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Sapienza S, Tsurkalenko O, Giraitis M, Mejia AC, Zelimkhanov G, Schwaninger I, Klucken J. Assessing the clinical utility of inertial sensors for home monitoring in Parkinson's disease: a comprehensive review. NPJ Parkinsons Dis 2024; 10:161. [PMID: 39164257 PMCID: PMC11335938 DOI: 10.1038/s41531-024-00755-6] [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: 11/27/2023] [Accepted: 07/24/2024] [Indexed: 08/22/2024] Open
Abstract
This review screened 296 articles on wearable sensors for home monitoring of people with Parkinson's Disease within the PubMed Database, from January 2017 to May 2023. A three-level maturity framework was applied for classifying the aims of 59 studies included: demonstrating technical efficacy, diagnostic sensitivity, or clinical utility. As secondary analysis, user experience (usability and patient adherence) was evaluated. The evidences provided by the studies were categorized and stratified according to the level of maturity. Our results indicate that approximately 75% of articles investigated diagnostic sensitivity, i.e. correlation of sensor-data with clinical parameters. Evidence of clinical utility, defined as improvement on health outcomes or clinical decisions after the use of the wearables, was found only in nine papers. A third of the articles included reported evidence of user experience. Future research should focus more on clinical utility, to facilitate the translation of research results within the management of Parkinson's Disease.
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Affiliation(s)
- Stefano Sapienza
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Olena Tsurkalenko
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
- Centre Hospitalier de Luxembourg (CHL), Rollengergronn-belair-nord, Luxembourg
| | - Marijus Giraitis
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
- Centre Hospitalier de Luxembourg (CHL), Rollengergronn-belair-nord, Luxembourg
| | - Alan Castro Mejia
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Gelani Zelimkhanov
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
- Centre Hospitalier de Luxembourg (CHL), Rollengergronn-belair-nord, Luxembourg
| | - Isabel Schwaninger
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Jochen Klucken
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg.
- Centre Hospitalier de Luxembourg (CHL), Rollengergronn-belair-nord, Luxembourg.
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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] [MESH Headings] [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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Zhu X, Chen Z, Ling Y, Luo N, Yin Q, Zhang Y, Zhao A, Ye G, Zhou H, Pan J, Zhou L, Cao L, Huang P, Zhang P, Chen C, Shi W, Lin S, Zhuang H, Zhao J, Ren K, Tan Y, Liu J. Motor symptom machine rating system for complete MDS-UPDRS III in Parkinson's disease: A proof-of-concept pilot study. Chin Med J (Engl) 2024; 137:1632-1634. [PMID: 38501363 PMCID: PMC11230756 DOI: 10.1097/cm9.0000000000003044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Indexed: 03/20/2024] Open
Affiliation(s)
- Xue Zhu
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Zhonglue Chen
- GYENNO Science Co., Ltd., Nanshan District, Shenzhen, Guangdong 518100, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, Hubei 430074, China
| | - Yun Ling
- GYENNO Science Co., Ltd., Nanshan District, Shenzhen, Guangdong 518100, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, Hubei 430074, China
| | - Ningdi Luo
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Qianyi Yin
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yichi Zhang
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Aonan Zhao
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Guanyu Ye
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Haiyan Zhou
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jing Pan
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Liche Zhou
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Linghao Cao
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Pei Huang
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Pingchen Zhang
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Cheng Chen
- GYENNO Science Co., Ltd., Nanshan District, Shenzhen, Guangdong 518100, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, Hubei 430074, China
| | - Weikun Shi
- GYENNO Science Co., Ltd., Nanshan District, Shenzhen, Guangdong 518100, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, Hubei 430074, China
| | - Shinuan Lin
- GYENNO Science Co., Ltd., Nanshan District, Shenzhen, Guangdong 518100, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, Hubei 430074, China
| | - Haimei Zhuang
- GYENNO Science Co., Ltd., Nanshan District, Shenzhen, Guangdong 518100, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, Hubei 430074, China
| | - Jin Zhao
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, Hubei 430074, China
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Kang Ren
- GYENNO Science Co., Ltd., Nanshan District, Shenzhen, Guangdong 518100, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, Hubei 430074, China
| | - Yuyan Tan
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jun Liu
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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Paredes-Acuna N, Utpadel-Fischler D, Ding K, Thakor NV, Cheng G. Upper limb intention tremor assessment: opportunities and challenges in wearable technology. J Neuroeng Rehabil 2024; 21:8. [PMID: 38218890 PMCID: PMC10787996 DOI: 10.1186/s12984-023-01302-9] [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: 08/02/2023] [Accepted: 12/26/2023] [Indexed: 01/15/2024] Open
Abstract
BACKGROUND Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature. METHODOLOGY & RESULTS This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table. CONCLUSIONS Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.
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Affiliation(s)
- Natalia Paredes-Acuna
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany.
| | - Daniel Utpadel-Fischler
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Keqin Ding
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Nitish V Thakor
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Gordon Cheng
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany
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Ali SM, Arjunan SP, Peter J, Perju-Dumbrava L, Ding C, Eller M, Raghav S, Kempster P, Motin MA, Radcliffe PJ, Kumar DK. Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 12:194-203. [PMID: 38196822 PMCID: PMC10776092 DOI: 10.1109/jtehm.2023.3329344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 06/20/2023] [Accepted: 10/23/2023] [Indexed: 01/11/2024]
Abstract
BACKGROUND Several validated clinical scales measure the severity of essential tremor (ET). Their assessments are subjective and can depend on familiarity and training with scoring systems. METHOD We propose a multi-modal sensing using a wearable inertial measurement unit for estimating scores on the Fahn-Tolosa-Marin tremor rating scale (FTM) and determine the classification accuracy within the tremor type. 17 ET participants and 18 healthy controls were recruited for the study. Two movement disorder neurologists who were blinded to prior clinical information viewed video recordings and scored the FTM. Participants drew a guided Archimedes spiral while wearing an inertial measurement unit placed at the mid-point between the lateral epicondyle of the humerus and the anatomical snuff box. Acceleration and gyroscope recordings were analyzed. The ratio of the power spectral density between frequency bands 0.5-4 Hz and 4-12 Hz, and the sum of power spectrum density over the entire spectrum of 2-74 Hz, for both accelerometer and gyroscope data, were computed. FTM was estimated using regression model and classification using SVM was validated using the leave-one-out method. RESULTS Regression analysis showed a moderate to good correlation when individual features were used, while correlation was high ([Formula: see text] = 0.818) when suitable features of the gyro and accelerometer were combined. The accuracy for two-class classification of the combined features using SVM was 91.42% while for four-class it was 68.57%. CONCLUSION Potential applications of this novel wearable sensing method using a wearable Inertial Measurement Unit (IMU) include monitoring of ET and clinical trials of new treatments for the disorder.
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Affiliation(s)
- Sheik Mohammed Ali
- Department of Electrical and Biomedical EngineeringRMIT UniversityMelbourneVIC3000Australia
| | | | - James Peter
- Neurosciences DepartmentMonash HealthClaytonVIC3168Australia
| | | | - Catherine Ding
- Neurosciences DepartmentMonash HealthClaytonVIC3168Australia
| | - Michael Eller
- Neurosciences DepartmentMonash HealthClaytonVIC3168Australia
| | - Sanjay Raghav
- Department of Electrical and Biomedical EngineeringRMIT UniversityMelbourneVIC3000Australia
- Neurosciences DepartmentMonash HealthClaytonVIC3168Australia
| | - Peter Kempster
- Neurosciences DepartmentMonash HealthClaytonVIC3168Australia
- Department of MedicineSchool of Clinical SciencesMonash UniversityClaytonVIC3800Australia
| | - Mohammod Abdul Motin
- Department of Electrical and Biomedical EngineeringRMIT UniversityMelbourneVIC3000Australia
- Department of Electrical and Electronic EngineeringRajshahi University of Engineering and TechnologyRajshahi6204Bangladesh
| | - P. J. Radcliffe
- Department of Electrical and Biomedical EngineeringRMIT UniversityMelbourneVIC3000Australia
| | - Dinesh Kant Kumar
- Department of Electrical and Biomedical EngineeringRMIT UniversityMelbourneVIC3000Australia
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Li Y, Wang Z, Dai H. Improved Parkinsonian tremor quantification based on automatic label modification and SVM with RBF kernel. Physiol Meas 2023; 44. [PMID: 36735971 DOI: 10.1088/1361-6579/acb8fe] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 02/03/2023] [Indexed: 02/05/2023]
Abstract
Objective. The quantitative assessment of Parkinsonian tremor, e.g. (0, 1, 2, 3, 4) according to the Movement Disorder Society-Unified Parkinson's Disease Rating Scale, is crucial for treating Parkinson's disease. However, the tremor amplitude constantly fluctuates due to environmental and psychological effects on the patient. In clinical practice, clinicians assess the tremor severity for a short duration, whereas manual tremor labeling relies on the clinician's physician experience. Therefore, automatic tremor quantification based on wearable inertial sensors and machine learning algorithms is affected by the manual labels of clinicians. In this study, an automatic modification method for the labels judged by clinicians is presented to improve Parkinsonian tremor quantitation.Approach. For the severe overlapping of dynamic feature range between different severities, an outlier modification algorithm (PCA-IQR) based on the combination of principal component analysis and interquartile range statistic rule is proposed to learn the blurred borders between different severity scores, thereby optimizing the labels. Afterward, according to the modified feature vectors, a support vector machine (SVM) with a radial basis function (RBF) kernel is proposed to classify the tremor severity. The classifier models of SVM with RBF kernel,k-nearest neighbors, and SVM with the linear kernel are compared.Main results. Experimental results show that the proposed method has high classification performance and excellent model generalization ability for tremor quantitation (accuracy: 97.93%, precision: 97.96%, sensitivity: 97.93%, F1-score: 97.94%).Significance. The proposed method may not only provide valuable assistance for clinicians to assess the tremor severity accurately, but also provides self-monitoring for patients at home and improve the assessment skills of clinicians.
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Affiliation(s)
- Yumin Li
- Lanzhou Jiaotong University, Lanzhou 730070, People's Republic of China.,Quanzhou Institute of Equipment Manufacturing, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Jinjiang 362216, People's Republic of China
| | - Zengwei Wang
- Quanzhou Institute of Equipment Manufacturing, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Jinjiang 362216, People's Republic of China
| | - Houde Dai
- Quanzhou Institute of Equipment Manufacturing, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Jinjiang 362216, People's Republic of China
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Fujikawa J, Morigaki R, Yamamoto N, Nakanishi H, Oda T, Izumi Y, Takagi Y. Diagnosis and Treatment of Tremor in Parkinson's Disease Using Mechanical Devices. LIFE (BASEL, SWITZERLAND) 2022; 13:life13010078. [PMID: 36676025 PMCID: PMC9863142 DOI: 10.3390/life13010078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/09/2022] [Accepted: 12/23/2022] [Indexed: 12/29/2022]
Abstract
BACKGROUND Parkinsonian tremors are sometimes confused with essential tremors or other conditions. Recently, researchers conducted several studies on tremor evaluation using wearable sensors and devices, which may support accurate diagnosis. Mechanical devices are also commonly used to treat tremors and have been actively researched and developed. Here, we aimed to review recent progress and the efficacy of the devices related to Parkinsonian tremors. METHODS The PubMed and Scopus databases were searched for articles. We searched for "Parkinson disease" and "tremor" and "device". RESULTS Eighty-six articles were selected by our systematic approach. Many studies demonstrated that the diagnosis and evaluation of tremors in patients with PD can be done accurately by machine learning algorithms. Mechanical devices for tremor suppression include deep brain stimulation (DBS), electrical muscle stimulation, and orthosis. In recent years, adaptive DBS and optimization of stimulation parameters have been studied to further improve treatment efficacy. CONCLUSIONS Due to developments using state-of-the-art techniques, effectiveness in diagnosing and evaluating tremor and suppressing it using these devices is satisfactorily high in many studies. However, other than DBS, no devices are in practical use. To acquire high-level evidence, large-scale studies and randomized controlled trials are needed for these devices.
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Affiliation(s)
- Joji Fujikawa
- Department of Advanced Brain Research, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
| | - Ryoma Morigaki
- Department of Advanced Brain Research, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
- Department of Neurosurgery, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
- Parkinson’s Disease and Dystonia Research Center, Tokushima University Hospital, 2-50-1 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
- Correspondence: ; Tel.: +81-88-633-7149
| | - Nobuaki Yamamoto
- Department of Neurology, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
| | - Hiroshi Nakanishi
- Department of Neurosurgery, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
- Beauty Life Corporation, 2 Kiba-Cho, Minato-Ku, Nagoya 455-0021, Aichi, Japan
| | - Teruo Oda
- Department of Advanced Brain Research, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
| | - Yuishin Izumi
- Parkinson’s Disease and Dystonia Research Center, Tokushima University Hospital, 2-50-1 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
- Department of Neurology, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
| | - Yasushi Takagi
- Department of Advanced Brain Research, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
- Department of Neurosurgery, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
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Deb R, An S, Bhat G, Shill H, Ogras UY. A Systematic Survey of Research Trends in Technology Usage for Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2022; 22:5491. [PMID: 35897995 PMCID: PMC9371095 DOI: 10.3390/s22155491] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/17/2022] [Accepted: 07/20/2022] [Indexed: 06/15/2023]
Abstract
Parkinson's disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The complexity of PD pathology is amplified due to its dependency on patient diaries and the neurologist's subjective assessment of clinical scales. A significant amount of recent research has explored new cost-effective and subjective assessment methods pertaining to PD symptoms to address this challenge. This article analyzes the application areas and use of mobile and wearable technology in PD research using the PRISMA methodology. Based on the published papers, we identify four significant fields of research: diagnosis, prognosis and monitoring, predicting response to treatment, and rehabilitation. Between January 2008 and December 2021, 31,718 articles were published in four databases: PubMed Central, Science Direct, IEEE Xplore, and MDPI. After removing unrelated articles, duplicate entries, non-English publications, and other articles that did not fulfill the selection criteria, we manually investigated 1559 articles in this review. Most of the articles (45%) were published during a recent four-year stretch (2018-2021), and 19% of the articles were published in 2021 alone. This trend reflects the research community's growing interest in assessing PD with wearable devices, particularly in the last four years of the period under study. We conclude that there is a substantial and steady growth in the use of mobile technology in the PD contexts. We share our automated script and the detailed results with the public, making the review reproducible for future publications.
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Affiliation(s)
| | - Sizhe An
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA;
| | - Ganapati Bhat
- School of Electrical Engineering & Computer Science, Washington State University, Pullman, WA 99164, USA;
| | - Holly Shill
- Lonnie and Muhammad Ali Movement Disorder Center, Phoenix, AZ 85013, USA;
| | - Umit Y. Ogras
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA;
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Ma C, Zhang P, Wang J, Zhang J, Pan L, Li X, Yin C, Li A, Zong R, Zhang Z. Objective quantification of the severity of postural tremor based on kinematic parameters: A multi-sensory fusion study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 219:106741. [PMID: 35338882 DOI: 10.1016/j.cmpb.2022.106741] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 02/27/2022] [Accepted: 03/08/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Current clinical assessments of essential tremor (ET) are primarily based on expert consultation combined with reviewing patient complaints, physician expertise, and diagnostic experience. Thus, traditional evaluation methods often lead to biased diagnostic results. There is a clinical demand for a method that can objectively quantify the severity of the patient's disease. METHODS This study aims to develop an artificial intelligence-aided diagnosis method based on multi-sensory fusion wearables. The experiment relies on a rigorous clinical trial paradigm to collect multi-modal fusion of signals from 98 ET patients. At the same time, three clinicians scored independently, and the consensus score obtained was used as the ground truth for the machine learning models. RESULTS Sixty kinematic parameters were extracted from the signals recorded by the nine-axis inertial measurement unit (IMU). The results showed that most of the features obtained by IMU could effectively characterize the severity of the tremors. The accuracy of the optimal model for three tasks classifying five severity levels was 97.71%, 97.54%, and 97.72%, respectively. CONCLUSIONS This paper reports the first attempt to combine multiple feature selection and machine learning algorithms for fine-grained automatic quantification of postural tremor in ET patients. The promising results showed the potential of the proposed approach to quantify the severity of ET objectively.
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Affiliation(s)
- Chenbin Ma
- Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, 100853, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, 100191, Beijing, China; School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China; Shenyuan Honors College, Beihang University, 100191, Beijing, China
| | - Peng Zhang
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, 100191, Beijing, China; School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China
| | - Jiachen Wang
- Medical School of Chinese PLA, 100853, Beijing, China
| | - Jian Zhang
- Medical School of Chinese PLA, 100853, Beijing, China
| | - Longsheng Pan
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, 100853, Beijing, China
| | - Xuemei Li
- Clinics of Cadre, Department of Outpatient, First Medical Center of Chinese PLA General Hospital, 100853, Beijing, China
| | - Chunyu Yin
- Clinics of Cadre, Department of Outpatient, First Medical Center of Chinese PLA General Hospital, 100853, Beijing, China
| | - Ailing Li
- Pusheng Yixin (Beijing) Hospital Management Co., Ltd, 100020, Beijing, China
| | - Rui Zong
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, 100853, Beijing, China.
| | - Zhengbo Zhang
- Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, 100853, Beijing, China.
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Ileșan RR, Cordoș CG, Mihăilă LI, Fleșar R, Popescu AS, Perju-Dumbravă L, Faragó P. Proof of Concept in Artificial-Intelligence-Based Wearable Gait Monitoring for Parkinson's Disease Management Optimization. BIOSENSORS 2022; 12:bios12040189. [PMID: 35448249 PMCID: PMC9027339 DOI: 10.3390/bios12040189] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/10/2022] [Accepted: 03/17/2022] [Indexed: 05/04/2023]
Abstract
Parkinson's disease (PD) is the second most common progressive neurodegenerative disorder, affecting 6.2 million patients and causing disability and decreased quality of life. The research is oriented nowadays toward artificial intelligence (AI)-based wearables for early diagnosis and long-term PD monitoring. Our primary objective is the monitoring and assessment of gait in PD patients. We propose a wearable physiograph for qualitative and quantitative gait assessment, which performs bilateral tracking of the foot biomechanics and unilateral tracking of arm balance. Gait patterns are assessed by means of correlation. The surface plot of a correlation coefficient matrix, generated from the recorded signals, is classified using convolutional neural networks into physiological or PD-specific gait. The novelty is given by the proposed AI-based decisional support procedure for gait assessment. A proof of concept of the proposed physiograph is validated in a clinical environment on five patients and five healthy controls, proving to be a feasible solution for ubiquitous gait monitoring and assessment in PD. PD management demonstrates the complexity of the human body. A platform empowering multidisciplinary, AI-evidence-based decision support assessments for optimal dosing between drug and non-drug therapy could lay the foundation for affordable precision medicine.
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Affiliation(s)
- Robert Radu Ileșan
- Department of Neurology and Pediatric Neurology, Faculty of Medicine, University of Medicine and Pharmacy “Iuliu Hatieganu” Cluj-Napoca, 400012 Cluj-Napoca, Romania; (R.R.I.); (A.-S.P.); (L.P.-D.)
- Clinic of Oral and Cranio-Maxillofacial Surgery, University Hospital Basel, CH-4031 Basel, Switzerland
| | - Claudia-Georgiana Cordoș
- Bases of Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (C.-G.C.); (L.-I.M.)
| | - Laura-Ioana Mihăilă
- Bases of Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (C.-G.C.); (L.-I.M.)
| | - Radu Fleșar
- Computer Science, Faculty of Mathematics and Computer Science, West University of Timișoara, 300223 Timișoara, Romania;
| | - Ana-Sorina Popescu
- Department of Neurology and Pediatric Neurology, Faculty of Medicine, University of Medicine and Pharmacy “Iuliu Hatieganu” Cluj-Napoca, 400012 Cluj-Napoca, Romania; (R.R.I.); (A.-S.P.); (L.P.-D.)
| | - Lăcrămioara Perju-Dumbravă
- Department of Neurology and Pediatric Neurology, Faculty of Medicine, University of Medicine and Pharmacy “Iuliu Hatieganu” Cluj-Napoca, 400012 Cluj-Napoca, Romania; (R.R.I.); (A.-S.P.); (L.P.-D.)
| | - Paul Faragó
- Bases of Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (C.-G.C.); (L.-I.M.)
- Correspondence:
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11
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Gopal A, Hsu WY, Allen DD, Bove R. Remote Assessments of Hand Function in Neurological Disorders: Systematic Review. JMIR Rehabil Assist Technol 2022; 9:e33157. [PMID: 35262502 PMCID: PMC8943610 DOI: 10.2196/33157] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 01/17/2022] [Accepted: 01/26/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Loss of fine motor skills is observed in many neurological diseases, and remote monitoring assessments can aid in early diagnosis and intervention. Hand function can be regularly assessed to monitor loss of fine motor skills in people with central nervous system disorders; however, there are challenges to in-clinic assessments. Remotely assessing hand function could facilitate monitoring and supporting of early diagnosis and intervention when warranted. OBJECTIVE Remote assessments can facilitate the tracking of limitations, aiding in early diagnosis and intervention. This study aims to systematically review existing evidence regarding the remote assessment of hand function in populations with chronic neurological dysfunction. METHODS PubMed and MEDLINE, CINAHL, Web of Science, and Embase were searched for studies that reported remote assessment of hand function (ie, outside of traditional in-person clinical settings) in adults with chronic central nervous system disorders. We excluded studies that included participants with orthopedic upper limb dysfunction or used tools for intervention and treatment. We extracted data on the evaluated hand function domains, validity and reliability, feasibility, and stage of development. RESULTS In total, 74 studies met the inclusion criteria for Parkinson disease (n=57, 77% studies), stroke (n=9, 12%), multiple sclerosis (n=6, 8%), spinal cord injury (n=1, 1%), and amyotrophic lateral sclerosis (n=1, 1%). Three assessment modalities were identified: external device (eg, wrist-worn accelerometer), smartphone or tablet, and telerehabilitation. The feasibility and overall participant acceptability were high. The most common hand function domains assessed included finger tapping speed (fine motor control and rigidity), hand tremor (pharmacological and rehabilitation efficacy), and finger dexterity (manipulation of small objects required for daily tasks) and handwriting (coordination). Although validity and reliability data were heterogeneous across studies, statistically significant correlations with traditional in-clinic metrics were most commonly reported for telerehabilitation and smartphone or tablet apps. The most readily implementable assessments were smartphone or tablet-based. CONCLUSIONS The findings show that remote assessment of hand function is feasible in neurological disorders. Although varied, the assessments allow clinicians to objectively record performance in multiple hand function domains, improving the reliability of traditional in-clinic assessments. Remote assessments, particularly via telerehabilitation and smartphone- or tablet-based apps that align with in-clinic metrics, facilitate clinic to home transitions, have few barriers to implementation, and prompt remote identification and treatment of hand function impairments.
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Affiliation(s)
- Arpita Gopal
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Wan-Yu Hsu
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Diane D Allen
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco/San Francisco State University, San Francisco, CA, United States
| | - Riley Bove
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
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12
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Ma C, Li D, Pan L, Li X, Yin C, Li A, Zhang Z, Zong R. Quantitative assessment of essential tremor based on machine learning methods using wearable device. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103244] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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13
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Ancona S, Faraci FD, Khatab E, Fiorillo L, Gnarra O, Nef T, Bassetti CLA, Bargiotas P. Wearables in the home-based assessment of abnormal movements in Parkinson's disease: a systematic review of the literature. J Neurol 2022; 269:100-110. [PMID: 33409603 DOI: 10.1007/s00415-020-10350-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 11/19/2020] [Accepted: 12/04/2020] [Indexed: 12/01/2022]
Abstract
At present, the standard practices for home-based assessments of abnormal movements in Parkinson's disease (PD) are based either on subjective tools or on objective measures that often fail to capture day-to-day fluctuations and long-term information in real-life conditions in a way that patient's compliance and privacy are secured. The employment of wearable technologies in PD represents a great paradigm shift in healthcare remote diagnostics and therapeutics monitoring. However, their applicability in everyday clinical practice seems to be still limited. We carried out a systematic search across the Medline Database. In total, 246 publications, published until 1 June 2020, were identified. Among them, 26 reports met the inclusion criteria and were included in the present review. We focused more on clinically relevant aspects of wearables' application including feasibility and efficacy of the assessment, the number, type and body position of the wearable devices, type of PD motor symptom, environment and duration of assessments and validation methodology. The aim of this review is to provide a systematic overview of the current knowledge and state-of-the-art of the home-based assessment of motor symptoms and fluctuations in PD patients using wearable technology, highlighting current problems and laying foundations for future works.
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Affiliation(s)
- Stefania Ancona
- Department of Neurology, University Hospital Bern (Inselspital), University of Bern, Bern, Switzerland
| | - Francesca D Faraci
- Institute for Information Systems and Networking, University of Applied Sciences and Arts of Southern Switzerland, Manno, Switzerland
| | - Elina Khatab
- Department of Neurology, Medical School, University of Cyprus, Nicosia, Cyprus
| | - Luigi Fiorillo
- Institute for Information Systems and Networking, University of Applied Sciences and Arts of Southern Switzerland, Manno, Switzerland.,Institute of Informatics, University of Bern, Bern, Switzerland
| | - Oriella Gnarra
- Department of Neurology, University Hospital Bern (Inselspital), University of Bern, Bern, Switzerland.,Sensory-Motor System Lab, IRIS, ETH Zurich, Zurich, Switzerland.,Neurotec, Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.,ARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation, University of Bern, Bern, Switzerland
| | - Tobias Nef
- Department of Neurology, University Hospital Bern (Inselspital), University of Bern, Bern, Switzerland.,Neurotec, Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.,ARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation, University of Bern, Bern, Switzerland
| | - Claudio L A Bassetti
- Department of Neurology, University Hospital Bern (Inselspital), University of Bern, Bern, Switzerland.,Neurotec, Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Panagiotis Bargiotas
- Department of Neurology, University Hospital Bern (Inselspital), University of Bern, Bern, Switzerland. .,Department of Neurology, Medical School, University of Cyprus, Nicosia, Cyprus.
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14
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Godkin FE, Turner E, Demnati Y, Vert A, Roberts A, Swartz RH, McLaughlin PM, Weber KS, Thai V, Beyer KB, Cornish B, Abrahao A, Black SE, Masellis M, Zinman L, Beaton D, Binns MA, Chau V, Kwan D, Lim A, Munoz DP, Strother SC, Sunderland KM, Tan B, McIlroy WE, Van Ooteghem K. Feasibility of a continuous, multi-sensor remote health monitoring approach in persons living with neurodegenerative disease. J Neurol 2021; 269:2673-2686. [PMID: 34705114 PMCID: PMC8548705 DOI: 10.1007/s00415-021-10831-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 01/02/2023]
Abstract
BACKGROUND Remote health monitoring with wearable sensor technology may positively impact patient self-management and clinical care. In individuals with complex health conditions, multi-sensor wear may yield meaningful information about health-related behaviors. Despite available technology, feasibility of device-wearing in daily life has received little attention in persons with physical or cognitive limitations. This mixed methods study assessed the feasibility of continuous, multi-sensor wear in persons with cerebrovascular (CVD) or neurodegenerative disease (NDD). METHODS Thirty-nine participants with CVD, Alzheimer's disease/amnestic mild cognitive impairment, frontotemporal dementia, Parkinson's disease, or amyotrophic lateral sclerosis (median age 68 (45-83) years, 36% female) wore five devices (bilateral ankles and wrists, chest) continuously for a 7-day period. Adherence to device wearing was quantified by examining volume and pattern of device removal (non-wear). A thematic analysis of semi-structured de-brief interviews with participants and study partners was used to examine user acceptance. RESULTS Adherence to multi-sensor wear, defined as a minimum of three devices worn concurrently, was high (median 98.2% of the study period). Non-wear rates were low across all sensor locations (median 17-22 min/day), with significant differences between some locations (p = 0.006). Multi-sensor non-wear was higher for daytime versus nighttime wear (p < 0.001) and there was a small but significant increase in non-wear over the collection period (p = 0.04). Feedback from de-brief interviews suggested that multi-sensor wear was generally well accepted by both participants and study partners. CONCLUSION A continuous, multi-sensor remote health monitoring approach is feasible in a cohort of persons with CVD or NDD.
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Affiliation(s)
- F Elizabeth Godkin
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Erin Turner
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Youness Demnati
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Adam Vert
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Angela Roberts
- School of Communication Sciences and Disorders, Elborn College, Western University, London, ON, Canada.,Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA
| | - Richard H Swartz
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | | | - Kyle S Weber
- 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 Cornish
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Agessandro Abrahao
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Sandra E Black
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Mario Masellis
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Lorne Zinman
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Derek Beaton
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Malcolm A Binns
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Vivian Chau
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Donna Kwan
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Andrew Lim
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Douglas P Munoz
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Kelly M Sunderland
- Rotman Research Institute, Baycrest Health Sciences, 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
| | - Karen Van Ooteghem
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
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15
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Visvanathan R, Ranasinghe DC, Lange K, Wilson A, Dollard J, Boyle E, Jones K, Chesser M, Ingram K, Hoskins S, Pham C, Karnon J, Hill KD. Effectiveness of the Wearable Sensor based Ambient Intelligent Geriatric Management System (AmbIGeM) in Preventing Falls in Older People in Hospitals. J Gerontol A Biol Sci Med Sci 2021; 77:155-163. [PMID: 34153102 PMCID: PMC8751806 DOI: 10.1093/gerona/glab174] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Indexed: 12/02/2022] Open
Abstract
Background The Ambient Intelligent Geriatric Management (AmbIGeM) system augments best practice and involves a novel wearable sensor (accelerometer and gyroscope) worn by patients where the data captured by the sensor are interpreted by algorithms to trigger alerts on clinician handheld mobile devices when risk movements are detected. Methods A 3-cluster stepped-wedge pragmatic trial investigating the effect on the primary outcome of falls rate and secondary outcome of injurious fall and proportion of fallers. Three wards across 2 states were included. Patients aged ≥65 years were eligible. Patients requiring palliative care were excluded. The trial was registered with the Australia and New Zealand Clinical Trials registry, number 12617000981325. Results A total of 4924 older patients were admitted to the study wards with 1076 excluded and 3240 (1995 control, 1245 intervention) enrolled. The median proportion of study duration with valid readings per patient was 49% ((interquartile range [IQR] 25%-67%)). There was no significant difference between intervention and control relating to the falls rate (adjusted rate ratio = 1.41, 95% confidence interval [0.85, 2.34]; p = .192), proportion of fallers (odds ratio = 1.54, 95% confidence interval [0.91, 2.61]; p = .105), and injurious falls rate (adjusted rate ratio = 0.90, 95% confidence interval [0.38, 2.14]; p = .807). In a post hoc analysis, falls and injurious falls rate were reduced in the Geriatric Evaluation and Management Unit wards when the intervention period was compared to the control period. Conclusions The AmbIGeM system did not reduce the rate of falls, rate of injurious falls, or proportion of fallers. There remains a case for further exploration and refinement of this technology given the post hoc analysis findings with the Geriatric Evaluation and Management Unit wards. Clinical Trials Registration Number: 12617000981325
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Affiliation(s)
- Renuka Visvanathan
- Aged & Extended Care Services and Basil Hetzel Institute, The Queen Elizabeth Hospital, Central Adelaide Local Health Network and Adelaide Geriatrics Training and Research with Aged Care (GTRAC) Centre, Adelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, Woodville South, Australia
| | | | - Kylie Lange
- Adelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, North Terrace, Australia
| | - Anne Wilson
- Adelaide Geriatrics Training and Research with Aged Care (GTRAC) Centre, Adelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, Woodville South, SA, Australia.,School of Medicine, Flinders University of South Australia, Bedford Park, Australia
| | - Joanne Dollard
- Adelaide Geriatrics Training and Research with Aged Care (GTRAC) Centre, Adelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, Woodville South, Australia
| | - Eileen Boyle
- School of Physiotherapy and Exercise Science, Curtin University, Perth, WA, Australia
| | - Katherine Jones
- School of Physiotherapy and Exercise Science, Curtin University, Perth, WA, Australia
| | - Michael Chesser
- School of Computer Science, University of Adelaide, Adelaide, SA, Australia
| | - Katharine Ingram
- Department of Rehabilitation and Aged Care, Sir Charles Gairdner Hospital, Nedlands, WA, Australia
| | - Stephen Hoskins
- Aged & Extended Care Services, The Queen Elizabeth Hospital, Central Adelaide Local Health Network, Woodville South, SA, Australia
| | - Clarabelle Pham
- Flinders Health and Medical Research Institute, Flinders University, Adelaide, SA, Australia
| | - Jonathan Karnon
- Flinders Health and Medical Research Institute, Flinders University, Adelaide, SA, Australia
| | - Keith D Hill
- School of Physiotherapy and Exercise Science, Curtin University, Perth, WA, Australia.,Rehabilitation, Ageing and Independent Living and mi(RAIL) Research Centre, Monash University, Melbourne, Victoria, Australia
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16
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Dai H, Cai G, Lin Z, Wang Z, Ye Q. Validation of Inertial Sensing-Based Wearable Device for Tremor and Bradykinesia Quantification. IEEE J Biomed Health Inform 2021; 25:997-1005. [PMID: 32750961 DOI: 10.1109/jbhi.2020.3009319] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Neurologists judge the severity of Parkinsonian motor symptoms according to clinical scales, and their judgments exist inconsistent because of differences in clinical experience. Correspondingly, inertial sensing-based wearable devices (ISWDs) produce objective and standardized quantifications. However, ISWDs indirectly quantify symptoms by parametric modeling of angular velocities and linear accelerations nd trained by the judgments of several neurologists through supervised learning algorithms. Hence, the ISWD outputs are biased along with the scores provided by neurologists. To investigate the effectiveness ISWDs for Parkinsonian symptoms quantification, technical verification and clinical validation of both tremor and bradykinesia quantification methods were carried out. A total of 45 Parkinson's disease patients and 30 healthy controls performed the tremor and finger-tapping tasks, which were tracked simultaneously by an ISWD and a 6-axis high-precision electromagnetic tracking system (EMTS). The Unified Parkinson's Disease Rating Scale (UPDRS) prescribed parameters obtained from the EMTS, which directly provides linear and rotational displacements, were compared with the scores provided by both the ISWD and seven neurologists. EMTS-based parameters were regarded as the ground truth and were employed to train several common machine learning (ML) algorithms, i.e., support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF) algorithms. Inconsistency among the scores provided by the neurologists was proven. Besides, the quantification performance (sensitivity, specificity, and accuracy) of the ISWD employed with ML algorithms were better than that of the neurologists. Furthermore, EMTS can be utilized to both modify the quantification algorithms of ISWDs and improve the assessment skills of young neurologists.
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17
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Quantitative mobility measures complement the MDS-UPDRS for characterization of Parkinson's disease heterogeneity. Parkinsonism Relat Disord 2021; 84:105-111. [PMID: 33607526 DOI: 10.1016/j.parkreldis.2021.02.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 12/28/2020] [Accepted: 02/03/2021] [Indexed: 11/20/2022]
Abstract
INTRODUCTION Emerging technologies show promise for enhanced characterization of Parkinson's Disease (PD) motor manifestations. We evaluated quantitative mobility measures from a wearable device compared to the conventional motor assessment, the Movement Disorders Society-Unified PD Rating Scale part III (motor MDS-UPDRS). METHODS We evaluated 176 PD subjects (mean age 65, 65% male, 66% H&Y stage 2) during routine clinic visits using the motor MDS-UPDRS and a 10-min motor protocol with a body-fixed sensor (DynaPort MT, McRoberts BV), including the 32-ft walk, Timed Up and Go (TUG), and standing posture with eyes closed. Regression models examined 12 quantitative mobility measures for associations with (i) motor MDS-UPDRS, (ii) motor subtype (tremor dominant vs. postural instability/gait difficulty), (iii) Montreal Cognitive Assessment (MoCA), and (iv) physical functioning disability (PROMIS-29). All analyses included age, gender, and disease duration as covariates. Models iii-iv were secondarily adjusted for motor MDS-UPDRS. RESULTS Quantitative mobility measures from gait, TUG transitions, turning, and posture were significantly associated with motor MDS-UPDRS (7 of 12 measures, p < 0.05) and motor subtype (6 of 12 measures, p < 0.05). Compared with motor MDS-UPDRS, several quantitative mobility measures accounted for a 1.5- or 1.9-fold increased variance in either cognition or physical functioning disability, respectively. Among minimally-impaired subjects in the bottom quartile of motor MDS-UPDRS, including subjects with normal gait exam, the measures captured substantial residual motor heterogeneity. CONCLUSION Clinic-based quantitative mobility assessments using a wearable sensor captured features of motor performance beyond those obtained with the motor MDS-UPDRS and may offer enhanced characterization of disease heterogeneity.
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18
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Powers R, Etezadi-Amoli M, Arnold EM, Kianian S, Mance I, Gibiansky M, Trietsch D, Alvarado AS, Kretlow JD, Herrington TM, Brillman S, Huang N, Lin PT, Pham HA, Ullal AV. Smartwatch inertial sensors continuously monitor real-world motor fluctuations in Parkinson's disease. Sci Transl Med 2021; 13:13/579/eabd7865. [PMID: 33536284 DOI: 10.1126/scitranslmed.abd7865] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 01/11/2021] [Indexed: 12/19/2022]
Abstract
Longitudinal, remote monitoring of motor symptoms in Parkinson's disease (PD) could enable more precise treatment decisions. We developed the Motor fluctuations Monitor for Parkinson's Disease (MM4PD), an ambulatory monitoring system that used smartwatch inertial sensors to continuously track fluctuations in resting tremor and dyskinesia. We designed and validated MM4PD in 343 participants with PD, including a longitudinal study of up to 6 months in a 225-subject cohort. MM4PD measurements correlated to clinical evaluations of tremor severity (ρ = 0.80) and mapped to expert ratings of dyskinesia presence (P < 0.001) during in-clinic tasks. MM4PD captured symptom changes in response to treatment that matched the clinician's expectations in 94% of evaluated subjects. In the remaining 6% of cases, symptom data from MM4PD identified opportunities to make improvements in pharmacologic strategy. These results demonstrate the promise of MM4PD as a tool to support patient-clinician communication, medication titration, and clinical trial design.
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Affiliation(s)
| | | | | | - Sara Kianian
- Apple Inc., Cupertino, CA 95014, USA.,Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | | | | | | | | | | | - Todd M Herrington
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.,Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Salima Brillman
- Parkinson's Disease and Movement Center of Silicon Valley, Menlo Park, CA 94025, USA
| | - Nengchun Huang
- Silicon Valley Parkinson's Center, Los Gatos, CA 95032, USA
| | - Peter T Lin
- Silicon Valley Parkinson's Center, Los Gatos, CA 95032, USA
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19
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Stephenson D, Alexander R, Aggarwal V, Badawy R, Bain L, Bhatnagar R, Bloem BR, Boroojerdi B, Burton J, Cedarbaum JM, Cosman J, Dexter DT, Dockendorf M, Dorsey ER, Dowling AV, Evers LJW, Fisher K, Frasier M, Garcia-Gancedo L, Goldsack JC, Hill D, Hitchcock J, Hu MT, Lawton MP, Lee SJ, Lindemann M, Marek K, Mehrotra N, Meinders MJ, Minchik M, Oliva L, Romero K, Roussos G, Rubens R, Sadar S, Scheeren J, Sengoku E, Simuni T, Stebbins G, Taylor KI, Yang B, Zach N. Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science. Digit Biomark 2020; 4:28-49. [PMID: 33442579 PMCID: PMC7768153 DOI: 10.1159/000512500] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 10/23/2020] [Indexed: 12/22/2022] Open
Abstract
Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson's Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.
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Affiliation(s)
| | | | | | - Reham Badawy
- University of Birmingham, Birmingham, United Kingdom
| | - Lisa Bain
- Independent Medical Writer, Philadelphia, Pennsylvania, USA
| | | | - Bastiaan R. Bloem
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Nijmegen, The Netherlands
| | | | | | - Jesse M. Cedarbaum
- Critical Path Institute, Tucson, Arizona, USA
- Coeruleus Clinical Sciences LLC, Woodbridge, Connecticut, USA
| | - Josh Cosman
- Biogen, Cambridge, Massachusetts, USA
- AbbVie, Chicago, Illinois, USA
| | | | | | | | | | - Luc J. W. Evers
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Nijmegen, The Netherlands
| | | | - Mark Frasier
- Michael J. Fox Foundation, New York, New York, USA
| | | | | | - Derek Hill
- Critical Path Institute, Tucson, Arizona, USA
| | | | - Michele T. Hu
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | | | | | | | - Ken Marek
- Institute of Neurodegenerative Diseases, New Haven, Connecticut, USA
| | | | - Marjan J. Meinders
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Nijmegen, The Netherlands
| | | | | | | | - George Roussos
- Critical Path Institute, Tucson, Arizona, USA
- Birbeck College, University of London, London, United Kingdom
| | | | | | | | | | - Tanya Simuni
- Northwestern University, Evanston, Illinois, USA
| | | | - Kirsten I. Taylor
- F. Hoffmann-La Roche Ltd., Basel, Switzerland
- University of Basel, Basel, Switzerland
| | | | - Neta Zach
- Takeda, Cambridge, Massachusetts, USA
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20
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Shawen N, O'Brien MK, Venkatesan S, Lonini L, Simuni T, Hamilton JL, Ghaffari R, Rogers JA, Jayaraman A. Role of data measurement characteristics in the accurate detection of Parkinson's disease symptoms using wearable sensors. J Neuroeng Rehabil 2020; 17:52. [PMID: 32312287 PMCID: PMC7168958 DOI: 10.1186/s12984-020-00684-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 04/03/2020] [Indexed: 01/07/2023] Open
Abstract
Background Parkinson’s disease (PD) is a progressive neurological disease, with characteristic motor symptoms such as tremor and bradykinesia. There is a growing interest to continuously monitor these and other symptoms through body-worn sensor technology. However, limited battery life and memory capacity hinder the potential for continuous, long-term monitoring with these devices. There is little information available on the relative value of adding sensors, increasing sampling rate, or computing complex signal features, all of which may improve accuracy of symptom detection at the expense of computational resources. Here we build on a previous study to investigate the relationship between data measurement characteristics and accuracy when using wearable sensor data to classify tremor and bradykinesia in patients with PD. Methods Thirteen individuals with PD wore a flexible, skin-mounted sensor (collecting tri-axial accelerometer and gyroscope data) and a commercial smart watch (collecting tri-axial accelerometer data) on their predominantly affected hand. The participants performed a series of standardized motor tasks, during which a clinician scored the severity of tremor and bradykinesia in that limb. Machine learning models were trained on scored data to classify tremor and bradykinesia. Model performance was compared when using different types of sensors (accelerometer and/or gyroscope), different data sampling rates (up to 62.5 Hz), and different categories of pre-engineered features (up to 148 features). Performance was also compared between the flexible sensor and smart watch for each analysis. Results First, there was no effect of device type for classifying tremor symptoms (p > 0.34), but bradykinesia models incorporating gyroscope data performed slightly better (up to 0.05 AUROC) than other models (p = 0.01). Second, model performance decreased with sampling frequency (p < 0.001) for tremor, but not bradykinesia (p > 0.47). Finally, model performance for both symptoms was maintained after substantially reducing the feature set. Conclusions Our findings demonstrate the ability to simplify measurement characteristics from body-worn sensors while maintaining performance in PD symptom detection. Understanding the trade-off between model performance and data resolution is crucial to design efficient, accurate wearable sensing systems. This approach may improve the feasibility of long-term, continuous, and real-time monitoring of PD symptoms by reducing computational burden on wearable devices.
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Affiliation(s)
- Nicholas Shawen
- Max Nader Lab for Rehabilitation Technologies and Outcomes, Shirley Ryan AbilityLab, Chicago, IL, 60611, USA.,Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Megan K O'Brien
- Max Nader Lab for Rehabilitation Technologies and Outcomes, Shirley Ryan AbilityLab, Chicago, IL, 60611, USA.,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, 60611, USA
| | - Sanjeev Venkatesan
- Max Nader Lab for Rehabilitation Technologies and Outcomes, Shirley Ryan AbilityLab, Chicago, IL, 60611, USA.,Department of Computer Science, University of Illinois at Urbana-Champagne, Urbana, IL, 61801, USA
| | - Luca Lonini
- Max Nader Lab for Rehabilitation Technologies and Outcomes, Shirley Ryan AbilityLab, Chicago, IL, 60611, USA.,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, 60611, USA
| | - Tanya Simuni
- Department of Neurology, Northwestern University, Chicago, IL, 60611, USA
| | - Jamie L Hamilton
- The Michael J. Fox Foundation for Parkinson's Research, New York, NY, 10120, USA
| | - Roozbeh Ghaffari
- Center for Bio-Integrated Electronics, Departments of Materials Science and Engineering, Biomedical Engineering, Chemistry, Mechanical Engineering, Electrical Engineering and Computer Science, Neurological Surgery, Simpson Querrey Institute for Nano/Biotechnology, McCormick School of Engineering, Feinberg School of Medicine, Northwestern University, Evanston, IL, 60208, USA
| | - John A Rogers
- Center for Bio-Integrated Electronics, Departments of Materials Science and Engineering, Biomedical Engineering, Chemistry, Mechanical Engineering, Electrical Engineering and Computer Science, Neurological Surgery, Simpson Querrey Institute for Nano/Biotechnology, McCormick School of Engineering, Feinberg School of Medicine, Northwestern University, Evanston, IL, 60208, USA
| | - Arun Jayaraman
- Max Nader Lab for Rehabilitation Technologies and Outcomes, Shirley Ryan AbilityLab, Chicago, IL, 60611, USA. .,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, 60611, USA. .,Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL, 60611, USA.
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21
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Albani G, Ferraris C, Nerino R, Chimienti A, Pettiti G, Parisi F, Ferrari G, Cau N, Cimolin V, Azzaro C, Priano L, Mauro A. An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2019; 19:E4764. [PMID: 31684020 PMCID: PMC6864792 DOI: 10.3390/s19214764] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 10/30/2019] [Accepted: 10/31/2019] [Indexed: 01/30/2023]
Abstract
The increment of the prevalence of neurological diseases due to the trend in population aging demands for new strategies in disease management. In Parkinson's disease (PD), these strategies should aim at improving diagnosis accuracy and frequency of the clinical follow-up by means of decentralized cost-effective solutions. In this context, a system suitable for the remote monitoring of PD subjects is presented. It consists of the integration of two approaches investigated in our previous works, each one appropriate for the movement analysis of specific parts of the body: low-cost optical devices for the upper limbs and wearable sensors for the lower ones. The system performs the automated assessments of six motor tasks of the unified Parkinson's disease rating scale, and it is equipped with a gesture-based human machine interface designed to facilitate the user interaction and the system management. The usability of the system has been evaluated by means of standard questionnaires, and the accuracy of the automated assessment has been verified experimentally. The results demonstrate that the proposed solution represents a substantial improvement in PD assessment respect to the former two approaches treated separately, and a new example of an accurate, feasible and cost-effective mean for the decentralized management of PD.
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Affiliation(s)
- Giovanni Albani
- Istituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), Italy.
| | - Claudia Ferraris
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy.
| | - Roberto Nerino
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.
| | - Antonio Chimienti
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.
| | - Giuseppe Pettiti
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.
| | - Federico Parisi
- CNIT Research Unit of Parma and Department of Information Engineering, University of Parma, 43124 Parma, Italy.
| | - Gianluigi Ferrari
- CNIT Research Unit of Parma and Department of Information Engineering, University of Parma, 43124 Parma, Italy.
| | - Nicola Cau
- Istituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), Italy.
| | - Veronica Cimolin
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, Italy.
| | - Corrado Azzaro
- Istituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), Italy.
| | - Lorenzo Priano
- Istituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), Italy.
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy.
| | - Alessandro Mauro
- Istituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), Italy.
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy.
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22
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Martín-Vaquero J, Hernández Encinas A, Queiruga-Dios A, José Bullón J, Martínez-Nova A, Torreblanca González J, Bullón-Carbajo C. Review on Wearables to Monitor Foot Temperature in Diabetic Patients. SENSORS (BASEL, SWITZERLAND) 2019; 19:E776. [PMID: 30769799 PMCID: PMC6412611 DOI: 10.3390/s19040776] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 01/17/2019] [Accepted: 01/31/2019] [Indexed: 01/01/2023]
Abstract
One of the diseases that could affect diabetic patients is the diabetic foot problem. Unnoticed minor injuries and subsequent infection can lead to ischemic ulceration, and may end in a foot amputation. Preliminary studies have shown that there is a positive relationship between increased skin temperature and the pre⁻ulceration phase. Hence, we have carried out a review on wearables, medical devices, and sensors used specifically for collecting vital data. In particular, we are interested in the measure of the foot⁻temperature. Since there is a large amount of this type of medical wearables, we will focus on those used to measure temperature and developed in Spain.
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Affiliation(s)
- Jesús Martín-Vaquero
- Department of Applied Mathematics, University of Salamanca, E37008 Salamanca, Spain.
- ETSII Béjar, E37700 Béjar, Spain.
| | | | - Araceli Queiruga-Dios
- Department of Applied Mathematics, University of Salamanca, E37008 Salamanca, Spain.
- ETSII Béjar, E37700 Béjar, Spain.
| | - Juan José Bullón
- Department of Chemical and Textile Engineering, University of Salamanca, E37008 Salamanca, Spain.
- ETSII Béjar, E37700 Béjar, Spain.
| | - Alfonso Martínez-Nova
- Department of Nursing, University of Extremadura, E06006 Badajoz, Spain.
- Centro Universitario de Plasencia, E10600 Plasencia, Spain.
| | - Jose Torreblanca González
- Department of Applied Physics, University of Salamanca, E37008 Salamanca, Spain.
- ETSII Béjar, E37700 Béjar, Spain.
| | - Cristina Bullón-Carbajo
- Department of Nursing, University of Extremadura, E06006 Badajoz, Spain.
- Centro Universitario de Plasencia, E10600 Plasencia, Spain.
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