1
|
Khalil RM, Shulman LM, Gruber-Baldini AL, Shakya S, Fenderson R, Van Hoven M, Hausdorff JM, von Coelln R, Cummings MP. Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2024; 24:4983. [PMID: 39124030 PMCID: PMC11314738 DOI: 10.3390/s24154983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 07/21/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024]
Abstract
Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.
Collapse
Affiliation(s)
- Rana M. Khalil
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA;
| | - Lisa M. Shulman
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Ann L. Gruber-Baldini
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (A.L.G.-B.); (S.S.)
| | - Sunita Shakya
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (A.L.G.-B.); (S.S.)
| | - Rebecca Fenderson
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Maxwell Van Hoven
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv 6492416, Israel;
- Department of Physical Therapy, Faculty of Medicine & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
- Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA
| | - Rainer von Coelln
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Michael P. Cummings
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA;
| |
Collapse
|
2
|
Wang B, Hu X, Ge R, Xu C, Zhang J, Gao Z, Zhao S, Polat K. Prediction of Freezing of Gait in Parkinson's disease based on multi-channel time-series neural network. Artif Intell Med 2024; 154:102932. [PMID: 39004005 DOI: 10.1016/j.artmed.2024.102932] [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: 01/02/2024] [Revised: 05/30/2024] [Accepted: 07/04/2024] [Indexed: 07/16/2024]
Abstract
Freezing of Gait (FOG) is a noticeable symptom of Parkinson's disease, like being stuck in place and increasing the risk of falls. The wearable multi-channel sensor system is an efficient method to predict and monitor the FOG, thus warning the wearer to avoid falls and improving the quality of life. However, the existing approaches for the prediction of FOG mainly focus on a single sensor system and cannot handle the interference between multi-channel wearable sensors. Hence, we propose a novel multi-channel time-series neural network (MCT-Net) approach to merge multi-channel gait features into a comprehensive prediction framework, alerting patients to FOG symptoms in advance. Owing to the causal distributed convolution, MCT-Net is a real-time method available to give optimal prediction earlier and implemented in remote devices. Moreover, intra-channel and inter-channel transformers of MCT-Net extract and integrate different sensor position features into a unified deep learning model. Compared with four other state-of-the-art FOG prediction baselines, the proposed MCT-Net obtains 96.21% in accuracy and 80.46% in F1-score on average 2 s before FOG occurrence, demonstrating the superiority of MCT-Net.
Collapse
Affiliation(s)
| | - Xuegang Hu
- Hefei University of Technology, Hefei, China.
| | - Rongjun Ge
- Nanjing University of Aeronautics and Astronautics, Nanjing, China.
| | - Chenchu Xu
- Institute of Artificial Intelligence, Hefei, China; Anhui University, Hefei, China.
| | | | - Zhifan Gao
- Sun Yat-sen University, Shenzhen, China.
| | | | - Kemal Polat
- Bolu Abant Izzet Baysal University, Bolu, Turkey.
| |
Collapse
|
3
|
Al-Nefaie AH, Aldhyani THH, Farhah N, Koundal D. Intelligent diagnosis system based on artificial intelligence models for predicting freezing of gait in Parkinson's disease. Front Med (Lausanne) 2024; 11:1418684. [PMID: 38966531 PMCID: PMC11222646 DOI: 10.3389/fmed.2024.1418684] [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: 04/16/2024] [Accepted: 05/29/2024] [Indexed: 07/06/2024] Open
Abstract
Introduction Freezing of gait (FoG) is a significant issue for those with Parkinson's disease (PD) since it is a primary contributor to falls and is linked to a poor superiority of life. The underlying apparatus is still not understood; however, it is postulated that it is associated with cognitive disorders, namely impairments in executive and visuospatial functions. During episodes of FoG, patients may experience the risk of falling, which significantly effects their quality of life. Methods This research aims to systematically evaluate the effectiveness of machine learning approaches in accurately predicting a FoG event before it occurs. The system was tested using a dataset collected from the Kaggle repository and comprises 3D accelerometer data collected from the lower backs of people who suffer from episodes of FoG, a severe indication frequently realized in persons with Parkinson's disease. Data were acquired by measuring acceleration from 65 patients and 20 healthy senior adults while they engaged in simulated daily life tasks. Of the total participants, 45 exhibited indications of FoG. This research utilizes seven machine learning methods, namely the decision tree, random forest, Knearest neighbors algorithm, LightGBM, and CatBoost models. The Gated Recurrent Unit (GRU)-Transformers and Longterm Recurrent Convolutional Networks (LRCN) models were applied to predict FoG. The construction and model parameters were planned to enhance performance by mitigating computational difficulty and evaluation duration. Results The decision tree exhibited exceptional performance, achieving sensitivity rates of 91% in terms of accuracy, precision, recall, and F1- score metrics for the FoG, transition, and normal activity classes, respectively. It has been noted that the system has the capacity to anticipate FoG objectively and precisely. This system will be instrumental in advancing consideration in furthering the comprehension and handling of FoG.
Collapse
Affiliation(s)
- Abdullah H. Al-Nefaie
- King Salman Center for Disability Research, Riyadh, Saudi Arabia
- Department of Quantitative Methods, School of Business, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Theyazn H. H. Aldhyani
- King Salman Center for Disability Research, Riyadh, Saudi Arabia
- Applied College in Abqaiq, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Nesren Farhah
- Department of Health Informatics, College of Health Sciences, Saudi Electronic University, Riyadh, Saudi Arabia
| | - Deepika Koundal
- King Salman Center for Disability Research, Riyadh, Saudi Arabia
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
| |
Collapse
|
4
|
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.
Collapse
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.)
| |
Collapse
|
5
|
Yang PK, Filtjens B, Ginis P, Goris M, Nieuwboer A, Gilat M, Slaets P, Vanrumste B. Freezing of gait assessment with inertial measurement units and deep learning: effect of tasks, medication states, and stops. J Neuroeng Rehabil 2024; 21:24. [PMID: 38350964 PMCID: PMC10865632 DOI: 10.1186/s12984-024-01320-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 01/30/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Freezing of gait (FOG) is an episodic and highly disabling symptom of Parkinson's Disease (PD). Traditionally, FOG assessment relies on time-consuming visual inspection of camera footage. Therefore, previous studies have proposed portable and automated solutions to annotate FOG. However, automated FOG assessment is challenging due to gait variability caused by medication effects and varying FOG-provoking tasks. Moreover, whether automated approaches can differentiate FOG from typical everyday movements, such as volitional stops, remains to be determined. To address these questions, we evaluated an automated FOG assessment model with deep learning (DL) based on inertial measurement units (IMUs). We assessed its performance trained on all standardized FOG-provoking tasks and medication states, as well as on specific tasks and medication states. Furthermore, we examined the effect of adding stopping periods on FOG detection performance. METHODS Twelve PD patients with self-reported FOG (mean age 69.33 ± 6.02 years) completed a FOG-provoking protocol, including timed-up-and-go and 360-degree turning-in-place tasks in On/Off dopaminergic medication states with/without volitional stopping. IMUs were attached to the pelvis and both sides of the tibia and talus. A temporal convolutional network (TCN) was used to detect FOG episodes. FOG severity was quantified by the percentage of time frozen (%TF) and the number of freezing episodes (#FOG). The agreement between the model-generated outcomes and the gold standard experts' video annotation was assessed by the intra-class correlation coefficient (ICC). RESULTS For FOG assessment in trials without stopping, the agreement of our model was strong (ICC (%TF) = 0.92 [0.68, 0.98]; ICC(#FOG) = 0.95 [0.72, 0.99]). Models trained on a specific FOG-provoking task could not generalize to unseen tasks, while models trained on a specific medication state could generalize to unseen states. For assessment in trials with stopping, the agreement of our model was moderately strong (ICC (%TF) = 0.95 [0.73, 0.99]; ICC (#FOG) = 0.79 [0.46, 0.94]), but only when stopping was included in the training data. CONCLUSION A TCN trained on IMU signals allows valid FOG assessment in trials with/without stops containing different medication states and FOG-provoking tasks. These results are encouraging and enable future work investigating automated FOG assessment during everyday life.
Collapse
Affiliation(s)
- Po-Kai Yang
- eMedia Research Lab/STADIUS, Department of Electrical Engineering (ESAT), KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium.
- Intelligent Mobile Platforms Research Group, Department of Mechanical Engineering, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium.
| | - Benjamin Filtjens
- eMedia Research Lab/STADIUS, Department of Electrical Engineering (ESAT), KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium
- Intelligent Mobile Platforms Research Group, Department of Mechanical Engineering, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium
| | - Pieter Ginis
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, Tervuursevest 101, 3001, Heverlee, Belgium
| | - Maaike Goris
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, Tervuursevest 101, 3001, Heverlee, Belgium
| | - Alice Nieuwboer
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, Tervuursevest 101, 3001, Heverlee, Belgium
| | - Moran Gilat
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, Tervuursevest 101, 3001, Heverlee, Belgium
| | - Peter Slaets
- Intelligent Mobile Platforms Research Group, Department of Mechanical Engineering, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium
| | - Bart Vanrumste
- eMedia Research Lab/STADIUS, Department of Electrical Engineering (ESAT), KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium
| |
Collapse
|
6
|
Tian H, Li H, Jiang W, Ma X, Li X, Wu H, Li Y. Cross-Spatiotemporal Graph Convolution Networks for Skeleton-Based Parkinsonian Gait MDS-UPDRS Score Estimation. IEEE Trans Neural Syst Rehabil Eng 2024; 32:412-421. [PMID: 38198272 DOI: 10.1109/tnsre.2024.3352004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Gait impairment in Parkinson's Disease (PD) is quantitatively assessed using the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), a well-established clinical tool. Objective and efficient PD gait assessment is crucial for developing interventions to slow or halt its advancement. Skeleton-based PD gait MDS-UPDRS score estimation has attracted increasing interest in improving diagnostic efficiency and objectivity. However, previous works ignore the important cross-spacetime dependencies between joints in PD gait. Moreover, existing PD gait skeleton datasets are very small, which is a big issue in deep learning-based gait studies. In this work, we collect a sizable PD gait skeleton dataset by multi-view Azure Kinect sensors. The collected dataset contains 102 PD patients and 30 healthy older adults. In addition, gait data from 16 young adults (aged 24-50 years) are collected to further examine the effect of age on PD gait assessment. For skeleton-based automatic PD gait analysis, we propose a novel cross-spatiotemporal graph convolution network (CST-GCN) to learn complex features of gait patterns. Specifically, a gait graph labeling strategy is designed to assemble and group cross-spacetime neighbors of the root node according to the spatiotemporal semantics of the gait skeleton. Based on this strategy, the CST-GCN module explicitly models the cross-spacetime dependencies among joints. Finally, a dual-path model is presented to realize the modeling and fusion of spatial, temporal, and cross-spacetime gait features. Extensive experiments validate the effectiveness of our method on the collected dataset.
Collapse
|
7
|
Shi B, Dhaliwal SS, Soo M, Chan C, Wong J, Lam NWC, Zhou E, Paitimusa V, Loke KY, Chin J, Chua MT, Liaw KCS, Lim AWH, Insyirah FF, Yen SC, Tay A, Ang SB. Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation. JMIR AI 2023; 2:e48340. [PMID: 38875549 PMCID: PMC11041426 DOI: 10.2196/48340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 08/31/2023] [Accepted: 09/28/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND Diabetes mellitus is the most challenging and fastest-growing global public health concern. Approximately 10.5% of the global adult population is affected by diabetes, and almost half of them are undiagnosed. The growing at-risk population exacerbates the shortage of health resources, with an estimated 10.6% and 6.2% of adults worldwide having impaired glucose tolerance and impaired fasting glycemia, respectively. All current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or laboratory by trained professionals. At-risk participants might remain undetected for years and miss the precious time window for early intervention to prevent or delay the onset of diabetes and its complications. OBJECTIVE We aimed to develop an artificial intelligence solution to recognize elevated blood glucose levels (≥7.8 mmol/L) noninvasively and evaluate diabetic risk based on repeated measurements. METHODS This study was conducted at KK Women's and Children's Hospital in Singapore, and 500 participants were recruited (mean age 38.73, SD 10.61 years; mean BMI 24.4, SD 5.1 kg/m2). The blood glucose levels for most participants were measured before and after consuming 75 g of sugary drinks using both a conventional glucometer (Accu-Chek Performa) and a wrist-worn wearable. The results obtained from the glucometer were used as ground-truth measurements. We performed extensive feature engineering on photoplethysmography (PPG) sensor data and identified features that were sensitive to glucose changes. These selected features were further analyzed using an explainable artificial intelligence approach to understand their contribution to our predictions. RESULTS Multiple machine learning models were trained and assessed with 10-fold cross-validation, using participant demographic data and critical features extracted from PPG measurements as predictors. A support vector machine with a radial basis function kernel had the best detection performance, with an average accuracy of 84.7%, a sensitivity of 81.05%, a specificity of 88.3%, a precision of 87.51%, a geometric mean of 84.54%, and F score of 84.03%. CONCLUSIONS Our findings suggest that PPG measurements can be used to identify participants with elevated blood glucose measurements and assist in the screening of participants for diabetes risk.
Collapse
Affiliation(s)
- Bohan Shi
- Actxa Pte Ltd, Singapore, Singapore
- Activate Interactive Pte Ltd, Singapore, Singapore
| | - Satvinder Singh Dhaliwal
- Curtin Health Innovation Research Institute, Curtin University, Perth, Australia
- Faculty of Health Sciences, Curtin University, Perth, Australia
- Duke-NUS Graduate Medical School, National University of Singapore, Singapore, Singapore
| | | | - Cheri Chan
- KK Women's and Children's Hospital, Singapore, Singapore
| | | | | | - Entong Zhou
- Activate Interactive Pte Ltd, Singapore, Singapore
| | | | - Kum Yin Loke
- Activate Interactive Pte Ltd, Singapore, Singapore
| | - Joel Chin
- Activate Interactive Pte Ltd, Singapore, Singapore
| | - Mei Tuan Chua
- KK Women's and Children's Hospital, Singapore, Singapore
| | | | | | | | - Shih-Cheng Yen
- Innovation and Design Programme, Faculty of Engineering, National University of Singapore, Singapore, Singapore
| | - Arthur Tay
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Seng Bin Ang
- Family Medicine Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore
- Menopause Unit, KK Women's and Children's Hospital, Singapore, Singapore
| |
Collapse
|
8
|
Chen M, Sun Z, Xin T, Chen Y, Su F. An Interpretable Deep Learning Optimized Wearable Daily Detection System for Parkinson's Disease. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3937-3946. [PMID: 37695969 DOI: 10.1109/tnsre.2023.3314100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
Walking detection in the daily life of patients with Parkinson's disease (PD) is of great significance for tracking the progress of the disease. This study aims to implement an accurate, objective, and passive detection algorithm optimized based on an interpretable deep learning architecture for the daily walking of patients with PD and to explore the most representative spatiotemporal motor features. Five inertial measurement units attached to the wrist, ankle, and waist are used to collect motion data from 100 subjects during a 10-meter walking test. The raw data of each sensor are subjected to the continuous wavelet transform to train the classification model of the constructed 6-channel convolutional neural network (CNN). The results show that the sensor located at the waist has the best classification performance with an accuracy of 98.01%±0.85% and the area under the receiver operating characteristic curve (AUC) of 0.9981±0.0017 under ten-fold cross-validation. The gradient-weighted class activation mapping shows that the feature points with greater contribution to PD were concentrated in the lower frequency band (0.5~3Hz) compared with healthy controls. The visual maps of the 3D CNN show that only three out of the six time series have a greater contribution, which is used as a basis to further optimize the model input, greatly reducing the raw data processing costs (50%) while ensuring its performance (AUC=0.9929±0.0019). To the best of our knowledge, this is the first study to consider the visual interpretation-based optimization of an intelligent classification model in the intelligent diagnosis of PD.
Collapse
|
9
|
Park H, Han S, Sung J, Hwang S, Youn I, Kim SJ. Classification of gait phases based on a machine learning approach using muscle synergy. Front Hum Neurosci 2023; 17:1201935. [PMID: 37266322 PMCID: PMC10230056 DOI: 10.3389/fnhum.2023.1201935] [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: 04/07/2023] [Accepted: 05/03/2023] [Indexed: 06/03/2023] Open
Abstract
The accurate detection of the gait phase is crucial for monitoring and diagnosing neurological and musculoskeletal disorders and for the precise control of lower limb assistive devices. In studying locomotion mode identification and rehabilitation of neurological disorders, the concept of modular organization, which involves the co-activation of muscle groups to generate various motor behaviors, has proven to be useful. This study aimed to investigate whether muscle synergy features could provide a more accurate and robust classification of gait events compared to traditional features such as time-domain and wavelet features. For this purpose, eight healthy individuals participated in this study, and wireless electromyography sensors were attached to four muscles in each lower extremity to measure electromyography (EMG) signals during walking. EMG signals were segmented and labeled as 2-class (stance and swing) and 3-class (weight acceptance, single limb support, and limb advancement) gait phases. Non-negative matrix factorization (NNMF) was used to identify specific muscle groups that contribute to gait and to provide an analysis of the functional organization of the movement system. Gait phases were classified using four different machine learning algorithms: decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN). The results showed that the muscle synergy features had a better classification accuracy than the other EMG features. This finding supported the hypothesis that muscle synergy enables accurate gait phase classification. Overall, the study presents a novel approach to gait analysis and highlights the potential of muscle synergy as a tool for gait phase detection.
Collapse
Affiliation(s)
- Heesu Park
- Biomedical Research Division, Korea Institute of Science and Technology, Seoul, Republic of Korea
- Department of Biomedical Engineering, Korea University College of Medicine, Seoul, Republic of Korea
| | - Sungmin Han
- Biomedical Research Division, Korea Institute of Science and Technology, Seoul, Republic of Korea
- Division of Bio-Medical Science and Technology, KIST School, Korea University of Science and Technology, Seoul, Republic of Korea
| | - Joohwan Sung
- Biomedical Research Division, Korea Institute of Science and Technology, Seoul, Republic of Korea
| | - Soree Hwang
- Biomedical Research Division, Korea Institute of Science and Technology, Seoul, Republic of Korea
- School of Biomedical Engineering, Korea University, Seoul, Republic of Korea
| | - Inchan Youn
- Biomedical Research Division, Korea Institute of Science and Technology, Seoul, Republic of Korea
- Division of Bio-Medical Science and Technology, KIST School, Korea University of Science and Technology, Seoul, Republic of Korea
| | - Seung-Jong Kim
- Department of Biomedical Engineering, Korea University College of Medicine, Seoul, Republic of Korea
| |
Collapse
|
10
|
Borzì L, Sigcha L, Olmo G. Context Recognition Algorithms for Energy-Efficient Freezing-of-Gait Detection in Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094426. [PMID: 37177629 PMCID: PMC10181532 DOI: 10.3390/s23094426] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 05/15/2023]
Abstract
Freezing of gait (FoG) is a disabling clinical phenomenon of Parkinson's disease (PD) characterized by the inability to move the feet forward despite the intention to walk. It is one of the most troublesome symptoms of PD, leading to an increased risk of falls and reduced quality of life. The combination of wearable inertial sensors and machine learning (ML) algorithms represents a feasible solution to monitor FoG in real-world scenarios. However, traditional FoG detection algorithms process all data indiscriminately without considering the context of the activity during which FoG occurs. This study aimed to develop a lightweight, context-aware algorithm that can activate FoG detection systems only under certain circumstances, thus reducing the computational burden. Several approaches were implemented, including ML and deep learning (DL) gait recognition methods, as well as a single-threshold method based on acceleration magnitude. To train and evaluate the context algorithms, data from a single inertial sensor were extracted using three different datasets encompassing a total of eighty-one PD patients. Sensitivity and specificity for gait recognition ranged from 0.95 to 0.96 and 0.80 to 0.93, respectively, with the one-dimensional convolutional neural network providing the best results. The threshold approach performed better than ML- and DL-based methods when evaluating the effect of context awareness on FoG detection performance. Overall, context algorithms allow for discarding more than 55% of non-FoG data and less than 4% of FoG episodes. The results indicate that a context classifier can reduce the computational burden of FoG detection algorithms without significantly affecting the FoG detection rate. Thus, implementation of context awareness can present an energy-efficient solution for long-term FoG monitoring in ambulatory and free-living settings.
Collapse
Affiliation(s)
- Luigi Borzì
- Data Analytics and Technologies for Health Lab (ANTHEA), Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
| | - Luis Sigcha
- Data-Driven Computer Engineering (D2iCE) Group, Department of Electronic and Computer Engineering, University of Limerick, V94 T9PX Limerick, Ireland
| | - Gabriella Olmo
- Data Analytics and Technologies for Health Lab (ANTHEA), Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
| |
Collapse
|
11
|
Huang T, Li M, Huang J. Recent trends in wearable device used to detect freezing of gait and falls in people with Parkinson's disease: A systematic review. Front Aging Neurosci 2023; 15:1119956. [PMID: 36875701 PMCID: PMC9975590 DOI: 10.3389/fnagi.2023.1119956] [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: 12/09/2022] [Accepted: 01/23/2023] [Indexed: 02/17/2023] Open
Abstract
Background The occurrence of freezing of gait (FOG) is often observed in moderate to last-stage Parkinson's disease (PD), leading to a high risk of falls. The emergence of the wearable device has offered the possibility of FOG detection and falls of patients with PD allowing high validation in a low-cost way. Objective This systematic review seeks to provide a comprehensive overview of existing literature to establish the forefront of sensors type, placement and algorithm to detect FOG and falls among patients with PD. Methods Two electronic databases were screened by title and abstract to summarize the state of art on FOG and fall detection with any wearable technology among patients with PD. To be eligible for inclusion, papers were required to be full-text articles published in English, and the last search was completed on September 26, 2022. Studies were excluded if they; (i) only examined cueing function for FOG, (ii) only used non-wearable devices to detect or predict FOG or falls, and (iii) did not provide sufficient details about the study design and results. A total of 1,748 articles were retrieved from two databases. However, only 75 articles were deemed to meet the inclusion criteria according to the title, abstract and full-text reviewed. Variable was extracted from chosen research, including authorship, details of the experimental object, type of sensor, device location, activities, year of publication, evaluation in real-time, the algorithm and detection performance. Results A total of 72 on FOG detection and 3 on fall detection were selected for data extraction. There were wide varieties of the studied population (from 1 to 131), type of sensor, placement and algorithm. The thigh and ankle were the most popular device location, and the combination of accelerometer and gyroscope was the most frequently used inertial measurement unit (IMU). Furthermore, 41.3% of the studies used the dataset as a resource to examine the validity of their algorithm. The results also showed that increasingly complex machine-learning algorithms had become the trend in FOG and fall detection. Conclusion These data support the application of the wearable device to access FOG and falls among patients with PD and controls. Machine learning algorithms and multiple types of sensors have become the recent trend in this field. Future work should consider an adequate sample size, and the experiment should be performed in a free-living environment. Moreover, a consensus on provoking FOG/fall, methods of assessing validity and algorithm are necessary.Systematic Review Registration: PROSPERO, identifier CRD42022370911.
Collapse
Affiliation(s)
- Tinghuai Huang
- Laboratory of Laser Sports Medicine, South China Normal University, Guangzhou, Guangdong, China
| | - Meng Li
- Laboratory of Laser Sports Medicine, South China Normal University, Guangzhou, Guangdong, China
| | - Jianwei Huang
- Department of Gastroenterology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|