1
|
Rodriguez F, Krauss P, Kluckert J, Ryser F, Stieglitz L, Baumann C, Gassert R, Imbach L, Bichsel O. Continuous and Unconstrained Tremor Monitoring in Parkinson's Disease Using Supervised Machine Learning and Wearable Sensors. PARKINSON'S DISEASE 2024; 2024:5787563. [PMID: 38803413 PMCID: PMC11129907 DOI: 10.1155/2024/5787563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 03/24/2024] [Accepted: 04/24/2024] [Indexed: 05/29/2024]
Abstract
Background Accurately assessing the severity and frequency of fluctuating motor symptoms is important at all stages of Parkinson's disease management. Contrarily to time-consuming clinical testing or patient self-reporting with uncertain reliability, recordings with wearable sensors show promise as a tool for continuously and objectively assessing PD symptoms. While wearables-based clinical assessments during standardised and scripted tasks have been successfully implemented, assessments during unconstrained activity remain a challenge. Methods We developed and implemented a supervised machine learning algorithm, trained and tested on tremor scores. We evaluated the algorithm on a 67-hour database comprising sensor data and clinical tremor scores for 24 Parkinson patients at four extremities for periods of about 3 hours. A random 25% subset of the labelled samples was used as test data, the remainder as training data. Based on features extracted from the sensor data, a Support Vector Machine was trained to predict tremor severity. Due to the inherent imbalance in tremor scores, we applied dataset rebalancing techniques. Results Our classifier demonstrated robust performance in detecting tremor events with a sensitivity of 0.90 on the test-portion of the resampled dataset. The overall classification accuracy was high at 0.88. Conclusion We implemented an accurate classifier for tremor monitoring in free-living environments that can be trained even with modestly sized and imbalanced datasets. This advancement offers significant clinical value in continuously monitoring Parkinson's disease symptoms beyond the hospital setting, paving the way for personalized management of PD, timely therapeutic adjustments, and improved patient quality of life.
Collapse
Affiliation(s)
- Fernando Rodriguez
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Philipp Krauss
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Clinical Neuroscience Centre, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Neurosurgery, University Hospital Augsburg, Augsburg, Germany
| | - Jonas Kluckert
- Clinical Neuroscience Centre, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Neurology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Franziska Ryser
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Lennart Stieglitz
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Clinical Neuroscience Centre, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Christian Baumann
- Clinical Neuroscience Centre, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Neurology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Roger Gassert
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Lukas Imbach
- Swiss Epilepsy Center, Klinik Lengg, Zurich, Switzerland
| | - Oliver Bichsel
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Clinical Neuroscience Centre, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Battista L, Casali M, Brusa L, Radicati FG, Stocchi F. Clinical assessment of a new wearable tool for continuous and objective recording of motor fluctuations and ON/OFF states in patients with Parkinson's disease. PLoS One 2023; 18:e0287139. [PMID: 37796842 PMCID: PMC10553324 DOI: 10.1371/journal.pone.0287139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 05/30/2023] [Indexed: 10/07/2023] Open
Abstract
Clinical rating scales typically includes subjective evaluations, and their time-limited duration may fail to capture daily fluctuations in motor symptoms resulting from Parkinson's disease (PD). Recently, a new tool (i.e. the PD-Watch) has been proposed for the objective and continuous assessment of PD motor manifestations based on evaluating frequency data from a wrist-worn tri-axial accelerometer and identifying specific movement patterns typically associated with disorders. This reduces the probability of confusing physiological or pathological movements occurring at the same frequency. In this work, we present a new method for assessing motor fluctuations through a wrist-worn accelerometer. We also explore the agreement between the continuous data generated by the proposed method and data reported in the patient diaries. In this study, twelve PD patients were recruited with an overall recording duration of 528 hours. Results of this preliminary study show that the proposed tool has suitable and adequate performances for analysing the motor signs of PD patients, and the estimated sensitivity, specificity, and accuracy of the tool are 85%, 94%, and 91%, respectively.
Collapse
Affiliation(s)
| | - Miriam Casali
- Department of Neurology, Institute of Research and Medical Care IRCCS San Raffaele, Rome, Italy
| | - Livia Brusa
- Department of Neurology, Ospedale S. Eugenio, Rome, Italy
| | - Fabiana Giada Radicati
- Department of Neurology, Institute of Research and Medical Care IRCCS San Raffaele, Rome, Italy
| | - Fabrizio Stocchi
- Department of Neurology, Institute of Research and Medical Care IRCCS San Raffaele, Rome, Italy
| |
Collapse
|
4
|
Knudson KC, Gupta AS. Assessing Cerebellar Disorders with Wearable Inertial Sensor Data Using Time-Frequency and Autoregressive Hidden Markov Model Approaches. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22239454. [PMID: 36502155 PMCID: PMC9737930 DOI: 10.3390/s22239454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/19/2022] [Accepted: 11/29/2022] [Indexed: 05/30/2023]
Abstract
Wearable sensor data is relatively easily collected and provides direct measurements of movement that can be used to develop useful behavioral biomarkers. Sensitive and specific behavioral biomarkers for neurodegenerative diseases are critical to supporting early detection, drug development efforts, and targeted treatments. In this paper, we use autoregressive hidden Markov models and a time-frequency approach to create meaningful quantitative descriptions of behavioral characteristics of cerebellar ataxias from wearable inertial sensor data gathered during movement. We create a flexible and descriptive set of features derived from accelerometer and gyroscope data collected from wearable sensors worn while participants perform clinical assessment tasks, and use these data to estimate disease status and severity. A short period of data collection (<5 min) yields enough information to effectively separate patients with ataxia from healthy controls with very high accuracy, to separate ataxia from other neurodegenerative diseases such as Parkinson’s disease, and to provide estimates of disease severity.
Collapse
Affiliation(s)
- Karin C. Knudson
- Data Intensive Studies Center, Tufts University, Medford, MA 02155, USA
| | - Anoopum S. Gupta
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| |
Collapse
|
5
|
Xu Z, Shen B, Tang Y, Wu J, Wang J. Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:349-361. [PMID: 36939759 PMCID: PMC9590510 DOI: 10.1007/s43657-022-00051-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 03/12/2022] [Accepted: 03/28/2022] [Indexed: 11/27/2022]
Abstract
Despite recent advances in technology, clinical phenotyping of Parkinson's disease (PD) has remained relatively limited as current assessments are mainly based on empirical observation and subjective categorical judgment at the clinic. A lack of comprehensive, objective, and quantifiable clinical phenotyping data has hindered our capacity to diagnose, assess patients' conditions, discover pathogenesis, identify preclinical stages and clinical subtypes, and evaluate new therapies. Therefore, deep clinical phenotyping of PD patients is a necessary step towards understanding PD pathology and improving clinical care. In this review, we present a growing community consensus and perspective on how to clinically phenotype this disease, that is, to phenotype the entire course of disease progression by integrating capacity, performance, and perception approaches with state-of-the-art technology. We also explore the most studied aspects of PD deep clinical phenotypes, namely, bradykinesia, tremor, dyskinesia and motor fluctuation, gait impairment, speech impairment, and non-motor phenotypes.
Collapse
Affiliation(s)
- Zhiheng Xu
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Bo Shen
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Yilin Tang
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Jianjun Wu
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Jian Wang
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| |
Collapse
|
6
|
A Single Wearable Sensor for Gait Analysis in Parkinson’s Disease: A Preliminary Study. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Movement monitoring in patients with Parkinson’s disease (PD) is critical for quantifying disease progression and assessing how a subject responds to medication administration over time. In this work, we propose a continuous monitoring system based on a single wearable sensor placed on the lower back and an algorithm for gait parameters evaluation. In order to preliminarily validate the proposed system, seven PD subjects took part in an experimental protocol in preparation for a larger randomized controlled study. We validated the feasibility of our algorithm in a constrained environment through a laboratory scenario. Successively, it was tested in an unsupervised environment, such as the home scenario, for a total of almost 12 h of daily living activity data. During all phases of the experimental protocol, videos were shot to document the tasks. The obtained results showed a good accuracy of the proposed algorithm. For all PD subjects in the laboratory scenario, the algorithm for step identification reached a percentage error low of 2%, 99.13% of sensitivity and 100% of specificity. In the home scenario the Bland–Altman plot showed a mean difference of −3.29 and −1 between the algorithm and the video recording for walking bout detection and steps identification, respectively.
Collapse
|
7
|
Vescio B, Quattrone A, Nisticò R, Crasà M, Quattrone A. Wearable Devices for Assessment of Tremor. Front Neurol 2021; 12:680011. [PMID: 34177785 PMCID: PMC8226078 DOI: 10.3389/fneur.2021.680011] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 05/05/2021] [Indexed: 12/28/2022] Open
Abstract
Tremor is an impairing symptom associated with several neurological diseases. Some of such diseases are neurodegenerative, and tremor characterization may be of help in differential diagnosis. To date, electromyography (EMG) is the gold standard for the analysis and diagnosis of tremors. In the last decade, however, several studies have been conducted for the validation of different techniques and new, non-invasive, portable, or even wearable devices have been recently proposed as complementary tools to EMG for a better characterization of tremors. Such devices have proven to be useful for monitoring the efficacy of therapies or even aiding in differential diagnosis. The aim of this review is to present systematically such new solutions, trying to highlight their potentialities and limitations, with a hint to future developments.
Collapse
Affiliation(s)
| | - Andrea Quattrone
- Department of Medical and Surgical Sciences, Institute of Neurology, Magna Græcia University, Catanzaro, Italy
| | - Rita Nisticò
- Neuroimaging Unit, Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Catanzaro, Italy
| | - Marianna Crasà
- Department of Medical and Surgical Sciences, Neuroscience Research Center, Magna Græcia University, Catanzaro, Italy
| | - Aldo Quattrone
- Neuroimaging Unit, Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Catanzaro, Italy
- Department of Medical and Surgical Sciences, Neuroscience Research Center, Magna Græcia University, Catanzaro, Italy
| |
Collapse
|
8
|
Battista L, Romaniello A. A wearable tool for continuous monitoring of movement disorders: clinical assessment and comparison with tremor scores. Neurol Sci 2021; 42:4241-4248. [PMID: 33594538 DOI: 10.1007/s10072-021-05120-6] [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: 04/03/2020] [Accepted: 02/04/2021] [Indexed: 10/22/2022]
Abstract
BACKGROUND The current gold standard for evaluating normal and impaired motor performances includes the use of the information provided by the patient and the Unified Parkinson's Disease Rating Scale (UPDRS). However, clinical rating scales are typically subjective and their time-limited duration may fail to capture daily fluctuations in motor symptoms resulting from Parkinson's disease. Recently, a new tool has been proposed for objective and continuous assessment of movement disorders based on the evaluation of frequential data content from multi-axial sensors and the identification of specific movement patterns typically associated with disorders. This reduces the probability of confusing physiological or pathological movements occurring at the same frequency with a different movement pattern. However, the data provided by the tool have not yet been compared with the information provided by the typically used clinical rating scales. OBJECTIVES The aim of this work is to investigate the possible relationship between UPRDS scores and the information provided by the tool for continuous and long-term monitoring. MATERIALS AND METHODS In this study, 20 patients with hand tremor were recruited. The UPDRS scoring was performed by a neurologist. Then, continuous monitoring was performed; data were acquired by means of the proposed wrist-worn-device "PD-Watch" for 24 h and then processed in order to get information and indexes on motor symptoms. Finally, these indexes were correlated to the UPDRS scores. RESULTS Results show that the concise indexes provided by the tool correlate well with some items in UPDRS Part III, and this correlation has allowed to provide a more direct and immediate meaning to the values of the concise indexes detected by the tool. CONCLUSIONS While results need to be extended with further studies, this can be considered useful information in the context of clinical trials and routine clinical practice for assessing motor symptoms and movement disorders.
Collapse
Affiliation(s)
- Luigi Battista
- Faculty of Medicine and Surgery, Catholic University of the Sacred Heart, Sede di Potenza, Potenza, Italy.
| | - Antonietta Romaniello
- Department of Neurosurgery, Neurology Unit, Hospital of Potenza "San Carlo", Via Potito Petrone, Potenza, Italy
| |
Collapse
|
9
|
Sica M, Tedesco S, Crowe C, Kenny L, Moore K, Timmons S, Barton J, O’Flynn B, Komaris DS. Continuous home monitoring of Parkinson's disease using inertial sensors: A systematic review. PLoS One 2021; 16:e0246528. [PMID: 33539481 PMCID: PMC7861548 DOI: 10.1371/journal.pone.0246528] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 01/20/2021] [Indexed: 02/01/2023] Open
Abstract
Parkinson’s disease (PD) is a progressive neurological disorder of the central nervous system that deteriorates motor functions, while it is also accompanied by a large diversity of non-motor symptoms such as cognitive impairment and mood changes, hallucinations, and sleep disturbance. Parkinsonism is evaluated during clinical examinations and appropriate medical treatments are directed towards alleviating symptoms. Tri-axial accelerometers, gyroscopes, and magnetometers could be adopted to support clinicians in the decision-making process by objectively quantifying the patient’s condition. In this context, at-home data collections aim to capture motor function during daily living and unobstructedly assess the patients’ status and the disease’s symptoms for prolonged time periods. This review aims to collate existing literature on PD monitoring using inertial sensors while it focuses on papers with at least one free-living data capture unsupervised either directly or via videotapes. Twenty-four papers were selected at the end of the process: fourteen investigated gait impairments, eight of which focused on walking, three on turning, two on falls, and one on physical activity; ten articles on the other hand examined symptoms, including bradykinesia, tremor, dyskinesia, and motor state fluctuations in the on/off phenomenon. In summary, inertial sensors are capable of gathering data over a long period of time and have the potential to facilitate the monitoring of people with Parkinson’s, providing relevant information about their motor status. Concerning gait impairments, kinematic parameters (such as duration of gait cycle, step length, and velocity) were typically used to discern PD from healthy subjects, whereas for symptoms’ assessment, researchers were capable of achieving accuracies of over 90% in a free-living environment. Further investigations should be focused on the development of ad-hoc hardware and software capable of providing real-time feedback to clinicians and patients. In addition, features such as the wearability of the system and user comfort, set-up process, and instructions for use, need to be strongly considered in the development of wearable sensors for PD monitoring.
Collapse
Affiliation(s)
- Marco Sica
- Tyndall National Institute, University College Cork, Cork, Ireland
- * E-mail:
| | | | - Colum Crowe
- Tyndall National Institute, University College Cork, Cork, Ireland
| | - Lorna Kenny
- Centre for Gerontology and Rehabilitation, University College Cork, Cork, Ireland
| | - Kevin Moore
- Centre for Gerontology and Rehabilitation, University College Cork, Cork, Ireland
| | - Suzanne Timmons
- Centre for Gerontology and Rehabilitation, University College Cork, Cork, Ireland
| | - John Barton
- Tyndall National Institute, University College Cork, Cork, Ireland
| | - Brendan O’Flynn
- Tyndall National Institute, University College Cork, Cork, Ireland
| | | |
Collapse
|
10
|
Vescio B, Nisticò R, Augimeri A, Quattrone A, Crasà M, Quattrone A. Development and Validation of a New Wearable Mobile Device for the Automated Detection of Resting Tremor in Parkinson's Disease and Essential Tremor. Diagnostics (Basel) 2021; 11:200. [PMID: 33573076 PMCID: PMC7911899 DOI: 10.3390/diagnostics11020200] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/13/2021] [Accepted: 01/25/2021] [Indexed: 12/12/2022] Open
Abstract
Involuntary tremor at rest is observed in patients with Parkinson's disease (PD) or essential tremor (ET). Electromyography (EMG) studies have shown that phase displacement between antagonistic muscles at prevalent tremor frequency can accurately differentiate resting tremor in PD from that detected in ET. Currently, phase evaluation is qualitative in most cases. The aim of this study is to develop and validate a new mobile tool for the automated and quantitative characterization of phase displacement (resting tremor pattern) in ambulatory clinical settings. A new low-cost, wearable mobile device, called µEMG, is described, based on low-end instrumentation amplifiers and simple digital signal processing (DSP) capabilities. Measurements of resting tremor characteristics from this new device were compared with standard EMG. A good level of agreement was found in a sample of 21 subjects (14 PD patients with alternating resting tremor pattern and 7 ET patients with synchronous resting tremor pattern). Our results demonstrate that tremor analysis using µEMG is easy to perform and it can be used in routine clinical practice for the automated quantification of resting tremor patterns. Moreover, the measurement process is handy and operator-independent.
Collapse
Affiliation(s)
- Basilio Vescio
- Biotecnomed S.C.aR.L., 88100 Catanzaro, Italy; (B.V.); (A.A.)
| | - Rita Nisticò
- Neuroimaging Unit, Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), 88100 Catanzaro, Italy;
| | | | - Andrea Quattrone
- Institute of Neurology, Magna Græcia University, 88100 Catanzaro, Italy;
| | - Marianna Crasà
- Neuroscience Research Center, Magna Græcia University, 88100 Catanzaro, Italy;
| | - Aldo Quattrone
- Neuroimaging Unit, Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), 88100 Catanzaro, Italy;
- Neuroscience Research Center, Magna Græcia University, 88100 Catanzaro, Italy;
| |
Collapse
|
11
|
Evaluation of Wearable Sensor Devices in Parkinson's Disease: A Review of Current Status and Future Prospects. PARKINSONS DISEASE 2020; 2020:4693019. [PMID: 33029343 PMCID: PMC7530475 DOI: 10.1155/2020/4693019] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 07/07/2020] [Accepted: 07/13/2020] [Indexed: 01/23/2023]
Abstract
Parkinson's disease (PD) decreases the quality of life of the affected individuals. The incidence of PD is expected to increase given the growing aging population. Motor symptoms associated with PD render the patients unable to self-care and function properly. Given that several drugs have been developed to control motor symptoms, highly sensitive scales for clinical evaluation of drug efficacy are needed. Among such scales, the objective and continuous evaluation of wearable devices is increasingly utilized by clinicians and patients. Several electronic technologies have revolutionized the clinical monitoring of PD development, especially its motor symptoms. Here, we review and discuss the recent advances in the development of wearable devices for bradykinesia, tremor, gait, and myotonia. Our aim is to capture the experiences of patients and clinicians, as well as expand our understanding on the application of wearable technology. In so-doing, we lay the foundation for further research into the use of wearable technology in the management of PD.
Collapse
|
12
|
Waerling RD, Kjaer TW. A systematic review of impairment focussed technology in neurology. Disabil Rehabil Assist Technol 2020; 17:234-247. [DOI: 10.1080/17483107.2020.1776776] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
| | - Troels Wesenberg Kjaer
- University of Copenhagen, Denmark
- Department of Neurology, Zealand University Hospital, Denmark
| |
Collapse
|
13
|
A wearable tool for selective and continuous monitoring of tremor and dyskinesia in Parkinsonian patients. Parkinsonism Relat Disord 2020; 77:43-47. [PMID: 32619969 DOI: 10.1016/j.parkreldis.2020.06.020] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 06/13/2020] [Accepted: 06/22/2020] [Indexed: 11/20/2022]
|
14
|
Ferreira-Sánchez MDR, Moreno-Verdú M, Cano-de-la-Cuerda R. Quantitative Measurement of Rigidity in Parkinson´s Disease: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2020; 20:E880. [PMID: 32041374 PMCID: PMC7038663 DOI: 10.3390/s20030880] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Revised: 01/27/2020] [Accepted: 02/04/2020] [Indexed: 12/16/2022]
Abstract
Rigidity is one of the cardinal symptoms of Parkinson´s disease (PD). Present in up 89% of cases, it is typically assessed with clinical scales. However, these instruments show limitations due to their subjectivity and poor intra- and inter-rater reliability. To compile all of the objective quantitative methods used to assess rigidity in PD and to study their validity and reliability, a systematic review was conducted using the Web of Science, PubMed, and Scopus databases. Studies from January 1975 to June 2019 were included, all of which were written in English. The Strengthening the Reporting of observational studies in Epidemiology Statement (STROBE) checklist for observational studies was used to assess the methodological rigor of the included studies. Thirty-six studies were included. Rigidity was quantitatively assessed in three ways, using servomotors, inertial sensors, and biomechanical and neurophysiological study of muscles. All methods showed good validity and reliability, good correlation with clinical scales, and were useful for detecting rigidity and studying its evolution. People with PD exhibit higher values in terms of objective muscle stiffness than healthy controls. Rigidity depends on the angular velocity and articular amplitude of the mobilization applied. There are objective, valid, and reliable methods that can be used to quantitatively assess rigidity in people with PD.
Collapse
Affiliation(s)
| | - Marcos Moreno-Verdú
- Department of Radiology, Rehabilitation and Physiotherapy, Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain;
- Asociación Parkinson Madrid, 28014 Madrid, Spain
| | - Roberto Cano-de-la-Cuerda
- Department of Physical Therapy, Occupational Therapy, Physical Medicine and Rehabilitation, Universidad Rey Juan Carlos (URJC), Alcorcón, 28922 Madrid, Spain;
| |
Collapse
|
15
|
Morgan C, Rolinski M, McNaney R, Jones B, Rochester L, Maetzler W, Craddock I, Whone AL. Systematic Review Looking at the Use of Technology to Measure Free-Living Symptom and Activity Outcomes in Parkinson's Disease in the Home or a Home-like Environment. JOURNAL OF PARKINSON'S DISEASE 2020; 10:429-454. [PMID: 32250314 PMCID: PMC7242826 DOI: 10.3233/jpd-191781] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/31/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND The emergence of new technologies measuring outcomes in Parkinson's disease (PD) to complement the existing clinical rating scales has introduced the possibility of measurement occurring in patients' own homes whilst they freely live and carry out normal day-to-day activities. OBJECTIVE This systematic review seeks to provide an overview of what technology is being used to test which outcomes in PD from free-living participant activity in the setting of the home environment. Additionally, this review seeks to form an impression of the nature of validation and clinimetric testing carried out on the technological device(s) being used. METHODS Five databases (Medline, Embase, PsycInfo, Cochrane and Web of Science) were systematically searched for papers dating from 2000. Study eligibility criteria included: adults with a PD diagnosis; the use of technology; the setting of a home or home-like environment; outcomes measuring any motor and non-motor aspect relevant to PD, as well as activities of daily living; unrestricted/unscripted activities undertaken by participants. RESULTS 65 studies were selected for data extraction. There were wide varieties of participant sample sizes (<10 up to hundreds) and study durations (<2 weeks up to a year). The metrics evaluated by technology, largely using inertial measurement units in wearable devices, included gait, tremor, physical activity, bradykinesia, dyskinesia and motor fluctuations, posture, falls, typing, sleep and activities of daily living. CONCLUSIONS Home-based free-living testing in PD is being conducted by multiple groups with diverse approaches, focussing mainly on motor symptoms and sleep.
Collapse
Affiliation(s)
- Catherine Morgan
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, Faculty of Engineering, University of Bristol, Bristol, UK
- Movement Disorders Group, Bristol Brain Centre, Southmead Hospital, North Bristol National Health Service Trust, Bristol, UK
| | - Michal Rolinski
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Movement Disorders Group, Bristol Brain Centre, Southmead Hospital, North Bristol National Health Service Trust, Bristol, UK
| | - Roisin McNaney
- School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, Faculty of Engineering, University of Bristol, Bristol, UK
| | - Bennet Jones
- Library and Knowledge Service, Learning and Research, Southmead Hospital, North Bristol National Health Service Trust, Bristol, UK
| | - Lynn Rochester
- Institute of Neuroscience, Newcastle University, Newcastle Upon Tyne, UK
- Newcastle Upon Tyne Hospitals National Health Service Foundation Trust, Newcastle Upon Tyne, UK
| | - Walter Maetzler
- Department of Neurology, Christian-Albrechts University, Kiel, Germany
| | - Ian Craddock
- School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, Faculty of Engineering, University of Bristol, Bristol, UK
| | - Alan L. Whone
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Movement Disorders Group, Bristol Brain Centre, Southmead Hospital, North Bristol National Health Service Trust, Bristol, UK
| |
Collapse
|
16
|
Sparaco M, Lavorgna L, Conforti R, Tedeschi G, Bonavita S. The Role of Wearable Devices in Multiple Sclerosis. Mult Scler Int 2018; 2018:7627643. [PMID: 30405913 PMCID: PMC6199873 DOI: 10.1155/2018/7627643] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 09/16/2018] [Indexed: 12/18/2022] Open
Abstract
Multiple sclerosis (MS) is the most common neurological disorder in young adults. The prevalence of walking impairment in people with MS (pwMS) is estimated between 41% and 75%. To evaluate the walking capacity in pwMS, the patient reported outcomes (PROs) and performance-based tests (i.e., the 2-minute walk test, the 6-minute walk test, the Timed 25-Foot Walk Test, the Timed Up and Go Test, and the Six Spot Step Test) could be used. However, some studies point out that the results of both performance-based tests and objective measures (i.e., by accelerometer) could not reflect patient reports of walking performance and impact of MS on daily life. This review analyses different motion sensors embedded in smartphones and motion wearable device (MWD) that can be useful to measure free-living walking behavior, to evaluate falls, fatigue, sedentary lifestyle, exercise, and quality of sleep in everyday life of pwMS. Caveats and limitations of MWD such as variable accuracy, user adherence, power consumption and recharging, noise susceptibility, and data management are discussed as well.
Collapse
Affiliation(s)
- Maddalena Sparaco
- 1st Clinic of Neurology, University of Campania “Luigi Vanvitelli”, Piazza Miraglia, 2, 80138 Naples, Italy
| | - Luigi Lavorgna
- 1st Clinic of Neurology, University of Campania “Luigi Vanvitelli”, Piazza Miraglia, 2, 80138 Naples, Italy
| | - Renata Conforti
- Neuroradiology Service, Department of Radiology, University of Campania “Luigi Vanvitelli”, C/o CTO Viale dei Colli Aminei 21, Naples, Italy
| | - Gioacchino Tedeschi
- 1st Clinic of Neurology, University of Campania “Luigi Vanvitelli”, Piazza Miraglia, 2, 80138 Naples, Italy
- MRI Research Center SUN-FISM, University of Campania “Luigi Vanvitelli”, Naples, Italy
- Institute for Diagnosis and Care “Hermitage Capodimonte”, Naples, Italy
| | - Simona Bonavita
- 1st Clinic of Neurology, University of Campania “Luigi Vanvitelli”, Piazza Miraglia, 2, 80138 Naples, Italy
- MRI Research Center SUN-FISM, University of Campania “Luigi Vanvitelli”, Naples, Italy
- Institute for Diagnosis and Care “Hermitage Capodimonte”, Naples, Italy
| |
Collapse
|