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Burtscher J, Bourdillon N, Daalen JMJ, Patoz A, Bally JF, Kopp M, Malatesta D, Bloem BR. Movement analysis in the diagnosis and management of Parkinson's disease. Neural Regen Res 2025; 20:485-486. [PMID: 38819059 DOI: 10.4103/nrr.nrr-d-24-00207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 03/30/2024] [Indexed: 06/01/2024] Open
Affiliation(s)
- Johannes Burtscher
- Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland (Burtscher J, Bourdillon N, Patoz A, Malatesta D)
| | - Nicolas Bourdillon
- Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland (Burtscher J, Bourdillon N, Patoz A, Malatesta D)
| | - Jules M Janssen Daalen
- Radboud University Medical Center, Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Center of Expertise for Parkinson and Movement Disorders, Nijmegen, The Netherlands (Janssen Daalen JM, Bloem BR)
- Radboud University Medical Center, Department of Medical BioSciences, Nijmegen, The Netherlands (Janssen Daalen JM, Bloem BR)
| | - Aurélien Patoz
- Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland (Burtscher J, Bourdillon N, Patoz A, Malatesta D)
- Research and Development Department, Volodalen Swiss Sport Lab, Aigle, Switzerland (Patoz A)
| | - Julien F Bally
- Service of Neurology, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland (Bally JF)
| | - Martin Kopp
- Department of Sport Science, University of Innsbruck, Innsbruck, Austria (Kopp M)
| | - Davide Malatesta
- Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland (Burtscher J, Bourdillon N, Patoz A, Malatesta D)
| | - Bastiaan R Bloem
- Radboud University Medical Center, Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Center of Expertise for Parkinson and Movement Disorders, Nijmegen, The Netherlands (Janssen Daalen JM, Bloem BR)
- Radboud University Medical Center, Department of Medical BioSciences, Nijmegen, The Netherlands (Janssen Daalen JM, Bloem BR)
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Amprimo G, Masi G, Olmo G, Ferraris C. Deep Learning for hand tracking in Parkinson's Disease video-based assessment: Current and future perspectives. Artif Intell Med 2024; 154:102914. [PMID: 38909431 DOI: 10.1016/j.artmed.2024.102914] [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: 10/31/2023] [Revised: 05/19/2024] [Accepted: 05/21/2024] [Indexed: 06/25/2024]
Abstract
BACKGROUND Parkinson's Disease (PD) demands early diagnosis and frequent assessment of symptoms. In particular, analysing hand movements is pivotal to understand disease progression. Advancements in hand tracking using Deep Learning (DL) allow for the automatic and objective disease evaluation from video recordings of standardised motor tasks, which are the foundation of neurological examinations. In view of this scenario, this narrative review aims to describe the state of the art and the future perspective of DL frameworks for hand tracking in video-based PD assessment. METHODS A rigorous search of PubMed, Web of Science, IEEE Explorer, and Scopus until October 2023 using primary keywords such as parkinson, hand tracking, and deep learning was performed to select eligible by focusing on video-based PD assessment through DL-driven hand tracking frameworks RESULTS:: After accurate screening, 23 publications met the selection criteria. These studies used various solutions, from well-established pose estimation frameworks, like OpenPose and MediaPipe, to custom deep architectures designed to accurately track hand and finger movements and extract relevant disease features. Estimated hand tracking data were then used to differentiate PD patients from healthy individuals, characterise symptoms such as tremors and bradykinesia, or regress the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) by automatically assessing clinical tasks such as finger tapping, hand movements, and pronation-supination. CONCLUSIONS DL-driven hand tracking holds promise for PD assessment, offering precise, objective measurements for early diagnosis and monitoring, especially in a telemedicine scenario. However, to ensure clinical acceptance, standardisation and validation are crucial. Future research should prioritise large open datasets, rigorous validation on patients, and the investigation of new frontiers such as tracking hand-hand and hand-object interactions for daily-life tasks assessment.
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Affiliation(s)
- Gianluca Amprimo
- Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy; National Research Council - Institute of Electronics, Information Engineering and Telecommunications, Corso Duca degli Abruzzi, 24, Turin, 10029, Italy.
| | - Giulia Masi
- Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy. https://www.researchgate.net/profile/Giulia-Masi-2
| | - Gabriella Olmo
- Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy. https://www.sysbio.polito.it/analytics-technologies-health/
| | - Claudia Ferraris
- National Research Council - Institute of Electronics, Information Engineering and Telecommunications, Corso Duca degli Abruzzi, 24, Turin, 10029, Italy. https://www.ieiit.cnr.it/people/Ferraris-Claudia
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Adams JL, Kangarloo T, Gong Y, Khachadourian V, Tracey B, Volfson D, Latzman RD, Cosman J, Edgerton J, Anderson D, Best A, Kostrzebski MA, Auinger P, Wilmot P, Pohlson Y, Jensen-Roberts S, Müller MLTM, Stephenson D, Dorsey ER. Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study over 12 months. NPJ Parkinsons Dis 2024; 10:112. [PMID: 38866793 PMCID: PMC11169239 DOI: 10.1038/s41531-024-00721-2] [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: 12/22/2023] [Accepted: 05/10/2024] [Indexed: 06/14/2024] Open
Abstract
Digital measures may provide objective, sensitive, real-world measures of disease progression in Parkinson's disease (PD). However, multicenter longitudinal assessments of such measures are few. We recently demonstrated that baseline assessments of gait, tremor, finger tapping, and speech from a commercially available smartwatch, smartphone, and research-grade wearable sensors differed significantly between 82 individuals with early, untreated PD and 50 age-matched controls. Here, we evaluated the longitudinal change in these assessments over 12 months in a multicenter observational study using a generalized additive model, which permitted flexible modeling of at-home data. All measurements were included until participants started medications for PD. Over one year, individuals with early PD experienced significant declines in several measures of gait, an increase in the proportion of day with tremor, modest changes in speech, and few changes in psychomotor function. As measured by the smartwatch, the average (SD) arm swing in-clinic decreased from 25.9 (15.3) degrees at baseline to 19.9 degrees (13.7) at month 12 (P = 0.004). The proportion of awake time an individual with early PD had tremor increased from 19.3% (18.0%) to 25.6% (21.4%; P < 0.001). Activity, as measured by the number of steps taken per day, decreased from 3052 (1306) steps per day to 2331 (2010; P = 0.16), but this analysis was restricted to 10 participants due to the exclusion of those that had started PD medications and lost the data. The change of these digital measures over 12 months was generally larger than the corresponding change in individual items on the Movement Disorder Society-Unified Parkinson's Disease Rating Scale but not greater than the change in the overall scale. Successful implementation of digital measures in future clinical trials will require improvements in study conduct, especially data capture. Nonetheless, gait and tremor measures derived from a commercially available smartwatch and smartphone hold promise for assessing the efficacy of therapeutics in early PD.
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Affiliation(s)
- Jamie L Adams
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
| | | | - Yishu Gong
- Takeda Pharmaceuticals, Cambridge, MA, USA
| | | | | | | | | | | | | | | | | | - Melissa A Kostrzebski
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Peggy Auinger
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Peter Wilmot
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Yvonne Pohlson
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Stella Jensen-Roberts
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | | | | | - E Ray Dorsey
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
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Valero-Cuevas FJ, Finley J, Orsborn A, Fung N, Hicks JL, Huang HH, Reinkensmeyer D, Schweighofer N, Weber D, Steele KM. NSF DARE-Transforming modeling in neurorehabilitation: Four threads for catalyzing progress. J Neuroeng Rehabil 2024; 21:46. [PMID: 38570842 PMCID: PMC10988973 DOI: 10.1186/s12984-024-01324-x] [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: 09/04/2023] [Accepted: 02/09/2024] [Indexed: 04/05/2024] Open
Abstract
We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation's Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling 'In-the-Wild'. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.
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Affiliation(s)
- Francisco J Valero-Cuevas
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, 90089, CA, USA.
- Division of Biokinesiology and Physical Therapy, University of Southern California, 1540 Alcazar St 155, Los Angeles, 90033, CA, USA.
- Thomas Lord Department of Computer Science, University of Southern California, 941 Bloom Walk, Los Angeles, 90089, CA, USA.
| | - James Finley
- Division of Biokinesiology and Physical Therapy, University of Southern California, 1540 Alcazar St 155, Los Angeles, 90033, CA, USA
| | - Amy Orsborn
- Department of Electrical and Computer Engineering, University of Washington, 185 W Stevens Way NE, Box 352500, Seattle, 98195, WA, USA
- Department of Bioengineering, University of Washington, 3720 15th Ave NE, Box 355061, Seattle, 98195, WA, USA
- Washington National Primate Research Center, University of Washington, 3018 Western Ave, Seattle, 98121, WA, USA
| | - Natalie Fung
- Thomas Lord Department of Computer Science, University of Southern California, 941 Bloom Walk, Los Angeles, 90089, CA, USA
| | - Jennifer L Hicks
- Department of Bioengineering, Stanford University, 443 Via Ortega, Stanford, 94305, CA, USA
| | - He Helen Huang
- Joint Department of Biomedical Engineering, North Carolina State University, 1840 Entrepreneur Dr Suite 4130, Raleigh, 27606, NC, USA
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, 333 S Columbia St, Chapel Hill, 27514, NC, USA
| | - David Reinkensmeyer
- Department of Mechanical and Aerospace Engineering, UCI Samueli School of Engineering, 3225 Engineering Gateway, Irvine, 92697, CA, USA
| | - Nicolas Schweighofer
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, 90089, CA, USA
- Division of Biokinesiology and Physical Therapy, University of Southern California, 1540 Alcazar St 155, Los Angeles, 90033, CA, USA
| | - Douglas Weber
- Department of Mechanical Engineering and the Neuroscience Institute, Carnegie Mellon University, 5000 Forbes Avenue, B12 Scaife Hall, Pittsburgh, 15213, PA, USA
| | - Katherine M Steele
- Department of Mechanical Engineering, University of Washington, 3900 E Stevens Way NE, Box 352600, Seattle, 98195, WA, USA
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Park KW, Mirian MS, McKeown MJ. Artificial intelligence-based video monitoring of movement disorders in the elderly: a review on current and future landscapes. Singapore Med J 2024; 65:141-149. [PMID: 38527298 PMCID: PMC11060643 DOI: 10.4103/singaporemedj.smj-2023-189] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/19/2023] [Indexed: 03/27/2024]
Abstract
ABSTRACT Due to global ageing, the burden of chronic movement and neurological disorders (Parkinson's disease and essential tremor) is rapidly increasing. Current diagnosis and monitoring of these disorders rely largely on face-to-face assessments utilising clinical rating scales, which are semi-subjective and time-consuming. To address these challenges, the utilisation of artificial intelligence (AI) has emerged. This review explores the advantages and challenges associated with using AI-driven video monitoring to care for elderly patients with movement disorders. The AI-based video monitoring systems offer improved efficiency and objectivity in remote patient monitoring, enabling real-time analysis of data, more uniform outcomes and augmented support for clinical trials. However, challenges, such as video quality, privacy compliance and noisy training labels, during development need to be addressed. Ultimately, the advancement of video monitoring for movement disorders is expected to evolve towards discreet, home-based evaluations during routine daily activities. This progression must incorporate data security, ethical considerations and adherence to regulatory standards.
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Affiliation(s)
- Kye Won Park
- Pacific Parkinson Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Maryam S Mirian
- Pacific Parkinson Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Martin J McKeown
- Pacific Parkinson Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, Canada
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6
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Single M, Bruhin LC, Colombo A, Möri K, Gerber SM, Lahr J, Krack P, Klöppel S, Müri RM, Mosimann UP, Nef T. A Transferable Lidar-Based Method to Conduct Contactless Assessments of Gait Parameters in Diverse Home-like Environments. SENSORS (BASEL, SWITZERLAND) 2024; 24:1172. [PMID: 38400329 PMCID: PMC10893300 DOI: 10.3390/s24041172] [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: 12/19/2023] [Revised: 02/08/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024]
Abstract
Gait abnormalities in older adults are linked to increased risks of falls, institutionalization, and mortality, necessitating accurate and frequent gait assessments beyond traditional clinical settings. Current methods, such as pressure-sensitive walkways, often lack the continuous natural environment monitoring needed to understand an individual's gait fully during their daily activities. To address this gap, we present a Lidar-based method capable of unobtrusively and continuously tracking human leg movements in diverse home-like environments, aiming to match the accuracy of a clinical reference measurement system. We developed a calibration-free step extraction algorithm based on mathematical morphology to realize Lidar-based gait analysis. Clinical gait parameters of 45 healthy individuals were measured using Lidar and reference systems (a pressure-sensitive walkway and a video recording system). Each participant participated in three predefined ambulation experiments by walking over the walkway. We observed linear relationships with strong positive correlations (R2>0.9) between the values of the gait parameters (step and stride length, step and stride time, cadence, and velocity) measured with the Lidar sensors and the pressure-sensitive walkway reference system. Moreover, the lower and upper 95% confidence intervals of all gait parameters were tight. The proposed algorithm can accurately derive gait parameters from Lidar data captured in home-like environments, with a performance not significantly less accurate than clinical reference systems.
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Affiliation(s)
- Michael Single
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Lena C. Bruhin
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Aaron Colombo
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Kevin Möri
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Stephan M. Gerber
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Jacob Lahr
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3012 Bern, Switzerland; (J.L.); (S.K.)
| | - Paul Krack
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, 3012 Bern, Switzerland
| | - Stefan Klöppel
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3012 Bern, Switzerland; (J.L.); (S.K.)
| | - René M. Müri
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Urs P. Mosimann
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Tobias Nef
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, 3012 Bern, Switzerland
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Siva P, Wong A, Hewston P, Ioannidis G, Adachi J, Rabinovich A, Lee AW, Papaioannou A. Automatic Radar-Based Step Length Measurement in the Home for Older Adults Living with Frailty. SENSORS (BASEL, SWITZERLAND) 2024; 24:1056. [PMID: 38400215 PMCID: PMC10891707 DOI: 10.3390/s24041056] [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: 01/03/2024] [Revised: 01/22/2024] [Accepted: 02/02/2024] [Indexed: 02/25/2024]
Abstract
With an aging population, numerous assistive and monitoring technologies are under development to enable older adults to age in place. To facilitate aging in place, predicting risk factors such as falls and hospitalization and providing early interventions are important. Much of the work on ambient monitoring for risk prediction has centered on gait speed analysis, utilizing privacy-preserving sensors like radar. Despite compelling evidence that monitoring step length in addition to gait speed is crucial for predicting risk, radar-based methods have not explored step length measurement in the home. Furthermore, laboratory experiments on step length measurement using radars are limited to proof-of-concept studies with few healthy subjects. To address this gap, a radar-based step length measurement system for the home is proposed based on detection and tracking using a radar point cloud followed by Doppler speed profiling of the torso to obtain step lengths in the home. The proposed method was evaluated in a clinical environment involving 35 frail older adults to establish its validity. Additionally, the method was assessed in people's homes, with 21 frail older adults who had participated in the clinical assessment. The proposed radar-based step length measurement method was compared to the gold-standard Zeno Walkway Gait Analysis System, revealing a 4.5 cm/8.3% error in a clinical setting. Furthermore, it exhibited excellent reliability (ICC(2,k) = 0.91, 95% CI 0.82 to 0.96) in uncontrolled home settings. The method also proved accurate in uncontrolled home settings, as indicated by a strong consistency (ICC(3,k) = 0.81 (95% CI 0.53 to 0.92)) between home measurements and in-clinic assessments.
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Affiliation(s)
- Parthipan Siva
- Chirp Inc., Waterloo, ON N2J 4R2, Canada
- Faculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - Alexander Wong
- Faculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - Patricia Hewston
- Geras Centre for Aging Research, St. Peter’s Hospital, Hamilton Health Sciences, Hamilton, ON L8M 1W9, Canada; (P.H.); (G.I.); (J.A.); (A.P.)
- Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - George Ioannidis
- Geras Centre for Aging Research, St. Peter’s Hospital, Hamilton Health Sciences, Hamilton, ON L8M 1W9, Canada; (P.H.); (G.I.); (J.A.); (A.P.)
- Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Jonathan Adachi
- Geras Centre for Aging Research, St. Peter’s Hospital, Hamilton Health Sciences, Hamilton, ON L8M 1W9, Canada; (P.H.); (G.I.); (J.A.); (A.P.)
- Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Alexander Rabinovich
- Department of Surgery, McMaster University, Hamilton, ON L8S 4L8, Canada
- ArthroBiologix Inc., Hamilton, ON L8L 5G4, Canada
| | - Andrea W. Lee
- Hamilton Health Sciences, Hamilton, ON L8N 3Z5, Canada;
| | - Alexandra Papaioannou
- Geras Centre for Aging Research, St. Peter’s Hospital, Hamilton Health Sciences, Hamilton, ON L8M 1W9, Canada; (P.H.); (G.I.); (J.A.); (A.P.)
- Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
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Xie Z, Ye F. SCALING: plug-n-play device-free indoor tracking. Sci Rep 2024; 14:2913. [PMID: 38316941 PMCID: PMC10844335 DOI: 10.1038/s41598-024-53524-z] [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: 01/16/2023] [Accepted: 02/01/2024] [Indexed: 02/07/2024] Open
Abstract
24/7 continuous recording of in-home daily trajectories is informative for health status assessment (e.g., monitoring Alzheimer's, dementia based on behavior patterns). Indoor device-free localization/tracking are ideal because no user efforts on wearing devices are needed. However, prior work mainly focused on improving the localization accuracy. They relied on well-calibrated sensor placements, which require hours of intensive manual setup and respective expertise, feasible only at small scale and by mostly researchers themselves. Scaling the deployments to tens or hundreds of real homes, however, would incur prohibitive manual efforts, and become infeasible for layman users. We present SCALING, a plug-and-play indoor trajectory monitoring system that layman users can easily set up by walking a one-minute loop trajectory after placing radar nodes on walls. It uses a self calibrating algorithm that estimates sensor locations through their distance measurements to the person walking the trajectory, a trivial effort without taxing layman users physically or cognitively. We evaluate SCALING via simulations and two testbeds (in lab and home configurations of sizes 3[Formula: see text]6 sq m and 4.5[Formula: see text]8.5 sq m). Experimental results demonstrate that SCALING outperformed the baseline using the approximate multidimensional scaling (MDS, the most relevant method in the context of self calibration) by 3.5 m/1.6 m in 80-percentile error of self calibration and tracking, respectively. Notably, only 1% degradation in performance has been observed with SCALING compared to the classical multilateration with known sensor locations (anchors), which costs hours of intensive calibrating effort. In addition, we conduct Monte Carlo experiments to numerically analyze the impact of sensor placements and develop practical guidelines for deployment in real life scenarios.
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Affiliation(s)
- Zongxing Xie
- Electrical and Computer Engineering Department, Stony Brook University, Stony Brook, 11794, USA.
| | - Fan Ye
- Electrical and Computer Engineering Department, Stony Brook University, Stony Brook, 11794, USA.
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Spangler J, Mitjans M, Collimore A, Gomes-Pires A, Levine DM, Tron R, Awad LN. Automation of Functional Mobility Assessments at Home Using a Multimodal Sensor System Integrating Inertial Measurement Units and Computer Vision (IMU-Vision). Phys Ther 2024; 104:pzad184. [PMID: 38159106 PMCID: PMC10851845 DOI: 10.1093/ptj/pzad184] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 12/05/2023] [Accepted: 12/14/2023] [Indexed: 01/03/2024]
Abstract
OBJECTIVE Functional movement assessments are routinely used to evaluate and track changes in mobility. The objective of this study was to evaluate a multimodal movement monitoring system developed for autonomous, home-based, functional movement assessment. METHODS Fifty frail and prefrail adults were recruited from the Brigham and Women's Hospital at Home program to evaluate the feasibility and accuracy of applying the multimodal movement monitoring system to autonomously recognize and score functional activities collected in the home. Study subjects completed sit-to-stand, standing balance (Romberg, semitandem, and tandem), and walking test activities in likeness to the Short Physical Performance Battery. Test activities were identified and scored manually and by the multimodal movement monitoring system's activity recognition and scoring algorithms, which were previously trained on lab-based biomechanical data to integrate wearable inertial measurement unit (IMU) and external red-blue-green-depth vision data. Feasibility was quantified as the proportion of completed tests that were analyzable. Accuracy was quantified as the degree of agreement between the actual and system-identified activities. In an exploratory analysis of a subset of functional activity data, the accuracy of a preliminary activity-scoring algorithm was also evaluated. RESULTS Activity recognition by the IMU-vision system had good feasibility and high accuracy. Of 271 test activities collected in the home, 217 (80%) were analyzable by the activity-recognition algorithm, which overall correctly identified 206 (95%) of the analyzable activities: 100% of walking, 97% of balance, and 82% of sit-to-stand activities (χ2(2) = 19.9). In the subset of 152 tests suitable for activity scoring, automatic and manual scores showed substantial agreement (Kw = 0.76 [0.69, 0.83]). CONCLUSIONS Autonomous recognition and scoring of home-based functional activities is enabled by a multimodal movement monitoring system that integrates inertial measurement unit and vision data. Further algorithm training with ecologically valid data and a kitted system that is independently usable by patients are needed before fully autonomous, functional movement assessment is realizable. IMPACT Functional movement assessments that can be administered in the home without a clinician present have the potential to democratize these evaluations and improve care access.
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Affiliation(s)
- Johanna Spangler
- Department of Physical Therapy, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, Massachusetts, USA
| | - Marc Mitjans
- Department of Mechanical Engineering, College of Engineering, Boston University, Boston, Massachusetts, USA
| | - Ashley Collimore
- Department of Physical Therapy, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, Massachusetts, USA
| | - Aysha Gomes-Pires
- Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - David M Levine
- Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Roberto Tron
- Department of Mechanical Engineering, College of Engineering, Boston University, Boston, Massachusetts, USA
| | - Louis N Awad
- Department of Physical Therapy, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, Massachusetts, USA
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10
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Esper CD, Valdovinos BY, Schneider RB. The Importance of Digital Health Literacy in an Evolving Parkinson's Disease Care System. JOURNAL OF PARKINSON'S DISEASE 2024:JPD230229. [PMID: 38250786 DOI: 10.3233/jpd-230229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Digital health technologies are growing at a rapid pace and changing the healthcare landscape. Our current understanding of digital health literacy in Parkinson's disease (PD) is limited. In this review, we discuss the potential challenges of low digital health literacy in PD with particular attention to telehealth, deep brain stimulation, wearable sensors, and smartphone applications. We also highlight inequities in access to digital health technologies. Future research is needed to better understand digital health literacy among individuals with PD and to develop effective solutions. We must invest resources to evaluate, understand, and enhance digital health literacy for individuals with PD.
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Affiliation(s)
| | | | - Ruth B Schneider
- Department of Neurology, University of Rochester, Rochester, NY, USA
- Center for Health + Technology, University of Rochester, Rochester, NY, USA
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11
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Nguyen DT, Zeng Q, Tian X, Chia P, Wu C, Liu Y, Ho JS. Ambient health sensing on passive surfaces using metamaterials. SCIENCE ADVANCES 2024; 10:eadj6613. [PMID: 38181071 PMCID: PMC10776016 DOI: 10.1126/sciadv.adj6613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 12/01/2023] [Indexed: 01/07/2024]
Abstract
Ambient sensors can continuously and unobtrusively monitor a person's health and well-being in everyday settings. Among various sensing modalities, wireless radio-frequency sensors offer exceptional sensitivity, immunity to lighting conditions, and privacy advantages. However, existing wireless sensors are susceptible to environmental interference and unable to capture detailed information from multiple body sites. Here, we present a technique to transform passive surfaces in the environment into highly sensitive and localized health sensors using metamaterials. Leveraging textiles' ubiquity, we engineer metamaterial textiles that mediate near-field interactions between wireless signals and the body for contactless and interference-free sensing. We demonstrate that passive surfaces functionalized by these metamaterials can provide hours-long cardiopulmonary monitoring with accuracy comparable to gold standards. We also show the potential of distributed sensors and machine learning for continuous blood pressure monitoring. Our approach enables passive environmental surfaces to be harnessed for ambient sensing and digital health applications.
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Affiliation(s)
- Dat T. Nguyen
- Integrative Sciences and Engineering Program, National University of Singapore, Singapore 119077, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
| | - Qihang Zeng
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
| | - Xi Tian
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
- SIA-NUS Digital Aviation Corporate Laboratory, National University of Singapore, Singapore 117602, Singapore
| | - Patrick Chia
- SIA-NUS Digital Aviation Corporate Laboratory, National University of Singapore, Singapore 117602, Singapore
| | - Changsheng Wu
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
- SIA-NUS Digital Aviation Corporate Laboratory, National University of Singapore, Singapore 117602, Singapore
- The N.1 Institute for Health, National University of Singapore, Singapore 117456, Singapore
- Department of Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore
| | - Yuxin Liu
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
- SIA-NUS Digital Aviation Corporate Laboratory, National University of Singapore, Singapore 117602, Singapore
- Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore
| | - John S. Ho
- Integrative Sciences and Engineering Program, National University of Singapore, Singapore 119077, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
- SIA-NUS Digital Aviation Corporate Laboratory, National University of Singapore, Singapore 117602, Singapore
- The N.1 Institute for Health, National University of Singapore, Singapore 117456, Singapore
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12
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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.
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13
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Packer E, Debelle H, Bailey HGB, Ciravegna F, Ireson N, Evers J, Niessen M, Shi JQ, Yarnall AJ, Rochester L, Alcock L, Del Din S. Translating digital healthcare to enhance clinical management: a protocol for an observational study using a digital health technology system to monitor medication adherence and its effect on mobility in people with Parkinson's. BMJ Open 2023; 13:e073388. [PMID: 37666560 PMCID: PMC10481731 DOI: 10.1136/bmjopen-2023-073388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 08/18/2023] [Indexed: 09/06/2023] Open
Abstract
INTRODUCTION In people with Parkinson's (PwP) impaired mobility is associated with an increased falls risk. To improve mobility, dopaminergic medication is typically prescribed, but complex medication regimens result in suboptimal adherence. Exploring medication adherence and its impact on mobility in PwP will provide essential insights to optimise medication regimens and improve mobility. However, this is typically assessed in controlled environments, during one-off clinical assessments. Digital health technology (DHT) presents a means to overcome this, by continuously and remotely monitoring mobility and medication adherence. This study aims to use a novel DHT system (DHTS) (comprising of a smartphone, smartwatch and inertial measurement unit (IMU)) to assess self-reported medication adherence, and its impact on digital mobility outcomes (DMOs) in PwP. METHODS AND ANALYSIS This single-centre, UK-based study, will recruit 55 participants with Parkinson's. Participants will complete a range of clinical, and physical assessments. Participants will interact with a DHTS over 7 days, to assess self-reported medication adherence, and monitor mobility and contextual factors in the real world. Participants will complete a motor complications diary (ON-OFF-Dyskinesia) throughout the monitoring period and, at the end, a questionnaire and series of open-text questions to evaluate DHTS usability. Feasibility of the DHTS and the motor complications diary will be assessed. Validated algorithms will quantify DMOs from IMU walking activity. Time series modelling and deep learning techniques will model and predict DMO response to medication and effects of contextual factors. This study will provide essential insights into medication adherence and its effect on real-world mobility in PwP, providing insights to optimise medication regimens. ETHICS AND DISSEMINATION Ethical approval was granted by London-142 Westminster Research Ethics Committee (REC: 21/PR/0469), protocol V.2.4. Results will be published in peer-reviewed journals. All participants will provide written, informed consent. TRIAL REGISTRATION NUMBER ISRCTN13156149.
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Affiliation(s)
- Emma Packer
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Héloïse Debelle
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Harry G B Bailey
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Fabio Ciravegna
- Dipartimento di Informatica, Università di Torino, Torino, Italy
| | - Neil Ireson
- Department of Computer Science and INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
| | | | | | - Jian Qing Shi
- Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, Guangdong, China
- National Center for Applied Mathematics, Shenzhen, Guangdong, China
| | - Alison J Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Based at The Newcastle upon Tyne Hospitals NHS Foundation Trust, NIHR Newcastle Biomedical Research Centre, Newcastle University, Newcastle upon Tyne, UK
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Based at The Newcastle upon Tyne Hospitals NHS Foundation Trust, NIHR Newcastle Biomedical Research Centre, Newcastle University, Newcastle upon Tyne, UK
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14
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Chen C, Ding S, Wang J. Digital health for aging populations. Nat Med 2023; 29:1623-1630. [PMID: 37464029 DOI: 10.1038/s41591-023-02391-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 05/09/2023] [Indexed: 07/20/2023]
Abstract
Growing life expectancy poses important societal challenges, placing an increasing burden on ever more strained health systems. Digital technologies offer tremendous potential for shifting from traditional medical routines to remote medicine and transforming our ability to manage health and independence in aging populations. In this Perspective, we summarize the current progress toward, and challenges and future opportunities of, harnessing digital technologies for effective geriatric care. Special attention is given to the role of wearables in assisting older adults to monitor their health and maintain independence at home. Challenges to the widespread future use of digital technologies in this population will be discussed, along with a vision for how such technologies will shape the future of healthy aging.
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Affiliation(s)
- Chuanrui Chen
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Shichao Ding
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Joseph Wang
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA.
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15
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Bloem BR, Post E, Hall DA. An Apple a Day to Keep the Parkinson's Disease Doctor Away? Ann Neurol 2023; 93:681-685. [PMID: 36708048 DOI: 10.1002/ana.26612] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 01/26/2023] [Indexed: 01/29/2023]
Abstract
It is challenging to reliably assess the motor features of Parkinson's disease in real-time. This has motivated the search for new digital outcomes that can objectively and remotely measure the severity of parkinsonian motor impairments over an extended period of time. The United States Food and Drug Administration (FDA) has recently granted a 510(k) clearance to the Rune Labs Kinematics System, an ambulatory, smartwatch-based monitoring system to remotely track tremor and dyskinesias in persons with Parkinson's disease. The FDA clearance means that this new digital approach can be regarded as being safe for use in daily practice, with acceptable correlations to clinically based measures. However, the immediate implications for clinicians are limited, because it remains to be demonstrated whether the digital signals correlate well to clinically meaningful outcomes at patient level. The impact on research is also restricted for now, as more validation studies are needed before this new digital approach can be used as primary or secondary endpoint in clinical trials. ANN NEUROL 2023;93:681-685.
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Affiliation(s)
- Bastiaan R Bloem
- Department of Neurology, Centre of Expertise for Parkinson & Movement Disorders, Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Erik Post
- Department of Neurology, Centre of Expertise for Parkinson & Movement Disorders, Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Deborah A Hall
- Department of Neurological Sciences, Rush University, Chicago, IL, USA
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16
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Klein C, Bloem BR. Research in movement disorders in 2022: a new era of biomarker and treatment development. Lancet Neurol 2023; 22:17-19. [PMID: 36517158 DOI: 10.1016/s1474-4422(22)00494-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 11/25/2022] [Indexed: 12/15/2022]
Affiliation(s)
- Christine Klein
- Institute of Neurogenetics, University of Lübeck, Lübeck, 23538, Germany.
| | - Bastiaan R Bloem
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Centre of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
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