1
|
Capato TTC, Rodrigues R, Cury RG, Teixeira MJ, Barbosa ER. Clinical assessment of upper limb impairments and functional capacity in Parkinson's disease: a systematic review. ARQUIVOS DE NEURO-PSIQUIATRIA 2023; 81:1008-1015. [PMID: 37899049 PMCID: PMC10689111 DOI: 10.1055/s-0043-1772769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 07/19/2023] [Indexed: 10/31/2023]
Abstract
BACKGROUND Parkinson's disease (PD) may progressively reduce the upper limb's functionality. Currently, there is no standardized upper limb functional capacity assessment in PD in the rehabilitation field. OBJECTIVE To identify specific outcome measurements to assess upper limbs in PD and access functional capacity. METHODS We systematically reviewed and analyzed the literature in English published from August/2012 to August/2022 according to PRISMA. The following keywords were used in our search: "upper limbs" OR "upper extremity" and "Parkinson's disease." Two researchers searched independently, including studies accordingly to our inclusion and exclusion criteria. Registered at PROSPERO CRD42021254486. RESULTS We found 797 studies, and 50 were included in this review (n = 2.239 participants in H&Y stage 1-4). The most common upper limbs outcome measures found in the studies were: (i) UPDRS-III and MDS-UPDRS to assess the severity and progression of PD motor symptoms (tremor, bradykinesia, and rigidity) (ii) Nine Hole Peg Test and Purdue Pegboard Test to assess manual dexterity; (iii) Spiral test and Funnel test to provoke and assess freezing of upper limbs; (iv) Technology assessment such as wearables sensors, apps, and other device were also found. CONCLUSION We found evidence to support upper limb impairments assessments in PD. However, there is still a large shortage of specific tests to assess the functional capacity of the upper limbs. The upper limbs' functional capacity is insufficiently investigated during the clinical and rehabilitation examination due to a lack of specific outcome measures to assess functionality.
Collapse
Affiliation(s)
- Tamine T. C. Capato
- Universidade de São Paulo, Faculdade de Medicina, Departamento de Neurologia, Centro de Distúrbios do Movimento, São Paulo SP, Brazil.
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Nijmegen, The Netherlands.
| | - Rúbia Rodrigues
- Universidade de São Paulo, Faculdade de Medicina, Departamento de Neurologia, Centro de Distúrbios do Movimento, São Paulo SP, Brazil.
| | - Rubens G. Cury
- Universidade de São Paulo, Faculdade de Medicina, Departamento de Neurologia, Centro de Distúrbios do Movimento, São Paulo SP, Brazil.
| | | | - Egberto R. Barbosa
- Universidade de São Paulo, Faculdade de Medicina, Departamento de Neurologia, Centro de Distúrbios do Movimento, São Paulo SP, Brazil.
| |
Collapse
|
2
|
Hayden CD, Murphy BP, Hardiman O, Murray D. Measurement of upper limb function in ALS: a structure review of current methods and future directions. J Neurol 2022; 269:4089-4101. [PMID: 35612658 PMCID: PMC9293830 DOI: 10.1007/s00415-022-11179-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 05/09/2022] [Accepted: 05/11/2022] [Indexed: 11/29/2022]
Abstract
Measurement of upper limb function is critical for tracking clinical severity in amyotrophic lateral sclerosis (ALS). The Amyotrophic Lateral Sclerosis Rating Scale-revised (ALSFRS-r) is the primary outcome measure utilised in clinical trials and research in ALS. This scale is limited by floor and ceiling effects within subscales, such that clinically meaningful changes for subjects are often missed, impacting upon the evaluation of new drugs and treatments. Technology has the potential to provide sensitive, objective outcome measurement. This paper is a structured review of current methods and future trends in the measurement of upper limb function with a particular focus on ALS. Technologies that have the potential to radically change the upper limb measurement field and explore the limitations of current technological sensors and solutions in terms of costs and user suitability are discussed. The field is expanding but there remains an unmet need for simple, sensitive and clinically meaningful tests of upper limb function in ALS along with identifying consensus on the direction technology must take to meet this need.
Collapse
Affiliation(s)
- C D Hayden
- Trinity Centre for Biomedical Engineering, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland. .,Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Dublin 2, Ireland. .,Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse St, Dublin 2, D02 R590, Ireland.
| | - B P Murphy
- Trinity Centre for Biomedical Engineering, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland.,Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Dublin 2, Ireland.,Advanced Materials and Bioengineering Research Centre (AMBER), Trinity College Dublin, Dublin 2, Ireland
| | - O Hardiman
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse St, Dublin 2, D02 R590, Ireland.,Neurocent Directorate, Beaumont Hospital, Beaumont, Dublin 9, Ireland
| | - D Murray
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse St, Dublin 2, D02 R590, Ireland.,Neurocent Directorate, Beaumont Hospital, Beaumont, Dublin 9, Ireland
| |
Collapse
|
3
|
Alfalahi H, Khandoker AH, Chowdhury N, Iakovakis D, Dias SB, Chaudhuri KR, Hadjileontiadis LJ. Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis. Sci Rep 2022; 12:7690. [PMID: 35546606 PMCID: PMC9095860 DOI: 10.1038/s41598-022-11865-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 04/25/2022] [Indexed: 12/12/2022] Open
Abstract
The unmet timely diagnosis requirements, that take place years after substantial neural loss and neuroperturbations in neuropsychiatric disorders, affirm the dire need for biomarkers with proven efficacy. In Parkinson's disease (PD), Mild Cognitive impairment (MCI), Alzheimers disease (AD) and psychiatric disorders, it is difficult to detect early symptoms given their mild nature. We hypothesize that employing fine motor patterns, derived from natural interactions with keyboards, also knwon as keystroke dynamics, could translate classic finger dexterity tests from clinics to populations in-the-wild for timely diagnosis, yet, further evidence is required to prove this efficiency. We have searched PubMED, Medline, IEEEXplore, EBSCO and Web of Science for eligible diagnostic accuracy studies employing keystroke dynamics as an index test for the detection of neuropsychiatric disorders as the main target condition. We evaluated the diagnostic performance of keystroke dynamics across 41 studies published between 2014 and March 2022, comprising 3791 PD patients, 254 MCI patients, and 374 psychiatric disease patients. Of these, 25 studies were included in univariate random-effect meta-analysis models for diagnostic performance assessment. Pooled sensitivity and specificity are 0.86 (95% Confidence Interval (CI) 0.82-0.90, I2 = 79.49%) and 0.83 (CI 0.79-0.87, I2 = 83.45%) for PD, 0.83 (95% CI 0.65-1.00, I2 = 79.10%) and 0.87 (95% CI 0.80-0.93, I2 = 0%) for psychomotor impairment, and 0.85 (95% CI 0.74-0.96, I2 = 50.39%) and 0.82 (95% CI 0.70-0.94, I2 = 87.73%) for MCI and early AD, respectively. Our subgroup analyses conveyed the diagnosis efficiency of keystroke dynamics for naturalistic self-reported data, and the promising performance of multimodal analysis of naturalistic behavioral data and deep learning methods in detecting disease-induced phenotypes. The meta-regression models showed the increase in diagnostic accuracy and fine motor impairment severity index with age and disease duration for PD and MCI. The risk of bias, based on the QUADAS-2 tool, is deemed low to moderate and overall, we rated the quality of evidence to be moderate. We conveyed the feasibility of keystroke dynamics as digital biomarkers for fine motor decline in naturalistic environments. Future work to evaluate their performance for longitudinal disease monitoring and therapeutic implications is yet to be performed. We eventually propose a partnership strategy based on a "co-creation" approach that stems from mechanistic explanations of patients' characteristics derived from data obtained in-clinics and under ecologically valid settings. The protocol of this systematic review and meta-analysis is registered in PROSPERO; identifier CRD42021278707. The presented work is supported by the KU-KAIST joint research center.
Collapse
Affiliation(s)
- Hessa Alfalahi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates.
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates.
| | - Ahsan H Khandoker
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
| | - Nayeefa Chowdhury
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
| | - Dimitrios Iakovakis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| | - Sofia B Dias
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Cruz Quebrada, 1499-002, Lisbon, Portugal
| | - K Ray Chaudhuri
- Parkinson's Foundation Centre of Excellence, King's College Hospital NHS Foundation Trust, Denmark Hill, London, SE5 9RS, United Kingdom
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, United Kingdom
| | - Leontios J Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| |
Collapse
|
4
|
Touchscreen-based finger tapping: Repeatability and configuration effects on tapping performance. PLoS One 2021; 16:e0260783. [PMID: 34874977 PMCID: PMC8651103 DOI: 10.1371/journal.pone.0260783] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 11/16/2021] [Indexed: 11/30/2022] Open
Abstract
Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects almost 2% of the population above the age of 65. To better quantify the effects of new medications, fast and objective methods are needed. Touchscreen-based tapping tasks are simple yet effective tools for quantifying drug effects on PD-related motor symptoms, especially bradykinesia. However, there is no consensus on the optimal task set-up. The present study compares four tapping tasks in 14 healthy participants. In alternate finger tapping (AFT), tapping occurred with the index and middle finger with 2.5 cm between targets, whereas in alternate side tapping (AST) the index finger with 20 cm between targets was used. Both configurations were tested with or without the presence of a visual cue. Moreover, for each tapping task, within- and between-day repeatability and (potential) sensitivity of the calculated parameters were assessed. Visual cueing reduced tapping speed and rhythm, and improved accuracy. This effect was most pronounced for AST. On average, AST had a lower tapping speed with impaired accuracy and improved rhythm compared to AFT. Of all parameters, the total number of taps and mean spatial error had the highest repeatability and sensitivity. The findings suggest against the use of visual cueing because it is crucial that parameters can vary freely to accurately capture medication effects. The choice for AFT or AST depends on the research question, as these tasks assess different aspects of movement. These results encourage further validation of non-cued AFT and AST in PD patients.
Collapse
|
5
|
Sahandi Far M, Eickhoff SB, Goni M, Dukart J. Exploring Test-Retest Reliability and Longitudinal Stability of Digital Biomarkers for Parkinson Disease in the m-Power Data Set: Cohort Study. J Med Internet Res 2021; 23:e26608. [PMID: 34515645 PMCID: PMC8477293 DOI: 10.2196/26608] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 06/21/2021] [Accepted: 07/05/2021] [Indexed: 02/05/2023] Open
Abstract
Background Digital biomarkers (DB), as captured using sensors embedded in modern smart devices, are a promising technology for home-based sign and symptom monitoring in Parkinson disease (PD). Objective Despite extensive application in recent studies, test-retest reliability and longitudinal stability of DB have not been well addressed in this context. We utilized the large-scale m-Power data set to establish the test-retest reliability and longitudinal stability of gait, balance, voice, and tapping tasks in an unsupervised and self-administered daily life setting in patients with PD and healthy controls (HC). Methods Intraclass correlation coefficients were computed to estimate the test-retest reliability of features that also differentiate between patients with PD and healthy volunteers. In addition, we tested for longitudinal stability of DB measures in PD and HC, as well as for their sensitivity to PD medication effects. Results Among the features differing between PD and HC, only a few tapping and voice features had good to excellent test-retest reliabilities and medium to large effect sizes. All other features performed poorly in this respect. Only a few features were sensitive to medication effects. The longitudinal analyses revealed significant alterations over time across a variety of features and in particular for the tapping task. Conclusions These results indicate the need for further development of more standardized, sensitive, and reliable DB for application in self-administered remote studies in patients with PD. Motivational, learning, and other confounders may cause variations in performance that need to be considered in DB longitudinal applications.
Collapse
Affiliation(s)
- Mehran Sahandi Far
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Maria Goni
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| |
Collapse
|
6
|
Ingram LA, Carroll VK, Butler AA, Brodie MA, Gandevia SC, Lord SR. Quantifying upper limb motor impairment in people with Parkinson's disease: a physiological profiling approach. PeerJ 2021; 9:e10735. [PMID: 33604177 PMCID: PMC7869669 DOI: 10.7717/peerj.10735] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 12/17/2020] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Upper limb motor impairments, such as slowness of movement and difficulties executing sequential tasks, are common in people with Parkinson's disease (PD). OBJECTIVE To evaluate the validity of the upper limb Physiological Profile Assessment (PPA) as a standard clinical assessment battery in people with PD, by determining whether the tests, which encompass muscle strength, dexterity, arm stability, position sense, skin sensation and bimanual coordination can (a) distinguish people with PD from healthy controls, (b) detect differences in upper limb test domains between "off" and "on" anti-Parkinson medication states and (c) correlate with a validated measure of upper limb function. METHODS Thirty-four participants with PD and 68 healthy controls completed the upper limb PPA tests within a single session. RESULTS People with PD exhibited impaired performance across most test domains. Based on validity, reliability and feasibility, six tests (handgrip strength, finger-press reaction time, 9-hole peg test, bimanual pole test, arm stability, and shirt buttoning) were identified as key tests for the assessment of upper limb function in people with PD. CONCLUSIONS The upper limb PPA provides a valid, quick and simple means of quantifying specific upper limb impairments in people with PD. These findings indicate clinical assessments should prioritise tests of muscle strength, unilateral movement and dexterity, bimanual coordination, arm stability and functional tasks in people with PD as these domains are the most commonly and significantly impaired.
Collapse
Affiliation(s)
- Lewis A. Ingram
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- University of New South Wales, Sydney, New South Wales, Australia
| | - Vincent K. Carroll
- NSW Health, Mid North Coast Local Health District, Coffs Harbour, New South Wales, Australia
- Parkinson’s NSW, Sydney, New South Wales, Australia
| | - Annie A. Butler
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- University of New South Wales, Sydney, New South Wales, Australia
| | - Matthew A. Brodie
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- University of New South Wales, Sydney, New South Wales, Australia
| | - Simon C. Gandevia
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- University of New South Wales, Sydney, New South Wales, Australia
| | - Stephen R. Lord
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- University of New South Wales, Sydney, New South Wales, Australia
| |
Collapse
|
7
|
Schallert W, Fluet MC, Kesselring J, Kool J. Evaluation of upper limb function with digitizing tablet-based tests: reliability and discriminative validity in healthy persons and patients with neurological disorders. Disabil Rehabil 2020; 44:1465-1473. [PMID: 32757680 DOI: 10.1080/09638288.2020.1800838] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
PURPOSE To evaluate discriminative validity, relative reliability and absolute reliability of four tablet-based tests for the evaluation of upper limb motor function in healthy persons and patients with neurological disorders. METHODS Cross-sectional study in 54 participants: 29 patients with upper limb movement impairment due to a neurological condition recruited from an inpatient rehabilitation centre and 25 healthy persons. Accuracy, speed and path length were analysed for four tablet-based tests: "Spiral drawings," "Tapping," "Follow the dot" and "Trace a star." The area under the receiver operating characteristic curve (AUC) was used to evaluate discriminative validity. Relative reliability was analysed with the intra-class correlation coefficient (ICC), and absolute reliability by limits of agreement (LoA) and minimal detectable difference (MDD). RESULTS All four tests showed excellent discriminative validity for the parameter accuracy (AUC 0.93-0.98). Tapping was the best test for discriminating patients from healthy persons. Test-retest reliability was good for accuracy in all tests (ICC = 0.76-0.88), but poor to moderate for speed and path length (ICC = 0.20-0.69). The MDD varied between 14% and 38%. Performance on the four tablet-based tests was stable between sessions, indicating that there was no learning effect. CONCLUSION The parameter accuracy showed excellent discriminative validity and reliability in all four tablet-based tests. Discriminative validity was excellent for all three parameters in the Tapping test. In the other tasks speed showed good to poor reliability, while the reliability of path-length was poor in all tasks. Results were comparable for the dominant and non-dominant hand. Tablet-based tests have the advantage that patients can use them for self-monitoring of upper limb motor function.Implications for rehabilitationFour tablet-based tests for the assessment of upper limb motor function in patients with upper limb neurological dysfunction were evaluated: "Spiral drawings", "Tapping", "Follow the dot" and "Trace a star". The parameter accuracy in these four tests had excellent discriminative validity and good reliability.Patients can perform the tests independently at home for self-monitoring of progress. This may increase patients' motivation to exercise at home.The results can be sent to physicians, enabling the earlier detection of deterioration, which may require medical attention.
Collapse
Affiliation(s)
- Wolfgang Schallert
- Department of Rehabilitation Research, Rehabilitation Centre Valens, Valens, Switzerland.,Department of Physiotherapy, Berner Fachhochschule, Bern, Switzerland
| | - Marie-Christine Fluet
- Swiss Federal Institute of Technology Zurich, Zurich, Switzerland.,ReHaptix GmbH, Rehabilitation Products, Zurich, Switzerland
| | - Juerg Kesselring
- Department of Rehabilitation Research, Rehabilitation Centre Valens, Valens, Switzerland
| | - Jan Kool
- Department of Rehabilitation Research, Rehabilitation Centre Valens, Valens, Switzerland
| |
Collapse
|
8
|
Abujrida H, Agu E, Pahlavan K. Machine learning-based motor assessment of Parkinson's disease using postural sway, gait and lifestyle features on crowdsourced smartphone data. Biomed Phys Eng Express 2020; 6:035005. [PMID: 33438650 DOI: 10.1088/2057-1976/ab39a8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVES Remote assessment of gait in patients' homes has become a valuable tool for monitoring the progression of Parkinson's disease (PD). However, these measurements are often not as accurate or reliable as clinical evaluations because it is challenging to objectively distinguish the unique gait characteristics of PD. We explore the inference of patients' stage of PD from their gait using machine learning analyses of data gathered from their smartphone sensors. Specifically, we investigate supervised machine learning (ML) models to classify the severity of the motor part of the UPDRS (MDS-UPDRS 2.10-2.13). Our goals are to facilitate remote monitoring of PD patients and to answer the following questions: (1) What is the patient PD stage based on their gait? (2) Which features are best for understanding and classifying PD gait severities? (3) Which ML classifier types best discriminate PD patients from healthy controls (HC)? and (4) Which ML classifier types can discriminate the severity of PD gait anomalies? METHODOLOGY Our work uses smartphone sensor data gathered from 9520 patients in the mPower study, of whom 3101 participants uploaded gait recordings and 344 subjects and 471 controls uploaded at least 3 walking activities. We selected 152 PD patients who performed at least 3 recordings before and 3 recordings after taking medications and 304 HC who performed at least 3 walking recordings. From the accelerometer and gyroscope sensor data, we extracted statistical, time, wavelet and frequency domain features, and other lifestyle features were derived directly from participants' survey data. We conducted supervised classification experiments using 10-fold cross-validation and measured the model precision, accuracy, and area under the curve (AUC). RESULTS The best classification model, best feature, highest classification accuracy, and AUC were (1) random forest and entropy rate, 93% and 0.97, respectively, for walking balance (MDS-UPDRS-2.12); (2) bagged trees and MinMaxDiff, 95% and 0.92, respectively, for shaking/tremor (MDS-UPDRS-2.10); (3) bagged trees and entropy rate, 98% and 0.98, respectively, for freeze of gait; and (4) random forest and MinMaxDiff, 95% and 0.99, respectively, for distinguishing PD patients from HC. CONCLUSION Machine learning classification was challenging due to the use of data that were subjectively labeled based on patients' answers to the MDS-UPDRS survey questions. However, with use of a significantly larger number of subjects than in prior work and clinically validated gait features, we were able to demonstrate that automatic patient classification based on smartphone sensor data can be used to objectively infer the severity of PD and the extent of specific gait anomalies.
Collapse
Affiliation(s)
- Hamza Abujrida
- Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA 01609, United States of America
| | | | | |
Collapse
|
9
|
Prince J, de Vos M. A Deep Learning Framework for the Remote Detection of Parkinson'S Disease Using Smart-Phone Sensor Data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:3144-3147. [PMID: 30441061 DOI: 10.1109/embc.2018.8512972] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The assessment of Parkinson's disease (PD) using wearable sensors in non-clinical environments presents an opportunity for objective disease classification and severity prediction on a high-frequency and longitudinal basis. However, many challenges exist in analysing remotely collected data due to many sources of data corruption. Using a cohort of 1,815 participants (866 controls and 949 with PD) we implement a range of classification algorithms on Alternate Finger Tapping test data collected on smart-phones in remote environments. We compare the disease classification ability of two traditional machine learning methods against two state-of-the-art deep learning approaches, determining if the latter is successful without the definition of an explicit feature set. We find the deep learning approaches capable of disease classification, often outperforming traditional methods. We show similarities between the participants successfully classified through use of a manually extracted feature set, and the features learnt by a convolutional neural network. Finally, we discuss the broader challenges of analysing remotely collected datasets whilst highlighting the suitability of deep learning for assessing PD when large participant numbers are available.
Collapse
|
10
|
Prince J, Andreotti F, De Vos M. Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-Wise Missing Data. IEEE Trans Biomed Eng 2018; 66:1402-1411. [PMID: 30403615 PMCID: PMC6487914 DOI: 10.1109/tbme.2018.2873252] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
As the collection of mobile health data becomes pervasive, missing data can make large portions of datasets inaccessible for analysis. Missing data has shown particularly problematic for remotely diagnosing and monitoring Parkinson's disease (PD) using smartphones. This contribution presents multi-source ensemble learning, a methodology which combines dataset deconstruction with ensemble learning and enables participants with incomplete data (i.e., where not all sensor data is available) to be included in the training of machine learning models and achieves a 100% participant retention rate. We demonstrate the proposed method on a cohort of 1513 participants, 91.2% of which contributed incomplete data in tapping, gait, voice, and/or memory tests. The use of multi-source ensemble learning, alongside convolutional neural networks (CNNs) capitalizing on the amount of available data, increases PD classification accuracy from 73.1% to 82.0% as compared to traditional techniques. The increase in accuracy is found to be partly caused by the use of multi-channel CNNs and partly caused by developing models using the large cohort of participants. Furthermore, through bootstrap sampling we reveal that feature selection is better performed on a large cohort of participants with incomplete data than on a small number of participants with complete data. The proposed method is applicable to a wide range of wearable/remote monitoring datasets that suffer from missing data and contributes to improving the ability to remotely monitor PD via revealing novel methods of accounting for symptom heterogeneity.
Collapse
|
11
|
Thomas I, Westin J, Alam M, Bergquist F, Nyholm D, Senek M, Memedi M. A Treatment-Response Index From Wearable Sensors for Quantifying Parkinson's Disease Motor States. IEEE J Biomed Health Inform 2018; 22:1341-1349. [DOI: 10.1109/jbhi.2017.2777926] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
12
|
Prince J, Arora S, de Vos M. Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes. Physiol Meas 2018. [PMID: 29516871 DOI: 10.1088/1361-6579/aab512] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE To better understand the longitudinal characteristics of Parkinson's disease (PD) through the analysis of finger tapping and memory tests collected remotely using smartphones. APPROACH Using a large cohort (312 PD subjects and 236 controls) of participants in the mPower study, we extract clinically validated features from a finger tapping and memory test to monitor the longitudinal behaviour of study participants. We investigate any discrepancy in learning rates associated with motor and non-motor tasks between PD subjects and healthy controls. The ability of these features to predict self-assigned severity measures is assessed whilst simultaneously inspecting the severity scoring system for floor-ceiling effects. Finally, we study the relationship between motor and non-motor longitudinal behaviour to determine if separate aspects of the disease are dependent on one another. MAIN RESULTS We find that the test performances of the most severe subjects show significant correlations with self-assigned severity measures. Interestingly, less severe subjects do not show significant correlations, which is shown to be a consequence of floor-ceiling effects within the mPower self-reporting severity system. We find that motor performance after practise is a better predictor of severity than baseline performance suggesting that starting performance at a new motor task is less representative of disease severity than the performance after the test has been learnt. We find PD subjects show significant impairments in motor ability as assessed through the alternating finger tapping (AFT) test in both the short- and long-term analyses. In the AFT and memory tests we demonstrate that PD subjects show a larger degree of longitudinal performance variability in addition to requiring more instances of a test to reach a steady state performance than healthy subjects. SIGNIFICANCE Our findings pave the way forward for objective assessment and quantification of longitudinal learning rates in PD. This can be particularly useful for symptom monitoring and assessing medication response. This study tries to tackle some of the major challenges associated with self-assessed severity labels by designing and validating features extracted from big datasets in PD, which could help identify digital biomarkers capable of providing measures of disease severity outside of a clinical environment.
Collapse
Affiliation(s)
- John Prince
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | | | | |
Collapse
|
13
|
Hasan H, Athauda DS, Foltynie T, Noyce AJ. Technologies Assessing Limb Bradykinesia in Parkinson's Disease. JOURNAL OF PARKINSONS DISEASE 2017; 7:65-77. [PMID: 28222539 PMCID: PMC5302048 DOI: 10.3233/jpd-160878] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Background: The MDS-UPDRS (Movement Disorders Society – Unified Parkinson’s Disease Rating Scale) is the most widely used scale for rating impairment in PD. Subscores measuring bradykinesia have low reliability that can be subject to rater variability. Novel technological tools can be used to overcome such issues. Objective: To systematically explore and describe the available technologies for measuring limb bradykinesia in PD that were published between 2006 and 2016. Methods: A systematic literature search using PubMed (MEDLINE), IEEE Xplore, Web of Science, Scopus and Engineering Village (Compendex and Inspec) databases was performed to identify relevant technologies published until 18 October 2016. Results: 47 technologies assessing bradykinesia in PD were identified, 17 of which offered home and clinic-based assessment whilst 30 provided clinic-based assessment only. Of the eligible studies, 7 were validated in a PD patient population only, whilst 40 were tested in both PD and healthy control groups. 19 of the 47 technologies assessed bradykinesia only, whereas 28 assessed other parkinsonian features as well. 33 technologies have been described in additional PD-related studies, whereas 14 are not known to have been tested beyond the pilot phase. Conclusion: Technology based tools offer advantages including objective motor assessment and home monitoring of symptoms, and can be used to assess response to intervention in clinical trials or routine care. This review provides an up-to-date repository and synthesis of the current literature regarding technology used for assessing limb bradykinesia in PD. The review also discusses the current trends with regards to technology and discusses future directions in development.
Collapse
Affiliation(s)
- Hasan Hasan
- UCL Institute of Neurology, Queen Square, London, UK
| | - Dilan S Athauda
- UCL Institute of Neurology, Queen Square, London, UK.,Sobell Department of Motor Neuroscience and Movement Disorders, The National Hospital for Neurology and Neurosurgery, London, UK
| | - Thomas Foltynie
- UCL Institute of Neurology, Queen Square, London, UK.,Sobell Department of Motor Neuroscience and Movement Disorders, The National Hospital for Neurology and Neurosurgery, London, UK
| | - Alastair J Noyce
- UCL Institute of Neurology, Queen Square, London, UK.,Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University London, London, UK.,Reta Lila Weston Institute of Neurological studies, UCL Institute of Neurology, London, UK
| |
Collapse
|
14
|
Jeon H, Lee W, Park H, Lee HJ, Kim SK, Kim HB, Jeon B, Park KS. High-accuracy automatic classification of Parkinsonian tremor severity using machine learning method. Physiol Meas 2017; 38:1980-1999. [DOI: 10.1088/1361-6579/aa8e1f] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
|
15
|
Verification of a Method for Measuring Parkinson's Disease Related Temporal Irregularity in Spiral Drawings. SENSORS 2017; 17:s17102341. [PMID: 29027941 PMCID: PMC5677449 DOI: 10.3390/s17102341] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Revised: 10/05/2017] [Accepted: 10/12/2017] [Indexed: 11/27/2022]
Abstract
Parkinson’s disease (PD) is a progressive movement disorder caused by the death of dopamine-producing cells in the midbrain. There is a need for frequent symptom assessment, since the treatment needs to be individualized as the disease progresses. The aim of this paper was to verify and further investigate the clinimetric properties of an entropy-based method for measuring PD-related upper limb temporal irregularities during spiral drawing tasks. More specifically, properties of a temporal irregularity score (TIS) for patients at different stages of PD, and medication time points were investigated. Nineteen PD patients and 22 healthy controls performed repeated spiral drawing tasks on a smartphone. Patients performed the tests before a single levodopa dose and at specific time intervals after the dose was given. Three movement disorder specialists rated videos of the patients based on the unified PD rating scale (UPDRS) and the Dyskinesia scale. Differences in mean TIS between the groups of patients and healthy subjects were assessed. Test-retest reliability of the TIS was measured. The ability of TIS to detect changes from baseline (before medication) to later time points was investigated. Correlations between TIS and clinical rating scores were assessed. The mean TIS was significantly different between healthy subjects and patients in advanced groups (p-value = 0.02). Test-retest reliability of TIS was good with Intra-class Correlation Coefficient of 0.81. When assessing changes in relation to treatment, TIS contained some information to capture changes from Off to On and wearing off effects. However, the correlations between TIS and clinical scores (UPDRS and Dyskinesia) were weak. TIS was able to differentiate spiral drawings drawn by patients in an advanced stage from those drawn by healthy subjects, and TIS had good test-retest reliability. TIS was somewhat responsive to single-dose levodopa treatment. Since TIS is an upper limb high-frequency-based measure, it cannot be detected during clinical assessment.
Collapse
|
16
|
Bank PJM, Marinus J, Meskers CGM, de Groot JH, van Hilten JJ. Optical Hand Tracking: A Novel Technique for the Assessment of Bradykinesia in Parkinson's Disease. Mov Disord Clin Pract 2017; 4:875-883. [PMID: 30363453 DOI: 10.1002/mdc3.12536] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Revised: 07/19/2017] [Accepted: 08/08/2017] [Indexed: 01/16/2023] Open
Abstract
Background Evaluation of therapies for Parkinson's disease (PD) may benefit from objective quantification of the separate movement components of bradykinesia (i.e., velocity, amplitude, and rhythm). This study evaluated the sensitivity and reliability of parameters derived from recently available optical hand tracking techniques for patient-friendly, automated quantification of bradykinesia of the upper extremity in PD. Methods Fifty-seven patients with PD and 57 healthy individuals (controls) performed repetitive finger tapping (RFT), alternating hand movements (AHM), and alternating forearm movements (AFM). Movement components of bradykinesia (i.e., velocity, frequency, amplitude, hesitations, and halts) were quantified using optical hand tracking. Reliability was quantified using intraclass correlation coefficients in a subgroup of 12 patients with PD and 12 controls (test-retest) and in all 57 controls (intra-trial). Results RFT and AHM were successfully recorded in 94% of all participants. Movement components differed between patients with PD and controls and were correlated with clinical ratings. Velocity and halt duration appeared to be most useful (i.e., the largest difference between the PD and control groups, good reliability) for the quantification of RFT, whereas frequency appeared to be most useful for the quantification of AHM. Other variables, such as frequency and amplitude of RFT, showed poor test-retest reliability, because they were susceptible to changes in movement strategy. AFM was excluded from the analysis because of problems with hand recognition. Conclusion Novel optical hand tracking techniques yield promising results for patient-friendly quantification of bradykinesia of the upper extremity in PD. Future work should aim to optimize optical hand tracking and reduce susceptibility to changes in strategy.
Collapse
Affiliation(s)
- Paulina J M Bank
- Department of Neurology Leiden University Medical Center Leiden the Netherlands
| | - Johan Marinus
- Department of Neurology Leiden University Medical Center Leiden the Netherlands
| | - Carel G M Meskers
- Department of Rehabilitation Medicine VU University Medical Center Amsterdam the Netherlands.,MOVE Research Institute Amsterdam the Netherlands
| | - Jurriaan H de Groot
- Department of Rehabilitation Medicine Leiden University Medical Center Leiden the Netherlands
| | | |
Collapse
|
17
|
Jeon H, Lee W, Park H, Lee HJ, Kim SK, Kim HB, Jeon B, Park KS. Automatic Classification of Tremor Severity in Parkinson's Disease Using a Wearable Device. SENSORS 2017; 17:s17092067. [PMID: 28891942 PMCID: PMC5621347 DOI: 10.3390/s17092067] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Revised: 09/06/2017] [Accepted: 09/06/2017] [Indexed: 11/25/2022]
Abstract
Although there is clinical demand for new technology that can accurately measure Parkinsonian tremors, automatic scoring of Parkinsonian tremors using machine-learning approaches has not yet been employed. This study aims to fill this gap by proposing machine-learning algorithms as a way to predict the Unified Parkinson’s Disease Rating Scale (UPDRS), which are similar to how neurologists rate scores in actual clinical practice. In this study, the tremor signals of 85 patients with Parkinson’s disease (PD) were measured using a wrist-watch-type wearable device consisting of an accelerometer and a gyroscope. The displacement and angle signals were calculated from the measured acceleration and angular velocity, and the acceleration, angular velocity, displacement, and angle signals were used for analysis. Nineteen features were extracted from each signal, and the pairwise correlation strategy was used to reduce the number of feature dimensions. With the selected features, a decision tree (DT), support vector machine (SVM), discriminant analysis (DA), random forest (RF), and k-nearest-neighbor (kNN) algorithm were explored for automatic scoring of the Parkinsonian tremor severity. The performance of the employed classifiers was analyzed using accuracy, recall, and precision, and compared to other findings in similar studies. Finally, the limitations and plans for further study are discussed.
Collapse
Affiliation(s)
- Hyoseon Jeon
- The Interdisciplinary Program for Bioengineering, Seoul National University, Seoul 03080, Korea.
| | - Woongwoo Lee
- Department of Neurology and Movement Disorder Center, Seoul National University Hospital, Seoul 03080, Korea.
| | - Hyeyoung Park
- Department of Neurology and Movement Disorder Center, Seoul National University Hospital, Seoul 03080, Korea.
| | - Hong Ji Lee
- The Interdisciplinary Program for Bioengineering, Seoul National University, Seoul 03080, Korea.
| | - Sang Kyong Kim
- The Interdisciplinary Program for Bioengineering, Seoul National University, Seoul 03080, Korea.
| | - Han Byul Kim
- The Interdisciplinary Program for Bioengineering, Seoul National University, Seoul 03080, Korea.
| | - Beomseok Jeon
- Department of Neurology and Movement Disorder Center, Seoul National University Hospital, Seoul 03080, Korea.
| | - Kwang Suk Park
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul 03080, Korea.
| |
Collapse
|
18
|
Abstract
This review will illustrate the process of moving from an idea through preclinical research and Galenic developments into clinical investigations and finally to approval by regulatory agencies within the European Union. The two new treatment strategies described, levodopa/carbidopa intestinal gel and levodopa/carbidopa microtablets, for advanced Parkinson's disease, have been developed in collaborative research within departments at Uppsala University. With this historical approach, reference priority is given to reports considered to be of special importance for this more than two decades long process 'from bedside to bench to bedside'.
Collapse
Affiliation(s)
| | - Dag Nyholm
- CONTACT Dag Nyholm Department of Neuroscience, Neurology, Uppsala University, Uppsala University Hospital, SE-75185 Uppsala, Sweden
| |
Collapse
|
19
|
Heldman DA, Harris DA, Felong T, Andrzejewski KL, Dorsey ER, Giuffrida JP, Goldberg B, Burack MA. Telehealth Management of Parkinson's Disease Using Wearable Sensors: An Exploratory Study. Digit Biomark 2017; 1:43-51. [PMID: 29725667 DOI: 10.1159/000475801] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Background Parkinson's disease (PD) motor symptoms can fluctuate and may not be accurately reflected during a clinical evaluation. In addition, access to movement disorder specialists is limited for many with PD. The objective was to assess the impact of motion sensor-based telehealth diagnostics on PD clinical care and management. Methods Eighteen adults with PD were randomized to control or experimental groups. All participants were instructed to use a motion sensor-based monitoring system at home one day per week, for seven months. The system included a finger-worn motion sensor and tablet-based software interface that guided patients through tasks to quantify tremor, bradykinesia, and dyskinesia. Data were processed into motor symptom severity reports, which were reviewed by a movement disorders neurologist for experimental group participants. After three months and six months, control group participants visited the clinic for a routine appointment, while experimental group participants had a videoconference or phone call instead. Results Home based assessments were completed with median compliance of 95.7%. For a subset of participants, the neurologist successfully used information in the reports such as quantified response to treatment or progression over time to make therapy adjustments. Changes in clinical characteristics from study start to end were not significantly different between groups. Discussion Individuals with PD were able and willing to use remote monitoring technology. Patient management aided by telehealth diagnostics provided comparable outcomes to standard care. Telehealth technologies combined with wearable sensors have the potential to improve care for disparate PD populations or those unable to travel.
Collapse
Affiliation(s)
| | - Denzil A Harris
- School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, NY, USA.,Center for Human Experimental Therapeutics, University of Rochester Medical Center, Rochester, NY, USA
| | - Timothy Felong
- Center for Human Experimental Therapeutics, University of Rochester Medical Center, Rochester, NY, USA
| | - Kelly L Andrzejewski
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.,Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - E Ray Dorsey
- Center for Human Experimental Therapeutics, University of Rochester Medical Center, Rochester, NY, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | | | | | - Michelle A Burack
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| |
Collapse
|
20
|
Pulliam CL, Heldman DA, Brokaw EB, Mera TO, Mari ZK, Burack MA. Continuous Assessment of Levodopa Response in Parkinson's Disease Using Wearable Motion Sensors. IEEE Trans Biomed Eng 2017; 65:159-164. [PMID: 28459677 DOI: 10.1109/tbme.2017.2697764] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Fluctuations in response to levodopa in Parkinson's disease (PD) are difficult to treat as tools to monitor temporal patterns of symptoms are hampered by several challenges. The objective was to use wearable sensors to quantify the dose response of tremor, bradykinesia, and dyskinesia in individuals with PD. METHODS Thirteen individuals with PD and fluctuating motor benefit were instrumented with wrist and ankle motion sensors and recorded by video. Kinematic data were recorded as subjects completed a series of activities in a simulated home environment through transition from off to on medication. Subjects were evaluated using the unified Parkinson disease rating scale motor exam (UPDRS-III) at the start and end of data collection. Algorithms were applied to the kinematic data to score tremor, bradykinesia, and dyskinesia. A blinded clinician rated severity observed on video. Accuracy of algorithms was evaluated by comparing scores with clinician ratings using a receiver operating characteristic (ROC) analysis. RESULTS Algorithm scores for tremor, bradykinesia, and dyskinesia agreed with clinician ratings of video recordings (ROC area > 0.8). Summary metrics extracted from time intervals before and after taking medication provided quantitative measures of therapeutic response (p < 0.01). Radar charts provided intuitive visualization, with graphical features correlated with UPDRS-III scores (R = 0.81). CONCLUSION A system with wrist and ankle motion sensors can provide accurate measures of tremor, bradykinesia, and dyskinesia as patients complete routine activities. SIGNIFICANCE This technology could provide insight on motor fluctuations in the context of daily life to guide clinical management and aid in development of new therapies.
Collapse
|
21
|
Quantification of Finger-Tapping Angle Based on Wearable Sensors. SENSORS 2017; 17:s17020203. [PMID: 28125051 PMCID: PMC5336005 DOI: 10.3390/s17020203] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2016] [Revised: 01/15/2017] [Accepted: 01/16/2017] [Indexed: 11/17/2022]
Abstract
We propose a novel simple method for quantitative and qualitative finger-tapping assessment based on miniature inertial sensors (3D gyroscopes) placed on the thumb and index-finger. We propose a simplified description of the finger tapping by using a single angle, describing rotation around a dominant axis. The method was verified on twelve subjects, who performed various tapping tasks, mimicking impaired patterns. The obtained tapping angles were compared with results of a motion capture camera system, demonstrating excellent accuracy. The root-mean-square (RMS) error between the two sets of data is, on average, below 4°, and the intraclass correlation coefficient is, on average, greater than 0.972. Data obtained by the proposed method may be used together with scores from clinical tests to enable a better diagnostic. Along with hardware simplicity, this makes the proposed method a promising candidate for use in clinical practice. Furthermore, our definition of the tapping angle can be applied to all tapping assessment systems.
Collapse
|
22
|
Aghanavesi S, Nyholm D, Senek M, Bergquist F, Memedi M. A smartphone-based system to quantify dexterity in Parkinson's disease patients. INFORMATICS IN MEDICINE UNLOCKED 2017. [DOI: 10.1016/j.imu.2017.05.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
|
23
|
Wu CK, Tsang KF, Chi HR, Hung FH. A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram. SENSORS 2016; 16:s16050659. [PMID: 27171090 PMCID: PMC4883350 DOI: 10.3390/s16050659] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Revised: 04/20/2016] [Accepted: 04/27/2016] [Indexed: 11/24/2022]
Abstract
Globally, 1.2 million people die and 50 million people are injured annually due to traffic accidents. These traffic accidents cost $500 billion dollars. Drunk drivers are found in 40% of the traffic crashes. Existing drunk driving detection (DDD) systems do not provide accurate detection and pre-warning concurrently. Electrocardiogram (ECG) is a proven biosignal that accurately and simultaneously reflects human’s biological status. In this letter, a classifier for DDD based on ECG is investigated in an attempt to reduce traffic accidents caused by drunk drivers. At this point, it appears that there is no known research or literature found on ECG classifier for DDD. To identify drunk syndromes, the ECG signals from drunk drivers are studied and analyzed. As such, a precise ECG-based DDD (ECG-DDD) using a weighted kernel is developed. From the measurements, 10 key features of ECG signals were identified. To incorporate the important features, the feature vectors are weighted in the customization of kernel functions. Four commonly adopted kernel functions are studied. Results reveal that weighted feature vectors improve the accuracy by 11% compared to the computation using the prime kernel. Evaluation shows that ECG-DDD improved the accuracy by 8% to 18% compared to prevailing methods.
Collapse
Affiliation(s)
- Chung Kit Wu
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.
| | - Kim Fung Tsang
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.
| | - Hao Ran Chi
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.
| | - Faan Hei Hung
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.
| |
Collapse
|
24
|
Yang K, Xiong WX, Liu FT, Sun YM, Luo S, Ding ZT, Wu JJ, Wang J. Objective and quantitative assessment of motor function in Parkinson's disease-from the perspective of practical applications. ANNALS OF TRANSLATIONAL MEDICINE 2016; 4:90. [PMID: 27047949 DOI: 10.21037/atm.2016.03.09] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Parkinson's disease (PD) is a common neurodegenerative disorder with high morbidity because of the coming aged society. Currently, disease management and the development of new treatment strategies mainly depend on the clinical information derived from rating scales and patients' diaries, which have various limitations with regard to validity, inter-rater variability and continuous monitoring. Recently the prevalence of mobile medical equipment has made it possible to develop an objective, accurate, remote monitoring system for motor function assessment, playing an important role in disease diagnosis, home-monitoring, and severity evaluation. This review discusses the recent development in sensor technology, which may be a promising replacement of the current rating scales in the assessment of motor function of PD.
Collapse
Affiliation(s)
- Ke Yang
- Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Wei-Xi Xiong
- Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Feng-Tao Liu
- Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yi-Min Sun
- Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Susan Luo
- Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Zheng-Tong Ding
- Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jian-Jun Wu
- Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jian Wang
- Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| |
Collapse
|
25
|
Sano Y, Kandori A, Shima K, Yamaguchi Y, Tsuji T, Noda M, Higashikawa F, Yokoe M, Sakoda S. Quantifying Parkinson's disease finger-tapping severity by extracting and synthesizing finger motion properties. Med Biol Eng Comput 2016; 54:953-65. [PMID: 27032933 DOI: 10.1007/s11517-016-1467-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2013] [Accepted: 02/15/2016] [Indexed: 10/22/2022]
Abstract
We propose a novel index of Parkinson's disease (PD) finger-tapping severity, called "PDFTsi," for quantifying the severity of symptoms related to the finger tapping of PD patients with high accuracy. To validate the efficacy of PDFTsi, the finger-tapping movements of normal controls and PD patients were measured by using magnetic sensors, and 21 characteristics were extracted from the finger-tapping waveforms. To distinguish motor deterioration due to PD from that due to aging, the aging effect on finger tapping was removed from these characteristics. Principal component analysis (PCA) was applied to the age-normalized characteristics, and principal components that represented the motion properties of finger tapping were calculated. Multiple linear regression (MLR) with stepwise variable selection was applied to the principal components, and PDFTsi was calculated. The calculated PDFTsi indicates that PDFTsi has a high estimation ability, namely a mean square error of 0.45. The estimation ability of PDFTsi is higher than that of the alternative method, MLR with stepwise regression selection without PCA, namely a mean square error of 1.30. This result suggests that PDFTsi can quantify PD finger-tapping severity accurately. Furthermore, the result of interpreting a model for calculating PDFTsi indicated that motion wideness and rhythm disorder are important for estimating PD finger-tapping severity.
Collapse
Affiliation(s)
- Yuko Sano
- Research & Development Group, Center for Technology Innovation - Healthcare, Hitachi Ltd., 1-280 Higashi-Koigakubo, Kokubunji, Tokyo, Japan.
| | - Akihiko Kandori
- Research & Development Group, Center for Technology Innovation - Healthcare, Hitachi Ltd., 1-280 Higashi-Koigakubo, Kokubunji, Tokyo, Japan
| | - Keisuke Shima
- Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, Japan
| | - Yuki Yamaguchi
- Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, Japan
| | - Toshio Tsuji
- Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, Japan
| | - Masafumi Noda
- Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Fumiko Higashikawa
- Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Masaru Yokoe
- Graduate School of Medicine, Osaka University, 1-7 Yamadaoka, Suita, Osaka, Japan
| | - Saburo Sakoda
- National Hospital Organization, Toneyama Hospital, 5-1-1 Toneyama, Toyonaka, Osaka, Japan
| |
Collapse
|
26
|
Carter A, Liddle J, Hall W, Chenery H. Mobile Phones in Research and Treatment: Ethical Guidelines and Future Directions. JMIR Mhealth Uhealth 2015; 3:e95. [PMID: 26474545 PMCID: PMC4704925 DOI: 10.2196/mhealth.4538] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Revised: 07/20/2015] [Accepted: 08/05/2015] [Indexed: 11/13/2022] Open
Abstract
Mobile phones and other remote monitoring devices, collectively referred to as "mHealth," promise to transform the treatment of a range of conditions, including movement disorders, such as Parkinson’s disease. In this viewpoint paper, we use Parkinson’s disease as an example, although most considerations discussed below are valid for a wide variety of conditions. The ability to easily collect vast arrays of personal data over long periods will give clinicians and researchers unique insights into disease treatment and progression. These capabilities also pose new ethical challenges that health care professionals will need to manage if this promise is to be realized with minimal risk of harm. These challenges include privacy protection when anonymity is not always possible, minimization of third-party uses of mHealth data, informing patients of complex risks when obtaining consent, managing data in ways that maximize benefit while minimizing the potential for disclosure to third parties, careful communication of clinically relevant information gleaned via mHealth technologies, and rigorous evaluation and regulation of mHealth products before widespread use. Given the complex array of symptoms and differences in comfort and literacy with technology, it is likely that these solutions will need to be individualized. It is therefore critical that developers of mHealth apps engage with patients throughout the development process to ensure that the technology meets their needs. These challenges will be best met through early and ongoing engagement with patients and other relevant stakeholders.
Collapse
Affiliation(s)
- Adrian Carter
- School of Psychological Sciences, Monash University, Melbourne, Australia.
| | | | | | | |
Collapse
|
27
|
Automatic Spiral Analysis for Objective Assessment of Motor Symptoms in Parkinson's Disease. SENSORS 2015; 15:23727-44. [PMID: 26393595 PMCID: PMC4610483 DOI: 10.3390/s150923727] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Revised: 09/03/2015] [Accepted: 09/09/2015] [Indexed: 12/03/2022]
Abstract
A challenge for the clinical management of advanced Parkinson’s disease (PD) patients is the emergence of fluctuations in motor performance, which represents a significant source of disability during activities of daily living of the patients. There is a lack of objective measurement of treatment effects for in-clinic and at-home use that can provide an overview of the treatment response. The objective of this paper was to develop a method for objective quantification of advanced PD motor symptoms related to off episodes and peak dose dyskinesia, using spiral data gathered by a touch screen telemetry device. More specifically, the aim was to objectively characterize motor symptoms (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Digitized upper limb movement data of 65 advanced PD patients and 10 healthy (HE) subjects were recorded as they performed spiral drawing tasks on a touch screen device in their home environment settings. Several spatiotemporal features were extracted from the time series and used as inputs to machine learning methods. The methods were validated against ratings on animated spirals scored by four movement disorder specialists who visually assessed a set of kinematic features and the motor symptom. The ability of the method to discriminate between PD patients and HE subjects and the test-retest reliability of the computed scores were also evaluated. Computed scores correlated well with mean visual ratings of individual kinematic features. The best performing classifier (Multilayer Perceptron) classified the motor symptom (bradykinesia or dyskinesia) with an accuracy of 84% and area under the receiver operating characteristics curve of 0.86 in relation to visual classifications of the raters. In addition, the method provided high discriminating power when distinguishing between PD patients and HE subjects as well as had good test-retest reliability. This study demonstrated the potential of using digital spiral analysis for objective quantification of PD-specific and/or treatment-induced motor symptoms.
Collapse
|