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Elbatanouny H, Kleanthous N, Dahrouj H, Alusi S, Almajali E, Mahmoud S, Hussain A. Insights into Parkinson's Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:3959. [PMID: 38931743 PMCID: PMC11207947 DOI: 10.3390/s24123959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/29/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024]
Abstract
Parkinson's Disease (PD) is a complex neurodegenerative disorder characterized by a spectrum of motor and non-motor symptoms, prominently featuring the freezing of gait (FOG), which significantly impairs patients' quality of life. Despite extensive research, the precise mechanisms underlying FOG remain elusive, posing challenges for effective management and treatment. This paper presents a comprehensive meta-analysis of FOG prediction and detection methodologies, with a focus on the integration of wearable sensor technology and machine learning (ML) approaches. Through an exhaustive review of the literature, this study identifies key trends, datasets, preprocessing techniques, feature extraction methods, evaluation metrics, and comparative analyses between ML and non-ML approaches. The analysis also explores the utilization of cueing devices. The limited adoption of explainable AI (XAI) approaches in FOG prediction research represents a significant gap. Improving user acceptance and comprehension requires an understanding of the logic underlying algorithm predictions. Current FOG detection and prediction research has a number of limitations, which are identified in the discussion. These include issues with cueing devices, dataset constraints, ethical and privacy concerns, financial and accessibility restrictions, and the requirement for multidisciplinary collaboration. Future research avenues center on refining explainability, expanding and diversifying datasets, adhering to user requirements, and increasing detection and prediction accuracy. The findings contribute to advancing the understanding of FOG and offer valuable guidance for the development of more effective detection and prediction methodologies, ultimately benefiting individuals affected by PD.
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Affiliation(s)
- Hagar Elbatanouny
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
| | | | - Hayssam Dahrouj
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
| | - Sundus Alusi
- The Walton Centre NHS Foundation Trust, Liverpool L9 7LJ, UK;
| | - Eqab Almajali
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
| | - Soliman Mahmoud
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
- University of Khorfakkan, Khorfakkan, Sharjah 18119, United Arab Emirates
| | - Abir Hussain
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
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Penedo T, Kalva-Filho CA, Cursiol JA, Faria MH, Coelho DB, Barbieri FA. Spatial-temporal parameters during unobstructed walking in people with Parkinson's disease and healthy older people: a public data set. Front Aging Neurosci 2024; 16:1354738. [PMID: 38605861 PMCID: PMC11007149 DOI: 10.3389/fnagi.2024.1354738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 03/15/2024] [Indexed: 04/13/2024] Open
Affiliation(s)
- Tiago Penedo
- Human Movement Research Laboratory (MOVI-LAB), Department of Physical Education, São Paulo State University (Unesp), Bauru, Brazil
| | - Carlos Augusto Kalva-Filho
- Human Movement Research Laboratory (MOVI-LAB), Department of Physical Education, São Paulo State University (Unesp), Bauru, Brazil
| | - Jônatas Augusto Cursiol
- Human Movement Research Laboratory (MOVI-LAB), Department of Physical Education, São Paulo State University (Unesp), Bauru, Brazil
| | - Murilo Henrique Faria
- Human Movement Research Laboratory (MOVI-LAB), Department of Physical Education, São Paulo State University (Unesp), Bauru, Brazil
| | - Daniel Boari Coelho
- Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
- Biomedical Engineering, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Fabio Augusto Barbieri
- Human Movement Research Laboratory (MOVI-LAB), Department of Physical Education, São Paulo State University (Unesp), Bauru, Brazil
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Klaver EC, Heijink IB, Silvestri G, van Vugt JPP, Janssen S, Nonnekes J, van Wezel RJA, Tjepkema-Cloostermans MC. Comparison of state-of-the-art deep learning architectures for detection of freezing of gait in Parkinson's disease. Front Neurol 2023; 14:1306129. [PMID: 38178885 PMCID: PMC10764416 DOI: 10.3389/fneur.2023.1306129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 11/21/2023] [Indexed: 01/06/2024] Open
Abstract
Introduction Freezing of gait (FOG) is one of the most debilitating motor symptoms experienced by patients with Parkinson's disease (PD). FOG detection is possible using acceleration data from wearable sensors, and a convolutional neural network (CNN) is often used to determine the presence of FOG epochs. We compared the performance of a standard CNN for the detection of FOG with two more complex networks, which are well suited for time series data, the MiniRocket and the InceptionTime. Methods We combined acceleration data of people with PD across four studies. The final data set was split into a training (80%) and hold-out test (20%) set. A fifth study was included as an unseen test set. The data were windowed (2 s) and five-fold cross-validation was applied. The CNN, MiniRocket, and InceptionTime models were evaluated using a receiver operating characteristic (ROC) curve and its area under the curve (AUC). Multiple sensor configurations were evaluated for the best model. The geometric mean was subsequently calculated to select the optimal threshold. The selected model and threshold were evaluated on the hold-out and unseen test set. Results A total of 70 participants (23.7 h, 9% FOG) were included in this study for training and testing, and in addition, 10 participants provided an unseen test set (2.4 h, 11% FOG). The CNN performed best (AUC = 0.86) in comparison to the InceptionTime (AUC = 0.82) and MiniRocket (AUC = 0.76) models. For the CNN, we found a similar performance for a seven-sensor configuration (lumbar, upper and lower legs and feet; AUC = 0.86), six-sensor configuration (upper and lower legs and feet; AUC = 0.87), and two-sensor configuration (lower legs; AUC = 0.86). The optimal threshold of 0.45 resulted in a sensitivity of 77% and a specificity of 58% for the hold-out set (AUC = 0.72), and a sensitivity of 85% and a specificity of 68% for the unseen test set (AUC = 0.90). Conclusion We confirmed that deep learning can be used to detect FOG in a large, heterogeneous dataset. The CNN model outperformed more complex networks. This model could be employed in future personalized interventions, with the ultimate goal of using automated FOG detection to trigger real-time cues to alleviate FOG in daily life.
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Affiliation(s)
- Emilie Charlotte Klaver
- Department of Neurology and Clinical Neurophysiology, Medical Spectrum Twente, Enschede, Netherlands
- Department of Neurobiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Irene B. Heijink
- Department of Neurology and Clinical Neurophysiology, Medical Spectrum Twente, Enschede, Netherlands
| | - Gianluigi Silvestri
- Department of Neurobiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
- OnePlanet Research Center imec-the Netherlands, Wageningen, Netherlands
| | - Jeroen P. P. van Vugt
- Department of Neurology and Clinical Neurophysiology, Medical Spectrum Twente, Enschede, Netherlands
| | - Sabine Janssen
- Department of Rehabilitation, Centre of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands
- Department of Biomedical Signals and Systems, MedTech Centre, University of Twente, Enschede, Netherlands
- Department of Neurology, Anna Hospital, Geldrop, Netherlands
| | - Jorik Nonnekes
- Department of Rehabilitation, Centre of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands
- Department of Rehabilitation, Sint Maartenskliniek, Nijmegen, Netherlands
| | - Richard J. A. van Wezel
- Department of Neurobiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
- Department of Biomedical Signals and Systems, MedTech Centre, University of Twente, Enschede, Netherlands
| | - Marleen C. Tjepkema-Cloostermans
- Department of Neurology and Clinical Neurophysiology, Medical Spectrum Twente, Enschede, Netherlands
- Department of Clinical Neurophysiology, MedTech Centre, University of Twente, Enschede, Netherlands
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Scully AE, Neo K, Lim E, Manharlal PK, de Oliveira B, Hill KD, Clark R, Pua YH, Tan D. Reliability and variability of physiotherapists scoring freezing of gait through video analysis. Physiother Theory Pract 2023:1-11. [PMID: 37639503 DOI: 10.1080/09593985.2023.2252059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/18/2023] [Accepted: 08/18/2023] [Indexed: 08/31/2023]
Abstract
BACKGROUND The "gold standard" marker for freezing of gait severity is percentage of time spent with freezing observed through video analysis. OBJECTIVE This study examined inter- and intra-rater reliability and variability of physiotherapists rating freezing of gait severity through video analysis and explored the effects of experience. METHODS Thirty physiotherapists rated 14 videos of Timed Up and Go performance by people with Parkinson's and gait freezing. Ten videos were unique, while four were repeated. Freezing frequency, total duration, and percentage of time spent with freezing were computed. Reliability and variability were estimated using ICC (2,1) and mean absolute differences. Between-group differences were calculated with the one-way ANOVA. RESULTS Inter- and intra-rater reliability ranged from moderate to good (ICC: inter-rater frequency = 0.63, duration = 0.78, percentage = 0.50; intra-rater frequency = 0.84, duration = 0.89, percentage = 0.50). Variability for freezing frequency was two episodes. Inter- and intra-rater variability for total freezing duration was 18.8 and 12.3 seconds, respectively. For percentage of time spent with freezing, this was 15.2% and 13.5%. Physiotherapy experience had no effect. CONCLUSION Physiotherapists demonstrated sufficient reliability, but variability was large enough to cause changes in severity classifications on existing rating scales. Percentage of time spent with freezing was the least reliable marker, supporting the use of freezing frequency or total duration instead.
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Affiliation(s)
- Aileen E Scully
- Curtin School of Allied Health, Curtin University, Bentley, Western Australia, Australia
- Health and Social Sciences, Singapore Institute of Technology, Singapore
| | - Kenneth Neo
- Health and Social Sciences, Singapore Institute of Technology, Singapore
| | - Eunice Lim
- Health and Social Sciences, Singapore Institute of Technology, Singapore
| | - Prakash K Manharlal
- Department of Neurology (SGH Campus), National Neuroscience Institute @Singapore General Hospital, Singapore
| | - Beatriz de Oliveira
- Curtin School of Allied Health, Curtin University, Bentley, Western Australia, Australia
| | - Keith D Hill
- Rehabilitation, Ageing and Independent Living (RAIL) Research Centre, Monash University, Frankston, Victoria, Australia
| | - Ross Clark
- School of Health, University of the Sunshine Coast, Queensland, Australia
| | - Yong Hao Pua
- Department of Physiotherapy, Singapore General Hospital, Singapore
| | - Dawn Tan
- Health and Social Sciences, Singapore Institute of Technology, Singapore
- Department of Physiotherapy, Singapore General Hospital, Singapore
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Shida TKF, Costa TM, de Oliveira CEN, de Castro Treza R, Hondo SM, Los Angeles E, Bernardo C, Dos Santos de Oliveira L, de Jesus Carvalho M, Coelho DB. A public data set of walking full-body kinematics and kinetics in individuals with Parkinson's disease. Front Neurosci 2023; 17:992585. [PMID: 36875659 PMCID: PMC9978741 DOI: 10.3389/fnins.2023.992585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 01/31/2023] [Indexed: 02/18/2023] Open
Abstract
Background To our knowledge, there is no Parkinson's disease (PD) gait biomechanics data sets available to the public. Objective This study aimed to create a public data set of 26 idiopathic individuals with PD who walked overground on ON and OFF medication. Materials and methods Their upper extremity, trunk, lower extremity, and pelvis kinematics were measured using a three-dimensional motion-capture system (Raptor-4; Motion Analysis). The external forces were collected using force plates. The results include raw and processed kinematic and kinetic data in c3d and ASCII files in different file formats. In addition, a metadata file containing demographic, anthropometric, and clinical data is provided. The following clinical scales were employed: Unified Parkinson's disease rating scale motor aspects of experiences of daily living and motor score, Hoehn & Yahr, New Freezing of Gait Questionnaire, Montreal Cognitive Assessment, Mini Balance Evaluation Systems Tests, Fall Efficacy Scale-International-FES-I, Stroop test, and Trail Making Test A and B. Results All data are available at Figshare (https://figshare.com/articles/dataset/A_dataset_of_overground_walking_full-body_kinematics_and_kinetics_in_individuals_with_Parkinson_s_disease/14896881). Conclusion This is the first public data set containing a three-dimensional full-body gait analysis of individuals with PD under the ON and OFF medication. It is expected to contribute so that different research groups worldwide have access to reference data and a better understanding of the effects of medication on gait.
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Affiliation(s)
| | - Thaisy Moraes Costa
- Biomedical Engineering, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Claudia Eunice Neves de Oliveira
- Biomedical Engineering, Federal University of ABC, São Bernardo do Campo, Brazil.,Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Renata de Castro Treza
- Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Sandy Mikie Hondo
- Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Emanuele Los Angeles
- Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Claudionor Bernardo
- Biomedical Engineering, Federal University of ABC, São Bernardo do Campo, Brazil
| | | | | | - Daniel Boari Coelho
- Biomedical Engineering, Federal University of ABC, São Bernardo do Campo, Brazil.,Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
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Guo Y, Yang J, Liu Y, Chen X, Yang GZ. Detection and assessment of Parkinson's disease based on gait analysis: A survey. Front Aging Neurosci 2022; 14:916971. [PMID: 35992585 PMCID: PMC9382193 DOI: 10.3389/fnagi.2022.916971] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Neurological disorders represent one of the leading causes of disability and mortality in the world. Parkinson's Disease (PD), for example, affecting millions of people worldwide is often manifested as impaired posture and gait. These impairments have been used as a clinical sign for the early detection of PD, as well as an objective index for pervasive monitoring of the PD patients in daily life. This review presents the evidence that demonstrates the relationship between human gait and PD, and illustrates the role of different gait analysis systems based on vision or wearable sensors. It also provides a comprehensive overview of the available automatic recognition systems for the detection and management of PD. The intervening measures for improving gait performance are summarized, in which the smart devices for gait intervention are emphasized. Finally, this review highlights some of the new opportunities in detecting, monitoring, and treating of PD based on gait, which could facilitate the development of objective gait-based biomarkers for personalized support and treatment of PD.
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Affiliation(s)
- Yao Guo
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Jianxin Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Yuxuan Liu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Xun Chen
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
| | - Guang-Zhong Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
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