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Deng D, Ostrem JL, Nguyen V, Cummins DD, Sun J, Pathak A, Little S, Abbasi-Asl R. Interpretable video-based tracking and quantification of parkinsonism clinical motor states. NPJ Parkinsons Dis 2024; 10:122. [PMID: 38918385 PMCID: PMC11199701 DOI: 10.1038/s41531-024-00742-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 06/14/2024] [Indexed: 06/27/2024] Open
Abstract
Quantification of motor symptom progression in Parkinson's disease (PD) patients is crucial for assessing disease progression and for optimizing therapeutic interventions, such as dopaminergic medications and deep brain stimulation. Cumulative and heuristic clinical experience has identified various clinical signs associated with PD severity, but these are neither objectively quantifiable nor robustly validated. Video-based objective symptom quantification enabled by machine learning (ML) introduces a potential solution. However, video-based diagnostic tools often have implementation challenges due to expensive and inaccessible technology, and typical "black-box" ML implementations are not tailored to be clinically interpretable. Here, we address these needs by releasing a comprehensive kinematic dataset and developing an interpretable video-based framework that predicts high versus low PD motor symptom severity according to MDS-UPDRS Part III metrics. This data driven approach validated and robustly quantified canonical movement features and identified new clinical insights, not previously appreciated as related to clinical severity, including pinkie finger movements and lower limb and axial features of gait. Our framework is enabled by retrospective, single-view, seconds-long videos recorded on consumer-grade devices such as smartphones, tablets, and digital cameras, thereby eliminating the requirement for specialized equipment. Following interpretable ML principles, our framework enforces robustness and interpretability by integrating (1) automatic, data-driven kinematic metric evaluation guided by pre-defined digital features of movement, (2) combination of bi-domain (body and hand) kinematic features, and (3) sparsity-inducing and stability-driven ML analysis with simple-to-interpret models. These elements ensure that the proposed framework quantifies clinically meaningful motor features useful for both ML predictions and clinical analysis.
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Affiliation(s)
- Daniel Deng
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Jill L Ostrem
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Vy Nguyen
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Daniel D Cummins
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Julia Sun
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | | | - Simon Little
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
| | - Reza Abbasi-Asl
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
- UCSF Weill Institute for Neurosciences, San Francisco, CA, USA.
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2
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Rodriguez F, Krauss P, Kluckert J, Ryser F, Stieglitz L, Baumann C, Gassert R, Imbach L, Bichsel O. Continuous and Unconstrained Tremor Monitoring in Parkinson's Disease Using Supervised Machine Learning and Wearable Sensors. PARKINSON'S DISEASE 2024; 2024:5787563. [PMID: 38803413 PMCID: PMC11129907 DOI: 10.1155/2024/5787563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 03/24/2024] [Accepted: 04/24/2024] [Indexed: 05/29/2024]
Abstract
Background Accurately assessing the severity and frequency of fluctuating motor symptoms is important at all stages of Parkinson's disease management. Contrarily to time-consuming clinical testing or patient self-reporting with uncertain reliability, recordings with wearable sensors show promise as a tool for continuously and objectively assessing PD symptoms. While wearables-based clinical assessments during standardised and scripted tasks have been successfully implemented, assessments during unconstrained activity remain a challenge. Methods We developed and implemented a supervised machine learning algorithm, trained and tested on tremor scores. We evaluated the algorithm on a 67-hour database comprising sensor data and clinical tremor scores for 24 Parkinson patients at four extremities for periods of about 3 hours. A random 25% subset of the labelled samples was used as test data, the remainder as training data. Based on features extracted from the sensor data, a Support Vector Machine was trained to predict tremor severity. Due to the inherent imbalance in tremor scores, we applied dataset rebalancing techniques. Results Our classifier demonstrated robust performance in detecting tremor events with a sensitivity of 0.90 on the test-portion of the resampled dataset. The overall classification accuracy was high at 0.88. Conclusion We implemented an accurate classifier for tremor monitoring in free-living environments that can be trained even with modestly sized and imbalanced datasets. This advancement offers significant clinical value in continuously monitoring Parkinson's disease symptoms beyond the hospital setting, paving the way for personalized management of PD, timely therapeutic adjustments, and improved patient quality of life.
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Affiliation(s)
- Fernando Rodriguez
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Philipp Krauss
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Clinical Neuroscience Centre, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Neurosurgery, University Hospital Augsburg, Augsburg, Germany
| | - Jonas Kluckert
- Clinical Neuroscience Centre, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Neurology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Franziska Ryser
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Lennart Stieglitz
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Clinical Neuroscience Centre, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Christian Baumann
- Clinical Neuroscience Centre, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Neurology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Roger Gassert
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Lukas Imbach
- Swiss Epilepsy Center, Klinik Lengg, Zurich, Switzerland
| | - Oliver Bichsel
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Clinical Neuroscience Centre, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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3
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di Biase L. Clinical Management of Movement Disorders. J Clin Med 2023; 13:43. [PMID: 38202050 PMCID: PMC10779840 DOI: 10.3390/jcm13010043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024] Open
Abstract
Movement disorders include a wide and heterogeneous variety of signs and syndromes, which are classified as hyperkinetic [...].
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Affiliation(s)
- Lazzaro di Biase
- Neurology Unit, Campus Bio-Medico University Hospital Foundation, Via Álvaro del Portillo 200, 00128 Rome, Italy
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4
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Gong NJ, Clifford GD, Esper CD, Factor SA, McKay JL, Kwon H. Classifying Tremor Dominant and Postural Instability and Gait Difficulty Subtypes of Parkinson's Disease from Full-Body Kinematics. SENSORS (BASEL, SWITZERLAND) 2023; 23:8330. [PMID: 37837160 PMCID: PMC10575216 DOI: 10.3390/s23198330] [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: 08/11/2023] [Revised: 10/03/2023] [Accepted: 10/07/2023] [Indexed: 10/15/2023]
Abstract
Characterizing motor subtypes of Parkinson's disease (PD) is an important aspect of clinical care that is useful for prognosis and medical management. Although all PD cases involve the loss of dopaminergic neurons in the brain, individual cases may present with different combinations of motor signs, which may indicate differences in underlying pathology and potential response to treatment. However, the conventional method for distinguishing PD motor subtypes involves resource-intensive physical examination by a movement disorders specialist. Moreover, the standardized rating scales for PD rely on subjective observation, which requires specialized training and unavoidable inter-rater variability. In this work, we propose a system that uses machine learning models to automatically and objectively identify some PD motor subtypes, specifically Tremor-Dominant (TD) and Postural Instability and Gait Difficulty (PIGD), from 3D kinematic data recorded during walking tasks for patients with PD (MDS-UPDRS-III Score, 34.7 ± 10.5, average disease duration 7.5 ± 4.5 years). This study demonstrates a machine learning model utilizing kinematic data that identifies PD motor subtypes with a 79.6% F1 score (N = 55 patients with parkinsonism). This significantly outperformed a comparison model using classification based on gait features (19.8% F1 score). Variants of our model trained to individual patients achieved a 95.4% F1 score. This analysis revealed that both temporal, spectral, and statistical features from lower body movements are helpful in distinguishing motor subtypes. Automatically assessing PD motor subtypes simply from walking may reduce the time and resources required from specialists, thereby improving patient care for PD treatments. Furthermore, this system can provide objective assessments to track the changes in PD motor subtypes over time to implement and modify appropriate treatment plans for individual patients as needed.
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Affiliation(s)
- N. Jabin Gong
- School of Computer Science, College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA;
| | - Gari D. Clifford
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA (J.L.M.)
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30322, USA
| | - Christine D. Esper
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA; (C.D.E.); (S.A.F.)
| | - Stewart A. Factor
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA; (C.D.E.); (S.A.F.)
| | - J. Lucas McKay
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA (J.L.M.)
| | - Hyeokhyen Kwon
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA (J.L.M.)
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5
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Van Ooteghem K, Godkin FE, Thai V, Beyer KB, Cornish BF, Weber KS, Bernstein H, Kheiri SO, Swartz RH, Tan B, McIlroy WE, Roberts AC. User-centered design of feedback regarding health-related behaviors derived from wearables: An approach targeting older adults and persons living with neurodegenerative disease. Digit Health 2023; 9:20552076231179031. [PMID: 37312943 PMCID: PMC10259132 DOI: 10.1177/20552076231179031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 05/12/2023] [Indexed: 06/15/2023] Open
Abstract
Objective There has been tremendous growth in wearable technologies for health monitoring but limited efforts to optimize methods for sharing wearables-derived information with older adults and clinical cohorts. This study aimed to co-develop, design and evaluate a personalized approach for information-sharing regarding daily health-related behaviors captured with wearables. Methods A participatory research approach was adopted with: (a) iterative stakeholder, and evidence-led development of feedback reporting; and (b) evaluation in a sample of older adults (n = 15) and persons living with neurodegenerative disease (NDD) (n = 25). Stakeholders included persons with lived experience, healthcare providers, health charity representatives and individuals involved in aging/NDD research. Feedback report information was custom-derived from two limb-mounted inertial measurement units and a mobile electrocardiography device worn by participants for 7-10 days. Mixed methods were used to evaluate reporting 2 weeks following delivery. Data were summarized using descriptive statistics for the group and stratified by cohort and cognitive status. Results Participants (n = 40) were 60% female (median 72 (60-87) years). A total of 82.5% found the report easy to read or understand, 80% reported the right amount of information was shared, 90% found the information helpful, 92% shared the information with a family member or friend and 57.5% made a behavior change. Differences emerged in sub-group comparisons. A range of participant profiles existed in terms of interest, uptake and utility. Conclusions The reporting approach was generally well-received with perceived value that translated into enhanced self-awareness and self-management of daily health-related behaviors. Future work should examine potential for scale, and the capacity for wearables-derived feedback to influence longer-term behavior change.
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Affiliation(s)
- Karen Van Ooteghem
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - F Elizabeth Godkin
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Vanessa Thai
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Kit B Beyer
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Benjamin F Cornish
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Kyle S Weber
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Hannah Bernstein
- Department of Nanotechnology Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Soha O Kheiri
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Richard H Swartz
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Brian Tan
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - William E McIlroy
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Angela C Roberts
- School of Communication Sciences and Disorders, Western University, London, ON, Canada
- Department of Computer Science, Western University, London, ON, Canada
- Canadian Centre for Activity and Aging, Western University, London, ON, Canada
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6
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Chen M, Sun Z, Su F, Chen Y, Bu D, Lyu Y. An Auxiliary Diagnostic System for Parkinson's Disease Based on Wearable Sensors and Genetic Algorithm Optimized Random Forest. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2254-2263. [PMID: 35947560 DOI: 10.1109/tnsre.2022.3197807] [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: 11/10/2022]
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder characterized mainly by motor-related impairment, an accurate, quantitative, and objective diagnosis is an effective way to slow the disease deterioration process. In this paper, a user-friendly auxiliary diagnostic system for PD is constructed based on the upper limb movement conditions of 100 subjects consisting of 50 PD patients and 50 healthy subjects. This system includes wearable sensors that collect upper limb movement data, host computer for data processing and classification, and graphic user interface (GUI). The genetic algorithm optimized random forest classifier is introduced to classify PD and normal states based on the selected optimal features, and the 50 trials leave-one-out cross-validation is used to evaluate the performance of the classifier, with the highest accuracy of 94.4%. The classification accuracy among different upper limb movement tasks and with the different number of sensors are compared, results show that the task with only alternation hand movement also has satisfactory classification accuracy, and sensors on both wrists performance better than one sensor on a single wrist. The utility of the proposed system is illustrated by neurologists with a deployed GUI during the clinical inquiry, opening the possibility for a wide range of applications in the auxiliary diagnosis of PD.
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7
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Habets JGV, Herff C, Kubben PL, Kuijf ML, Temel Y, Evers LJW, Bloem BR, Starr PA, Gilron R, Little S. Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson's Disease Using a Wrist-Worn Accelerometer. SENSORS 2021; 21:s21237876. [PMID: 34883886 PMCID: PMC8659489 DOI: 10.3390/s21237876] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 01/07/2023]
Abstract
Motor fluctuations in Parkinson’s disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson’s patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson’s patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.
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Affiliation(s)
- Jeroen G. V. Habets
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
- Correspondence: ; Tel.: +31-433-876-052
| | - Christian Herff
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Pieter L. Kubben
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Mark L. Kuijf
- Department of Neurology, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands;
| | - Yasin Temel
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Luc J. W. Evers
- Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 GC Nijmegen, The Netherlands; (L.J.W.E.); (B.R.B.)
| | - Bastiaan R. Bloem
- Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 GC Nijmegen, The Netherlands; (L.J.W.E.); (B.R.B.)
| | - Philip A. Starr
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
| | - Ro’ee Gilron
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
| | - Simon Little
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
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Ashfaque Mostafa T, Soltaninejad S, McIsaac TL, Cheng I. A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction. SENSORS (BASEL, SWITZERLAND) 2021; 21:6446. [PMID: 34640763 PMCID: PMC8512068 DOI: 10.3390/s21196446] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 09/13/2021] [Accepted: 09/24/2021] [Indexed: 11/30/2022]
Abstract
Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson's Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory Stimulation (RAS) can be used to help patients recover from FOG and resume normal gait. RAS might be ineffective due to the latency between the start of a FOG event, its detection and initialization of RAS. We propose a system capable of both FOG prediction and detection using signals from tri-axial accelerometer sensors that will be useful in initializing RAS with minimal latency. We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). We also propose three Ensemble Network Architectures that combine all the time frequency representations and DL architectures. Experimental results show that our ensemble architectures significantly improve the performance compared with existing techniques. We also present the results of applying our method trained on a publicly available dataset to data collected from patients using wearable sensors in collaboration with A.T. Still University.
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Affiliation(s)
- Tahjid Ashfaque Mostafa
- Multimedia Research Center, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada;
| | - Sara Soltaninejad
- Multimedia Research Center, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada;
| | - Tara L. McIsaac
- Arizona School of Health Sciences, A.T. Still University, 5850 E. Still Circle, Mesa, AZ 85206, USA;
- School of Pharmacy and Health Professions, Creighton University Health Sciences, 3100 N. Central Ave., Phoenix, AZ 85013, USA
| | - Irene Cheng
- Multimedia Research Center, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada;
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9
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Sigcha L, Pavón I, Costa N, Costa S, Gago M, Arezes P, López JM, De Arcas G. Automatic Resting Tremor Assessment in Parkinson's Disease Using Smartwatches and Multitask Convolutional Neural Networks. SENSORS 2021; 21:s21010291. [PMID: 33406692 PMCID: PMC7794726 DOI: 10.3390/s21010291] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 12/22/2020] [Accepted: 12/29/2020] [Indexed: 12/28/2022]
Abstract
Resting tremor in Parkinson's disease (PD) is one of the most distinctive motor symptoms. Appropriate symptom monitoring can help to improve management and medical treatments and improve the patients' quality of life. Currently, tremor is evaluated by physical examinations during clinical appointments; however, this method could be subjective and does not represent the full spectrum of the symptom in the patients' daily lives. In recent years, sensor-based systems have been used to obtain objective information about the disease. However, most of these systems require the use of multiple devices, which makes it difficult to use them in an ambulatory setting. This paper presents a novel approach to evaluate the amplitude and constancy of resting tremor using triaxial accelerometers from consumer smartwatches and multitask classification models. These approaches are used to develop a system for an automated and accurate symptom assessment without interfering with the patients' daily lives. Results show a high agreement between the amplitude and constancy measurements obtained from the smartwatch in comparison with those obtained in a clinical assessment. This indicates that consumer smartwatches in combination with multitask convolutional neural networks are suitable for providing accurate and relevant information about tremor in patients in the early stages of the disease, which can contribute to the improvement of PD clinical evaluation, early detection of the disease, and continuous monitoring.
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Affiliation(s)
- Luis Sigcha
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7, 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimarães, Portugal; (N.C.); (S.C.); (P.A.)
| | - Ignacio Pavón
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7, 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
- Correspondence: ; Tel.: +34-91-067-7222
| | - Nélson Costa
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimarães, Portugal; (N.C.); (S.C.); (P.A.)
| | - Susana Costa
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimarães, Portugal; (N.C.); (S.C.); (P.A.)
| | - Miguel Gago
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057 Braga, Portugal;
| | - Pedro Arezes
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimarães, Portugal; (N.C.); (S.C.); (P.A.)
| | - Juan Manuel López
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7, 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
| | - Guillermo De Arcas
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7, 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
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10
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Point of care TECHNOLOGIES. Digit Health 2021. [DOI: 10.1016/b978-0-12-818914-6.00008-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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11
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Monje MHG, Foffani G, Obeso J, Sánchez-Ferro Á. New Sensor and Wearable Technologies to Aid in the Diagnosis and Treatment Monitoring of Parkinson's Disease. Annu Rev Biomed Eng 2020; 21:111-143. [PMID: 31167102 DOI: 10.1146/annurev-bioeng-062117-121036] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Parkinson's disease (PD) is a degenerative disorder of the brain characterized by the impairment of the nigrostriatal system. This impairment leads to specific motor manifestations (i.e., bradykinesia, tremor, and rigidity) that are assessed through clinical examination, scales, and patient-reported outcomes. New sensor-based and wearable technologies are progressively revolutionizing PD care by objectively measuring these manifestations and improving PD diagnosis and treatment monitoring. However, their use is still limited in clinical practice, perhaps because of the absence of external validation and standards for their continuous use at home. In the near future, these systems will progressively complement traditional tools and revolutionize the way we diagnose and monitor patients with PD.
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Affiliation(s)
- Mariana H G Monje
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, 28938 Móstoles, Madrid, Spain; , , , .,Department of Anatomy, Histology and Neuroscience, School of Medicine, Universidad Autónoma de Madrid, 28029 Madrid, Spain
| | - Guglielmo Foffani
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, 28938 Móstoles, Madrid, Spain; , , , .,Hospital Nacional de Parapléjicos, Servicio de Salud de Castilla La Mancha, 45071 Toledo, Spain
| | - José Obeso
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, 28938 Móstoles, Madrid, Spain; , , , .,Centro de Investigación Biomédica en Red, Enfermedades Neurodegenerativas, 28031 Madrid, Spain
| | - Álvaro Sánchez-Ferro
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, 28938 Móstoles, Madrid, Spain; , , , .,Centro de Investigación Biomédica en Red, Enfermedades Neurodegenerativas, 28031 Madrid, Spain.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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12
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Garza-Rodríguez A, Sánchez-Fernández LP, Sánchez-Pérez LA, Carbajal Hernández JJ. Fuzzy inference model based on triaxial signals for pronation and supination assessment in Parkinson's disease patients. Artif Intell Med 2020; 105:101873. [PMID: 32505417 DOI: 10.1016/j.artmed.2020.101873] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 03/17/2020] [Accepted: 05/02/2020] [Indexed: 11/27/2022]
Abstract
Nowadays, the Unified Parkinson Disease Rating Scale supported by the Movement Disorder Society (MDS-UPDRS), is a standardized and widely accepted instrument to rate Parkinson's disease (PD). This work presents a thorough analysis of item 3.6 of the MDS-UPDRS scale which corresponds to the pronation and supination hand movements. The motivation for this work lies in the objective quantification of motor affectations not covered by the MDS-UPDRS scale such as unsteady oscillations and velocity decrements during the motor exploration. Overall, 12 different bio-mechanical features were quantified based on measurements performed by inertial measurement units (IMUs). After a feature selection process, the selected bio-mechanical features were used as inputs for a fuzzy inference model that predicts the stage of development of the disease in each patient. In addition to this model's output, the scores of three different expert examiners and the output of a fuzzy inference model which covers affectations strictly attached the MDS-UPDRS guidelines, were also considered to obtain an integrated computational model. The proposed integrated model was incorporated using the Analytic Hierarchy Process (AHP), which gives the novelty of a combined score that helps expert examiners to give a broader assessment of the disease that covers both affectations mentioned in the MDS-UPDRS guidelines and affectations not covered by it in an objective manner.
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Affiliation(s)
- Alejandro Garza-Rodríguez
- Instituto Politécnico Nacional, Centro de Investigación en Computación, Juan de Dios Bátiz Ave., 07738, México City, Mexico
| | - Luis Pastor Sánchez-Fernández
- Instituto Politécnico Nacional, Centro de Investigación en Computación, Juan de Dios Bátiz Ave., 07738, México City, Mexico.
| | - Luis Alejandro Sánchez-Pérez
- Electrical and Computer Engineering Department, University of Michigan, 4901 Evergreen Rd., Dearborn, MI, 48128, USA
| | - José Juan Carbajal Hernández
- Instituto Politécnico Nacional, Centro de Investigación en Computación, Juan de Dios Bátiz Ave., 07738, México City, Mexico
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13
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Abrami A, Heisig S, Ramos V, Thomas KC, Ho BK, Caggiano V. Using an unbiased symbolic movement representation to characterize Parkinson's disease states. Sci Rep 2020; 10:7377. [PMID: 32355166 PMCID: PMC7193555 DOI: 10.1038/s41598-020-64181-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 04/09/2020] [Indexed: 11/16/2022] Open
Abstract
Unconstrained human movement can be broken down into a series of stereotyped motifs or 'syllables' in an unsupervised fashion. Sequences of these syllables can be represented by symbols and characterized by a statistical grammar which varies with external situational context and internal neurological state. By first constructing a Markov chain from the transitions between these syllables then calculating the stationary distribution of this chain, we estimate the overall severity of Parkinson's symptoms by capturing the increasingly disorganized transitions between syllables as motor impairment increases. Comparing stationary distributions of movement syllables has several advantages over traditional neurologist administered in-clinic assessments. This technique can be used on unconstrained at-home behavior as well as scripted in-clinic exercises, it avoids differences across human evaluators, and can be used continuously without requiring scripted tasks be performed. We demonstrate the effectiveness of this technique using movement data captured with commercially available wrist worn sensors in 35 participants with Parkinson's disease in-clinic and 25 participants monitored at home.
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Affiliation(s)
- Avner Abrami
- IBM Research - Healthcare and Life Sciences - 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA
| | - Stephen Heisig
- IBM Research - Healthcare and Life Sciences - 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA
| | - Vesper Ramos
- Digital Medicine and the Pfizer Innovation Research Lab, Pfizer, 610 Main Street, Cambridge, MA, 02139, USA
| | - Kevin C Thomas
- Laboratory for Human Neurobiology, Spivack Center for Clinical and Translational Neuroscience, 650 Albany Street, X-140, Boston, MA, 02118, USA
| | - Bryan K Ho
- Department of Neurology Tufts Medical Center 800 Washington Street, Box 314, Boston, MA, 02111-1800, USA
| | - Vittorio Caggiano
- IBM Research - Healthcare and Life Sciences - 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA.
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14
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Sigcha L, Costa N, Pavón I, Costa S, Arezes P, López JM, De Arcas G. Deep Learning Approaches for Detecting Freezing of Gait in Parkinson's Disease Patients through On-Body Acceleration Sensors. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1895. [PMID: 32235373 PMCID: PMC7181252 DOI: 10.3390/s20071895] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 03/21/2020] [Accepted: 03/25/2020] [Indexed: 12/19/2022]
Abstract
Freezing of gait (FOG) is one of the most incapacitating motor symptoms in Parkinson's disease (PD). The occurrence of FOG reduces the patients' quality of live and leads to falls. FOG assessment has usually been made through questionnaires, however, this method can be subjective and could not provide an accurate representation of the severity of this symptom. The use of sensor-based systems can provide accurate and objective information to track the symptoms' evolution to optimize PD management and treatments. Several authors have proposed specific methods based on wearables and the analysis of inertial signals to detect FOG in laboratory conditions, however, its performance is usually lower when being used at patients' homes. This study presents a new approach based on a recurrent neural network (RNN) and a single waist-worn triaxial accelerometer to enhance the FOG detection performance to be used in real home-environments. Also, several machine and deep learning approaches for FOG detection are evaluated using a leave-one-subject-out (LOSO) cross-validation. Results show that modeling spectral information of adjacent windows through an RNN can bring a significant improvement in the performance of FOG detection without increasing the length of the analysis window (required to using it as a cue-system).
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Affiliation(s)
- Luis Sigcha
- Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal; (N.C.); (S.C.); (P.A.)
| | - Nélson Costa
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal; (N.C.); (S.C.); (P.A.)
| | - Ignacio Pavón
- Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
| | - Susana Costa
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal; (N.C.); (S.C.); (P.A.)
| | - Pedro Arezes
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal; (N.C.); (S.C.); (P.A.)
| | - Juan Manuel López
- Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
| | - Guillermo De Arcas
- Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
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15
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Fernández-González P, Carratalá-Tejada M, Monge-Pereira E, Collado-Vázquez S, Sánchez-Herrera Baeza P, Cuesta-Gómez A, Oña-Simbaña ED, Jardón-Huete A, Molina-Rueda F, Balaguer-Bernaldo de Quirós C, Miangolarra-Page JC, Cano-de la Cuerda R. Leap motion controlled video game-based therapy for upper limb rehabilitation in patients with Parkinson's disease: a feasibility study. J Neuroeng Rehabil 2019; 16:133. [PMID: 31694653 PMCID: PMC6836460 DOI: 10.1186/s12984-019-0593-x] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Accepted: 09/10/2019] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Non-immersive video games are currently being used as technological rehabilitation tools for individuals with Parkinson's disease (PD). The aim of this feasibility study was to evaluate the effectiveness of the Leap Motion Controller® (LMC) system used with serious games designed for the upper limb (UL), as well as the levels of satisfaction and compliance among patients in mild-to-moderate stages of the disease. METHODS A non-probabilistic sampling of non-consecutive cases was performed. 23 PD patients, in stages II-IV of the Hoehn & Yahr scale, were randomized into two groups: an experimental group (n = 12) who received treatment based on serious games designed by the research team using the LMC system for the UL, and a control group (n = 11) who received a specific intervention for the UL. Grip muscle strength, coordination, speed of movements, fine and gross UL dexterity, as well as satisfaction and compliance, were assessed in both groups pre-treatment and post-treatment. RESULTS Within the experimental group, significant improvements were observed in all post-treatment assessments, except for Box and Blocks test for the less affected side. Clinical improvements were observed for all assessments in the control group. Statistical intergroup analysis showed significant improvements in coordination, speed of movements and fine motor dexterity scores on the more affected side of patients in the experimental group. CONCLUSIONS The LMC system and the serious games designed may be a feasible rehabilitation tool for the improvement of coordination, speed of movements and fine UL dexterity in PD patients. Further studies are needed to confirm these preliminary findings.
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Affiliation(s)
- Pilar Fernández-González
- Department of Physical Therapy, Occupational Therapy, Physical Medicine and Rehabilitation. Faculty of Health Sciences, Rey Juan Carlos University, Avenida de Atenas s/n 28922 Alcorcón, Madrid, Spain
| | - María Carratalá-Tejada
- Department of Physical Therapy, Occupational Therapy, Physical Medicine and Rehabilitation. Faculty of Health Sciences, Rey Juan Carlos University, Avenida de Atenas s/n 28922 Alcorcón, Madrid, Spain
| | - Esther Monge-Pereira
- Department of Physical Therapy, Occupational Therapy, Physical Medicine and Rehabilitation. Faculty of Health Sciences, Rey Juan Carlos University, Avenida de Atenas s/n 28922 Alcorcón, Madrid, Spain
| | - Susana Collado-Vázquez
- Department of Physical Therapy, Occupational Therapy, Physical Medicine and Rehabilitation. Faculty of Health Sciences, Rey Juan Carlos University, Avenida de Atenas s/n 28922 Alcorcón, Madrid, Spain
| | - Patricia Sánchez-Herrera Baeza
- Department of Physical Therapy, Occupational Therapy, Physical Medicine and Rehabilitation. Faculty of Health Sciences, Rey Juan Carlos University, Avenida de Atenas s/n 28922 Alcorcón, Madrid, Spain
| | - Alicia Cuesta-Gómez
- Department of Physical Therapy, Occupational Therapy, Physical Medicine and Rehabilitation. Faculty of Health Sciences, Rey Juan Carlos University, Avenida de Atenas s/n 28922 Alcorcón, Madrid, Spain.
| | | | | | - Francisco Molina-Rueda
- Department of Physical Therapy, Occupational Therapy, Physical Medicine and Rehabilitation. Faculty of Health Sciences, Rey Juan Carlos University, Avenida de Atenas s/n 28922 Alcorcón, Madrid, Spain
| | | | - Juan Carlos Miangolarra-Page
- Department of Physical Therapy, Occupational Therapy, Physical Medicine and Rehabilitation. Faculty of Health Sciences, Rey Juan Carlos University, Avenida de Atenas s/n 28922 Alcorcón, Madrid, Spain
- Chair of Rehabilitation Unit, Hospital Universitario de Fuenlabrada, Fuenlabrada, Madrid, Spain
| | - Roberto Cano-de la Cuerda
- Department of Physical Therapy, Occupational Therapy, Physical Medicine and Rehabilitation. Faculty of Health Sciences, Rey Juan Carlos University, Avenida de Atenas s/n 28922 Alcorcón, Madrid, Spain
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16
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Cavallo F, Moschetti A, Esposito D, Maremmani C, Rovini E. Upper limb motor pre-clinical assessment in Parkinson's disease using machine learning. Parkinsonism Relat Disord 2019; 63:111-116. [DOI: 10.1016/j.parkreldis.2019.02.028] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 02/19/2019] [Accepted: 02/19/2019] [Indexed: 10/27/2022]
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17
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Stack E, Agarwal V, King R, Burnett M, Tahavori F, Janko B, Harwin W, Ashburn A, Kunkel D. Identifying balance impairments in people with Parkinson's disease using video and wearable sensors. Gait Posture 2018; 62:321-326. [PMID: 29614464 DOI: 10.1016/j.gaitpost.2018.03.047] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 03/26/2018] [Accepted: 03/28/2018] [Indexed: 02/02/2023]
Abstract
BACKGROUND Falls and near falls are common among people with Parkinson's (PwP). To date, most wearable sensor research focussed on fall detection, few studies explored if wearable sensors can detect instability. RESEARCH QUESTION Can instability (caution or near-falls) be detected using wearable sensors in comparison to video analysis? METHODS Twenty-four people (aged 60-86) with and without Parkinson's were recruited from community groups. Movements (e.g. walking, turning, transfers and reaching) were observed in the gait laboratory and/or at home; recorded using clinical measures, video and five wearable sensors (attached on the waist, ankles and wrists). After defining 'caution' and 'instability', two researchers evaluated video data and a third the raw wearable sensor data; blinded to each other's evaluations. Agreement between video and sensor data was calculated on stability, timing, step count and strategy. RESULTS Data was available for 117 performances: 82 (70%) appeared stable on video. Ratings agreed in 86/117 cases (74%). Highest agreement was noted for chair transfer, timed up and go test and 3 m walks. Video analysts noted caution (slow, contained movements, safety-enhancing postures and concentration) and/or instability (saving reactions, stopping after stumbling or veering) in 40/134 performances (30%): raw wearable sensor data identified 16/35 performances rated cautious or unstable (sensitivity 46%) and 70/82 rated stable (specificity 85%). There was a 54% chance that a performance identified from wearable sensors as cautious/unstable was so; rising to 80% for stable movements. SIGNIFICANCE Agreement between wearable sensor and video data suggested that wearable sensors can detect subtle instability and near-falls. Caution and instability were observed in nearly a third of performances, suggesting that simple, mildly challenging actions, with clearly defined start- and end-points, may be most amenable to monitoring during free-living at home. Using the genuine near-falls recorded, work continues to automatically detect subtle instability using algorithms.
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Affiliation(s)
- Emma Stack
- Faculty of Health Sciences, University of Southampton, Southampton, UK
| | - Veena Agarwal
- Faculty of Health Sciences, University of Southampton, Southampton, UK; Southampton Centre for Biomedical Research, University Hospital Southampton NHS Foundation Trust, UK
| | - Rachel King
- School of Systems Engineering, University of Reading, Reading, UK
| | - Malcolm Burnett
- Faculty of Health Sciences, University of Southampton, Southampton, UK
| | - Fatemeh Tahavori
- Faculty of Health Sciences, University of Southampton, Southampton, UK
| | - Balazs Janko
- School of Systems Engineering, University of Reading, Reading, UK
| | - William Harwin
- School of Systems Engineering, University of Reading, Reading, UK
| | - Ann Ashburn
- Faculty of Health Sciences, University of Southampton, Southampton, UK
| | - Dorit Kunkel
- Faculty of Health Sciences, University of Southampton, Southampton, UK.
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18
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FitzGerald JJ, Lu Z, Jareonsettasin P, Antoniades CA. Quantifying Motor Impairment in Movement Disorders. Front Neurosci 2018; 12:202. [PMID: 29695949 PMCID: PMC5904266 DOI: 10.3389/fnins.2018.00202] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2017] [Accepted: 03/14/2018] [Indexed: 02/05/2023] Open
Abstract
Until recently the assessment of many movement disorders has relied on clinical rating scales that despite careful design are inherently subjective and non-linear. This makes accurate and truly observer-independent quantification difficult and limits the use of sensitive parametric statistical methods. At last, devices capable of measuring neurological problems quantitatively are becoming readily available. Examples include the use of oculometers to measure eye movements and accelerometers to measure tremor. Many applications are being developed for use on smartphones. The benefits include not just more accurate disease quantification, but also consistency of data for longitudinal studies, accurate stratification of patients for entry into trials, and the possibility of automated data capture for remote follow-up. In this mini review, we will look at movement disorders with a particular focus on Parkinson's disease, describe some of the limitations of existing clinical evaluation tools, and illustrate the ways in which objective metrics have already been successful.
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Affiliation(s)
- James J FitzGerald
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.,Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
| | - Zhongjiao Lu
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.,Department of Neurology, West China Hospital of Medicine, Sichuan University, Chengdu, China
| | - Prem Jareonsettasin
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.,Exeter College, University of Oxford, Oxford, United Kingdom
| | - Chrystalina A Antoniades
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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19
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Rong Y, Padron AV, Hagerty KJ, Nelson N, Chi S, Keyhani NO, Katz J, Datta SPA, Gomes C, McLamore ES. Post hoc support vector machine learning for impedimetric biosensors based on weak protein–ligand interactions. Analyst 2018; 143:2066-2075. [DOI: 10.1039/c8an00065d] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
We develop a simple, open source machine learning algorithm for analyzing impedimetric biosensor data using a mobile phone.
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Affiliation(s)
- Y. Rong
- Agricultural & Biological Engineering
- Institute of Food and Agricultural Sciences
- University of Florida
- USA
| | - A. V. Padron
- Agricultural & Biological Engineering
- Institute of Food and Agricultural Sciences
- University of Florida
- USA
| | - K. J. Hagerty
- Agricultural & Biological Engineering
- Institute of Food and Agricultural Sciences
- University of Florida
- USA
| | - N. Nelson
- Biological & Agricultural Engineering
- North Carolina State University
- USA
| | - S. Chi
- Institute of Agricultural Resources and Regional Planning
- Chinese Academy of Agricultural Sciences; Key Laboratory of Microbial Resources
- Ministry of Agriculture
- Beijing
- China
| | - N. O. Keyhani
- Department of Microbiology and Cell Sciences
- Institute of Food and Agricultural Sciences
- University of Florida
- USA
| | - J. Katz
- Department of Oral and Maxillofacial Diagnostic Sciences
- University of Florida
- USA
| | - S. P. A. Datta
- MIT Auto-ID Labs
- Department of Mechanical Engineering
- Massachusetts Institute of Technology
- USA
- Biomedical Engineering Program
| | - C. Gomes
- Department of Mechanical Engineering
- Iowa State University
- USA
| | - E. S. McLamore
- Agricultural & Biological Engineering
- Institute of Food and Agricultural Sciences
- University of Florida
- USA
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