du.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A smartphone-based system to quantify dexterity in Parkinson's disease patients
Dalarna University, School of Technology and Business Studies, Microdata Analysis.ORCID iD: 0000-0002-1548-5077
Show others and affiliations
2017 (English)In: Informatics in Medicine Unlocked, ISSN 2352-9148, Vol. 9, p. 11-17Article in journal (Refereed) Published
Abstract [en]

Objectives: The aim of this paper is to investigate whether a smartphone-based system can be used to quantify dexterity in Parkinson’s disease (PD). More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD. Methods: Nineteen advanced PD patients and 22 healthy controls participated in a clinical trial in Uppsala, Sweden. The subjects were asked to perform tapping and spiral drawing tests using a smartphone. Patients performed the tests before, and at pre-specified time points after they received 150% of their usual levodopa morning dose. Patients were video recorded and their motor symptoms were assessed by three movement disorder specialists using three Unified PD Rating Scale (UPDRS) motor items from part III, the dyskinesia scoring and the treatment response scale (TRS). The raw tapping and spiral data were processed and analyzed with time series analysis techniques to extract 37 spatiotemporal features. For each of the five scales, separate machine learning models were built and tested by using principal components of the features as predictors and mean ratings of the three specialists as target variables. Results: There were weak to moderate correlations between smartphone-based scores and mean ratings of UPDRS item #23 (0.52; finger tapping), UPDRS #25 (0.47; rapid alternating movements of hands), UPDRS #31 (0.57; body bradykinesia and hypokinesia), sum of the three UPDRS items (0.46), dyskinesia (0.64), and TRS (0.59). When assessing the test-retest reliability of the scores it was found that, in general, the clinical scores had better test-retest reliability than the smartphone-based scores. Only the smartphone-based predicted scores on the TRS and dyskinesia scales had good repeatability with intra-class correlation coefficients of 0.51 and 0.84, respectively. Clinician-based scores had higher effect sizes than smartphone-based scores indicating a better responsiveness in detecting changes in relation to treatment interventions. However, the first principal component of the 37 features was able to capture changes throughout the levodopa cycle and had trends similar to the clinical TRS and dyskinesia scales. Smartphone-based scores differed significantly between patients and healthy controls. Conclusions: Quantifying PD motor symptoms via instrumented, dexterity tests employed in a smartphone is feasible and data from such tests can also be used for measuring treatment-related changes in patients.

Place, publisher, year, edition, pages
2017. Vol. 9, p. 11-17
Keywords [en]
Parkinson's disease; motor assessment; spiral tests; tapping tests; smartphone; dyskinesia; bradykinesia; objective measures; telemedicine
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-25034DOI: 10.1016/j.imu.2017.05.005OAI: oai:DiVA.org:du-25034DiVA, id: diva2:1096769
Funder
Knowledge FoundationAvailable from: 2017-05-19 Created: 2017-05-19 Last updated: 2019-02-27Bibliographically approved
In thesis
1. Smartphone-based Parkinson’s disease symptom assessment
Open this publication in new window or tab >>Smartphone-based Parkinson’s disease symptom assessment
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis consists of four research papers presenting a microdata analysis approach to assess and evaluate the Parkinson’s disease (PD) motor symptoms using smartphone-based systems. PD is a progressive neurological disorder that is characterized by motor symptoms. It is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Both patients’ perception regarding common symptom and their motor function need to be related to the repeated and time-stamped assessment; with this, the full extent of patient’s condition could be revealed. The smartphone enables and facilitates the remote, long-term and repeated assessment of PD symptoms. Two types of collected data from smartphone were used, one during a three year, and another during one-day clinical study. The data were collected from series of tests consisting of tapping and spiral motor tests. During the second time scale data collection, along smartphone-based measurements patients were video recorded while performing standardized motor tasks according to Unified Parkinson’s disease rating scales (UPDRS).

At first, the objective of this thesis was to elaborate the state of the art, sensor systems, and measures that were used to detect, assess and quantify the four cardinal and dyskinetic motor symptoms. This was done through a review study. The review showed that smartphones as the new generation of sensing devices are preferred since they are considered as part of patients’ daily accessories, they are available and they include high-resolution activity data. Smartphones can capture important measures such as forces, acceleration and radial displacements that are useful for assessing PD motor symptoms.

Through the obtained insights from the review study, the second objective of this thesis was to investigate whether a combination of tapping and spiral drawing tests could be useful to quantify dexterity in PD. More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD. The results from this study showed that tapping and spiral drawing tests that were collected by smartphone can detect movements reasonably well related to under- and over-medication.

The thesis continued by developing an Approximate Entropy (ApEn)-based method, which aimed to measure the amount of temporal irregularity during spiral drawing tests. One of the disabilities associated with PD is the impaired ability to accurately time movements. The increase in timing variability among patients when compared to healthy subjects, suggests that the Basal Ganglia (BG) has a role in interval timing. ApEn method was used to measure temporal irregularity score (TIS) which could significantly differentiate the healthy subjects and patients at different stages of the disease. This method was compared to two other methods which were used to measure the overall drawing impairment and shakiness. TIS had better reliability and responsiveness compared to the other methods. However, in contrast to other methods, the mean scores of the ApEn-based method improved significantly during a 3-year clinical study, indicating a possible impact of pathological BG oscillations in temporal control during spiral drawing tasks. In addition, due to the data collection scheme, the study was limited to have no gold standard for validating the TIS. However, the study continued to further investigate the findings using another screen resolution, new dataset, new patient groups, and for shorter term measurements. The new dataset included the clinical assessments of patients while they performed tests according to UPDRS. The results of this study confirmed the findings in the previous study. Further investigation when assessing the correlation of TIS to clinical ratings showed the amount of temporal irregularity present in the spiral drawing cannot be detected during clinical assessment since TIS is an upper limb high frequency-based measure. 

Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2017. p. 67
Series
Dalarna Licentiate Theses ; 6
Keywords
Parkinson’s disease; symptom assessment; spiral; tapping; smartphone; temporal irregularity; timing variability; approximate entropy;
National Category
Computer Systems
Research subject
Complex Systems – Microdata Analysis, FLOAT - Flexible Levodopa Optimizing Assistive Technology
Identifiers
urn:nbn:se:du-24925 (URN)978-91-85941-99-5 (ISBN)
Presentation
2017-06-02, Clas Ohlson, Borlänge, 11:43 (English)
Supervisors
Available from: 2017-05-15 Created: 2017-05-12 Last updated: 2018-12-12Bibliographically approved
2. Multisensor data-driven methods for automated quantification of motor symptoms in Parkinson’s disease
Open this publication in new window or tab >>Multisensor data-driven methods for automated quantification of motor symptoms in Parkinson’s disease
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The overall aim of this thesis was to develop and evaluate new data-driven methods for supporting treatment and providing information for better management of Parkinson’s disease (PD).

This disease is complex and progressive. There is a large amount of inter- and intra-variability in motor symptoms of patients with PD (PwPD). Current evaluation of motor symptoms which is done at clinics by using clinical rating scales provides limited and only part of the health status of PwPD. PD requires an accurate assessment that is approved by clinics. Therefore frequent evaluation of symptoms at micro-level is required.

Sensor systems including smartphone and motion sensors were employed to collect data from PwPD and the recruited healthy controls. Repeated measures consisting of subjective assessment of symptoms and objective assessment of motor functions were collected.

First, the smartphone-based data-driven methods were developed to quantify the dexterity presented in fine motor tests of spiral drawing and alternate tapping. The upper extremities temporal irregularity measure presented in spiral drawing tests of PwPD was further analyzed by the approximate entropy (ApEn) method. Second, tri-axial motion sensor data were collected from various tests like leg agility, walking, and rapid alternating movements of hands of PwPD during a full cycled levodopa challenge. Data driven methods for quantification of leg agility tests and a combination of multiple motor tests were developed. The clinimetric properties of the methods such as reliability, validity, and responsiveness were evaluated. In addition, the feasibility of using smartphone inertial measurement unit (IMU) sensors in comparison to motion sensors for quantifying the motor states in PD during rapid alternating movements of hands tests was investigated.

Results of the developed methods for quantification of PD motor symptoms via dexterity tests in a smartphone can be used for measuring treatment related changes in PwPD. Investigation of the ApEn method showed good sensitivity and test-retest reliability indicating that this method is useful in measuring upper limb temporal irregularity at micro-level. High convergence validity resulted from using motion sensors during leg agility tests which led to valid and reliable objective measures of PD motor symptoms. The results of fusion of sensor data gathered during standardized motor tests were promising and led to highly valid, reliable and sensitive objective measures of PD motor symptoms. The results of the analyzing acceleration IMU data showed that smartphone IMU is capable of capturing symptom information from hand rotation tests. It can provide sufficient data for quantification of the motor states.

The findings from the data-driven methodology in this thesis can be used in development of systems for follow up of the effects of treatment and individualizing treatments in PD.

Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2019
Series
Dalarna Doctoral Dissertations ; 10
Keywords
Parkinson’s disease, motor symptoms, motion sensors, smartphone, microdata, multivariate analysis, data-driven, support vector machine, stepwise regression, predictive models
National Category
Medical Laboratory and Measurements Technologies Biomedical Laboratory Science/Technology Computer Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-29583 (URN)978-91-88679-00-0 (ISBN)
Public defence
2019-04-26, Clas Ohlson, Borlänge, 13:00 (English)
Opponent
Supervisors
Available from: 2019-03-28 Created: 2019-02-27 Last updated: 2019-03-28Bibliographically approved

Open Access in DiVA

fulltext(953 kB)66 downloads
File information
File name FULLTEXT02.pdfFile size 953 kBChecksum SHA-512
8059154a811b16b0fd07d8f5bd5b0fb1e7268359654f7e13e4a24b854c98030d01a3eb4030270f56b9ddf78b278a22e363abc4fad7f42a695e51059e3e9579b5
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records BETA

Aghanavesi, SomayehMemedi, Mevludin

Search in DiVA

By author/editor
Aghanavesi, SomayehMemedi, Mevludin
By organisation
Microdata AnalysisComputer Engineering
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 69 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 258 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf