
• Multimodal gait recognition for neurodegenerative diseases(IEEE transactions on cybernetics, 2021)
• Design and implementation of ml model for early diagnosis of parkinson’s disease using gait data analysis in iot environment(International Journal of Advanced Computer Science and Applications, 2022)
• PD-resnet for classification of parkinson’s disease from gait (IEEE Journal of Translational Engineering in Health and Medicine, 2022)
• A deep learning approach for parkinson’s disease severity assessment(Health and Technology, 2022)
• A riemannian deep learning representation to describe gait parkinsonian locomotor patterns (2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, 2022)
• SlowFast GCN network for quantification of parkinsonian gait using 2D videos (2022 12th International Conference on CYBER Technology in Automation Control and Intelligent Systems (CYBER), IEEE, 2022)
• WM-STGCN: A novel spatiotemporal modeling method for parkinsonian gait recognition (Sensors, 2023)
• Static-dynamic temporal networks for parkinson’s disease detection and severity prediction (IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023)

Huntington’s Disease (HD)

• Assessment of gait spatio-temporal parameters in neurological disorders using wearable inertial sensors (N.A., 2015)
• Machine learning framework for gait classification using inertial sensors: Application to elderly, post-stroke and huntington’s disease patients(Sensors, 2016)
• Meta-classifiers in Huntington's disease patients classification using iPhone's movement sensors placed at the ankles (IEEE Access, 2018)
• A deep learning-based approach for gait analysis in Huntington disease (MEDINFO 2019: Health and Wellbeing e-Networks for All, IOS Press, 2019)
• An automatic method for identifying Huntington's disease using gait dynamics (2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), IEEE, 2019, pp. 1659–1663)
• Diagnosing Huntington's disease through gait dynamics (Advances in Visual Computing: 14th International Symposium on Visual Computing ISVC 2019, Springer, 2019)
• Machine learning-based method for Huntington's disease gait pattern recognition (Neural Information Processing: 26th International Conference, ICONIP 2019, Springer, 2019)

Multiple Sclerosis (MS)

• Clinical human gait classification: extreme learning machine approach (2019 1st International Conference on Advances in Science, Engineering, and Robotics Technology (ICASERT), IEEE, 2019)
• Predicting multiple sclerosis from gait dynamics using an instrumented treadmill: a machine learning approach (IEEE Transactions on Biomedical Engineering, 2020)
• Smartphone- and smartwatch-based remote characterisation of ambulation in multiple sclerosis during the two-minute walk test (IEEE Journal of Biomedical and Health Informatics, 2020)
• Using machine learning algorithms for identifying gait parameters suitable to evaluate subtle changes in gait in people with multiple sclerosis (Brain Sciences, 2021)
• Interpretable deep learning for the remote characterisation of ambulation in patients with Parkinson's disease using smartphone sensors(Scientific Reports, 2021)
• Machine learning classification of multiple sclerosis patients based on raw data from an instrumented walkway (BioMedical Engineering OnLine, 2022)

Alzheimer’s Disease (AD)

• Application of machine learning in postural control kinematics for the diagnosis of Alzheimer’s disease (Computational Intelligence and Neuroscience, 2016)
• Alzheimer’s disease classification with a cascade neural network (Frontiers in Public Health, 2020)
• Detection of mild cognitive impairment and Alzheimer’s disease using dual-task gait assessments and machine learning (Biomedical Signal Processing and Control, vol. 64, p. 102249, 2021)
• Alzheimer’s disease detection using comprehensive analysis of timed up and go test via Kinect v.2 camera and machine learning (IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 1589–1600, 2022)
• Early alzheimer’s disease diagnosis using wearable sensors and multilevel gait assessment: A machine learning ensemble approach (IEEE Sensors Journal, 2023)

Amyotrophic Lateral Sclerosis (ALS)

• A novel approach for analysis of altered gait variability in amyotrophic lateral sclerosis (Medical & biological engineering & computing, 2016)
• An effective andautomatic method to aid the diagnosis of amyotrophic lateral sclerosisusing one minute of gait signal (2020 IEEE International Conferenceon Bioinformatics and Biomedicine (BIBM). IEEE, 2020)
• Automatic classificationof amyotrophic lateral sclerosis through gait dynamics (2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2021)

Combinatorial Diagnoses of Different Diseases

• Automatic diagnosis of neuro-degenerative diseases using gait dynamics (Measurement, 2012)
• Hybrid correlation - neural network synergy for gait signal classification (Advances in Heuristic Signal Processing and Applications, Springer, 2013)
• Classification of gait rhythm signals between patients with neuro-degenerative diseases and normal subjects: Experiments with statistical features and different classification models (Biomedical Signal Processing and Control, 2015)
• Towards automated human gait disease classification using phase space representation of intrinsic mode functions (Automated Visual Inspection and Machine Vision II, SPIE, 2017)
• Statistical energy values and peak analysis (sep) approach for detection of neurodegenerative diseases (2017 World Congress on Computing and Communication Technologies (WCCCT), IEEE, 2017)
• Neurodegenerative disease classification using nonlinear gait signal analysis (Electrical Engineering (ICEE), Iranian Conference on. IEEE, 2018)
• String grammar unsupervised possibilistic fuzzy c-medians for gait pattern classification in patients with neurodegenerative diseases (Computational Intelligence and Neuroscience, 2018)
• Cassification of neurodegenerative diseases via topological motion analysis—a comparison study for multiple gait fluctuations (Ieee Access, 2020)
• Recognition of neurodegenerative diseases with gait patterns using double feature extraction methods (2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE, 2020)
• Development of a neurodegenerative disease gait classification algorithm using multiscale sample entropy and machine learning classifiers (Entropy, 2020)
• Evaluation of vertical ground reaction forces pattern visualization in neurodegenerative diseases identification using deep learning and recurrence plot image feature extraction (Sensors, 2020)
• A robust cost-effective and non-invasive computer-aided method for diagnosis three types of neurodegenerative diseases with gait signal analysis (Measurement, 2020)
• Evaluation of gait behavior with state space vectors for classification of neurodegenerative diseases (EJONS International Journal on Mathematic Engineering and Natural Sciences, 2020)
• Multimodal gait recognition for neurodegenerative diseases (IEEE transactions on cybernetics, 2021)
• Identification of neurodegenerative diseases based on vertical ground reaction force classification using time-frequency spectrogram and deep learning neural network features (Brain Sciences, 2021)
• Development of neurodegenerative diseases’ gait classification algorithm using convolutional neural network and wavelet coherence spectrogram of gait synchronization (IEEE Access, 2022)
• Petri net transition times as training features for multiclass models to support the detection of neurodegenerative diseases (Biomedical Physics & Engineering Express, 2022)
• Using machine learning algorithms for neurodegenerative disease gait classification (Ingenierias USBMed, 2023)
• Deep learning-based classification of neurodegenerative diseases using gait dataset: A comparative study (Proceedings of the 2023 International Conference on Robotics, Control and Vision Engineering, 2023)

Studies by Different Years

2023

• Using machine learning algorithms for neurodegenerative disease gait classification (Ingenierias USBMed, 2023)
• Deep learning-based classification of neurodegenerative diseases using gait dataset: A comparative study (Proceedings of the 2023 International Conference on Robotics, Control and Vision Engineering, 2023)
• Early alzheimer’s disease diagnosis using wearable sensors and multilevel gait assessment: A machine learning ensemble approach (IEEE Sensors Journal, 2023)
• Explainable deep learning architecture for early diagnosis of Parkinson’s disease (Soft Computing, 2023)
• A novel plantar pressure analysis method to signify gait dynamics in Parkinson’s disease (Mathematical Biosciences and Engineering, 2023)
• WM-STGCN: A novel spatiotemporal modeling method for parkinsonian gait recognition (Sensors, 2023)
• Static-dynamic temporal networks for parkinson’s disease detection and severity prediction (IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023)

2022

• Development of neurodegenerative diseases’ gait classification algorithm using convolutional neural network and wavelet coherence spectrogram of gait synchronization (IEEE Access, 2022)
• Petri net transition times as training features for multiclass models to support the detection of neurodegenerative diseases (Biomedical Physics & Engineering Express, 2022)
• Design and implementation of ml model for early diagnosis of parkinson’s disease using gait data analysis in iot environment (International Journal of Advanced Computer Science and Applications, 2022)
• PD-resnet for classification of parkinson’s disease from gait (IEEE Journal of Translational Engineering in Health and Medicine, 2022)
• A deep learning approach for parkinson’s disease severity assessment (Health and Technology, 2022)
• A riemannian deep learning representation to describe gait parkinsonian locomotor patterns (2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, 2022)
• Alzheimer’s disease detection using comprehensive analysis of timed up and go test via Kinect v.2 camera and machine learning (IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 1589–1600, 2022)
• Machine learning classification of multiple sclerosis patients based on raw data from an instrumented walkway (BioMedical Engineering OnLine, 2022)
• Design and implementation of ML model for early diagnosis of Parkinson’s disease using gait data analysis in IoT environment (International Journal of Advanced Computer Science and Applications, 2022)
• Gait classification for early detection and severity rating of Parkinson’s disease based on hybrid signal processing and machine learning methods (Cognitive Neurodynamics, 2022)
• Classification of parkinson’s disease patients—a deep learning strategy (Electronics, 2022)
• Gait detection from a wrist-worn sensor using machine learning methods: a daily living study in older adults and people with Parkinson’s disease (Sensors, 2022)
• AI computing as ubiquitous healthcare solution: Predict Parkinson’s for large masses in society (IEEE Transactions on Computational Social Systems, 2022)
• SlowFast GCN network for quantification of parkinsonian gait using 2D videos (2022 12th International Conference on CYBER Technology in Automation Control and Intelligent Systems (CYBER), IEEE, 2022)

2021

• Identification of neurodegenerative diseases based on vertical ground reaction force classification using time-frequency spectrogram and deep learning neural network features (Brain Sciences, 2021)
• Multimodal gait recognition for neurodegenerative diseases (IEEE transactions on cybernetics, 2021)
• Automatic classificationof amyotrophic lateral sclerosis through gait dynamics (2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2021)
• Detection of mild cognitive impairment and Alzheimer’s disease using dual-task gait assessments and machine learning (Biomedical Signal Processing and Control, vol. 64, p. 102249, 2021)
• Using machine learning algorithms for identifying gait parameters suitable to evaluate subtle changes in gait in people with multiple sclerosis (Brain Sciences, 2021)
• Interpretable deep learning for the remote characterisation of ambulation in patients with Parkinson's disease using smartphone sensors (Scientific Reports, 2021)
• Classification of parkinson’s disease - associated gait patterns (Research in Intelligent and Computing in Engineering: Select Proceedings of RICE 2020, Springer, 2021)
• Automatic and non-invasive Parkinson’s disease diagnosis and severity rating using LSTM network (Applied Soft Computing, 2021)
• Classification of gait in Parkinson’s disease using single sensors (2021 International Conference on Electrical Computer and Energy Technologies (ICECET), IEEE, 2021)
• Data-driven gait analysis for diagnosis and severity rating of Parkinson’s disease (Medical Engineering & Physics, 2021)
• Inter-limb time-varying singular value: a new gait feature for Parkinson’s disease detection and stage classification (Measurement, 2021)
• Accelerating diagnosis of Parkinson’s disease through risk prediction (BMC Neurology, 2021)
• Spatiotemporal ground reaction force analysis using convolutional neural networks to analyze Parkinsonian gait (arXiv preprint arXiv:2102.00628, 2021)
• Multimodal gait recognition for neurodegenerative diseases (IEEE transactions on cybernetics, 2021)

2020

• Cassification of neurodegenerative diseases via topological motion analysis—a comparison study for multiple gait fluctuations (Ieee Access, 2020)
• Recognition of neurodegenerative diseases with gait patterns using double feature extraction methods (2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE, 2020)
• Development of a neurodegenerative disease gait classification algorithm using multiscale sample entropy and machine learning classifiers (Entropy, 2020)
• Evaluation of vertical ground reaction forces pattern visualization in neurodegenerative diseases identification using deep learning and recurrence plot image feature extraction (Sensors, 2020)
• A robust cost-effective and non-invasive computer-aided method for diagnosis three types of neurodegenerative diseases with gait signal analysis (Measurement, 2020)
• Evaluation of gait behavior with state space vectors for classification of neurodegenerative diseases (EJONS International Journal on Mathematic Engineering and Natural Sciences, 2020)
• Alzheimer’s disease classification with a cascade neural network (Frontiers in Public Health, 2020)
• An effective andautomatic method to aid the diagnosis of amyotrophic lateral sclerosisusing one minute of gait signal (2020 IEEE International Conferenceon Bioinformatics and Biomedicine (BIBM). IEEE, 2020)
• Predicting multiple sclerosis from gait dynamics using an instrumented treadmill: a machine learning approach (IEEE Transactions on Biomedical Engineering, 2020)
• Smartphone- and smartwatch-based remote characterisation of ambulation in multiple sclerosis during the two-minute walk test (IEEE Journal of Biomedical and Health Informatics, 2020)
• Unsupervised pre-trained models from healthy ADLs improve Parkinson’s disease classification of gait patterns (2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, 2020)
• Supervised machine learning based gait classification system for early detection and stage classification of Parkinson’s disease (Applied Soft Computing, 2020)
• A novel computer vision based gait analysis technique for normal and parkinson’s gaits classification (2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC / PiCom / CBDCom / CyberSciTech, IEEE, 2020)
• Deep 1d-convnet for accurate Parkinson disease detection and severity prediction from gait (Expert Systems with Applications, 2020)
• Parkinson’s disease diagnosis and severity assessment using ground reaction forces and neural networks (Frontiers in Physiology, 2020)
• Classification of Parkinson’s disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data driven approach (Journal of neuroengineering and rehabilitation, 2020)
• Biomechanical parameters assessment for the classification of Parkinson disease using bidirectional long short-term memory (2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, 2020)
• Deep learning identifies digital biomarkers for self-reported Parkinson’s disease (Patterns, 2020)
• Detection of Parkinson’s disease from gait using neighborhood representation local binary patterns (Biomedical Signal Processing and Control, 2020)
• Gait spatiotemporal signal analysis for Parkinson’s disease detection and severity rating (IEEE Sensors Journal, 2020)

2019

• Clinical human gait classification: extreme learning machine approach (2019 1st International Conference on Advances in Science, Engineering, and Robotics Technology (ICASERT), IEEE, 2019)
• A deep learning-based approach for gait analysis in Huntington disease (MEDINFO 2019: Health and Wellbeing e-Networks for All, IOS Press, 2019)
• An automatic method for identifying Huntington's disease using gait dynamics (2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), IEEE, 2019)
• Diagnosing Huntington's disease through gait dynamics (Advances in Visual Computing: 14th International Symposium on Visual Computing ISVC 2019, Springer, 2019)
• Machine learning-based method for Huntington's disease gait pattern recognition (Neural Information Processing: 26th International Conference, ICONIP 2019, Springer, 2019)
• Data-driven based approach to aid Parkinson’s disease diagnosis (Sensors, 2019)
• Parkinson’s disease detection from gait patterns (2019 E-Health and Bioengineering Conference (EHB), IEEE, 2019)
• Classification of gait patterns between patients with Parkinson’s disease and healthy controls using phase space reconstruction (PSR), empirical mode decomposition (EMD) and neural networks (Neural Networks, 2019)
• Models of Parkinson’s disease patient gait (IEEE Journal of Biomedical and Health Informatics, 2019)
• LSTM and convolution networks exploration for Parkinson’s diagnosis (2019 IEEE Colombian Conference on Communications and Computing (COLCOM), IEEE, 2019)
• A dual-modal attention-enhanced deep learning network for quantification of Parkinson’s disease characteristics (IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019)

2018

• Neurodegenerative disease classification using nonlinear gait signal analysis (Electrical Engineering (ICEE), Iranian Conference on. IEEE, 2018)
• String grammar unsupervised possibilistic fuzzy c-medians for gait pattern classification in patients with neurodegenerative diseases (Computational Intelligence and Neuroscience, 2018)
• Parkinson’s disease monitoring from gait analysis via foot-worn sensors (Biocybernetics and Biomedical Engineering, 2018)
• A hybrid spatio-temporal model for detection and severity rating of Parkinson’s disease from gait data (Neurocomputing, 2018)
• Spatial temporal graph convolutional networks for skeleton-based action recognition (Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2018)
• Comparative motor pre-clinical assessment in Parkinson’s disease using supervised machine learning approaches (Annals of biomedical engineering, 2018)
• A validation study of freezing of gait (FOG) detection and machine-learning-based FOG prediction using estimated gait characteristics with a wearable accelerometer (Sensors, 2018)
• A computer-aided diagnosis system for the early detection of neurodegenerative diseases using linear and non-linear analysis (2018 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME), IEEE, 2018)
• Meta-classifiers in Huntington's disease patients classification using iPhone's movement sensors placed at the ankles (IEEE Access, 2018)

2017

• Towards automated human gait disease classification using phase space representation of intrinsic mode functions (Automated Visual Inspection and Machine Vision II, SPIE, 2017)
• Statistical energy values and peak analysis (sep) approach for detection of neurodegenerative diseases (2017 World Congress on Computing and Communication Technologies (WCCCT), IEEE, 2017)
• Vertical ground reaction force marker for parkinson’s disease (PloS one, 2017)
• An automatic non-invasive method for Parkinson’s disease classification (Computer methods and programs in biomedicine, 2017)
• Parkinson's disease classification using wavelet transform based feature extraction of gait data (2017 International Conference on Circuit Power and Computing Technologies (ICCPCT), IEEE, 2017)
• Tensor decomposition of gait dynamics in Parkinson’s disease (IEEE Transactions on Biomedical Engineering, 2017)

2016

• A novel approach for analysis of altered gait variability in amyotrophic lateral sclerosis (Medical & biological engineering & computing, 2016)
• Application of machine learning in postural control kinematics for the diagnosis of Alzheimer’s disease (Computational Intelligence and Neuroscience, 2016)
• Machine learning framework for gait classification using inertial sensors: Application to elderly, post-stroke and huntington’s disease patients (Sensors, 2016)
• Parkinson’s disease classification using gait analysis via deterministic learning (Neuroscience letters, 2016)
• Detection of parkinson’s disease by shifted one dimensional local binary patterns from gait (Expert Systems with Applications, 2016)
• Q-backpropagated Time Delay Neural Network (Q-BTDNN) for predicting gait disturbances in PD (Journal of biomedical informatics, 2016)
• Recognizing parkinsonian gait pattern by exploiting fine-grained movement function features (ACM Transactions on Intelligent Systems and Technology (TIST), 2016)
• Deep residual learning for image recognition (Proceedings of the IEEE conference on computer vision and pattern recognition 2016)

2015

• Classification of gait rhythm signals between patients with neuro-degenerative diseases and normal subjects: Experiments with statistical features and different classification models (Biomedical Signal Processing and Control, 2015)
• Assessment of gait spatio-temporal parameters in neurological disorders using wearable inertial sensors (N.A., 2015)
• Motion tracking and gait feature estimation for recognising parkinson’s disease using ms kinect (Biomedical engineering online, 2015)

2014

• Hmm for classification of parkinson’s disease based on the raw gait data (Journal of medical systems, 2014)

2013

• Hybrid correlation - neural network synergy for gait signal classification (Advances in Heuristic Signal Processing and Applications, Springer, 2013)

2012

• Automatic diagnosis of neuro-degenerative diseases using gait dynamics (Measurement, 2012)

List of All Included Papers

Taxonomy of Gait Data

Research Vision on 3D Skeleton based NDs Diagnosis

Citation

If you found this repository useful, please consider citing:

@article{rao2024survey,
      title={A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis}, 
      author={Rao, Haocong and Zeng, Minlin and Zhao, Xuejiao and Miao, Chunyan},
      year={2024},
      journal={arXiv preprint arXiv:2405.13082},
}