du.sePublikasjoner
Endre søk
Link to record
Permanent link

Direct link
BETA
Publikasjoner (10 av 14) Visa alla publikasjoner
Khan, T., Memedi, M., Song, W. W. & Westin, J. (2014). A case study in healthcare informatics: a telemedicine framework for automated parkinson’s disease symptom assessment. In: Zheng X. et al. (Ed.), Smart Health: International Conference, ICSH 2014, Beijing, China, July 10-11, 2014. Proceedings. Paper presented at International Conference, ICSH 2014, Beijing, China, July 10-11, 2014 (pp. 197-199). Springer
Åpne denne publikasjonen i ny fane eller vindu >>A case study in healthcare informatics: a telemedicine framework for automated parkinson’s disease symptom assessment
2014 (engelsk)Inngår i: Smart Health: International Conference, ICSH 2014, Beijing, China, July 10-11, 2014. Proceedings / [ed] Zheng X. et al., Springer, 2014, s. 197-199Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper reports the development and evaluation of a mobile-based telemedicine framework for enabling remote monitoring of Parkinson’s disease (PD) symptoms. The system consists of different measurement devices for remote collection, processing and presentation of symptom data of advanced PD patients. Different numerical analysis techniques were applied on the raw symptom data to extract clinically symptom information which in turn were then used in a machine learning process to be mapped to the standard clinician-based measures. The methods for quantitative and automatic assessment of symptoms were then evaluated for their clinimetric properties such as validity, reliability and sensitivity to change. Results from several studies indicate that the methods had good metrics suggesting that they are appropriate to quantitatively and objectively assess the severity of motor impairments of PD patients.

sted, utgiver, år, opplag, sider
Springer, 2014
Serie
Lecture Notes in Computer Science, ISSN 0302-9743 ; 8549
Emneord
patient monitoring, Parkinson’s disease, sensors, machine learning, healthcare informatics, artificial intelligence
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys, PAULINA - Uppföljning av Parkinsonsymptom från hemmet
Identifikatorer
urn:nbn:se:du-15069 (URN)978-3-319-08416-9 (ISBN)
Konferanse
International Conference, ICSH 2014, Beijing, China, July 10-11, 2014
Forskningsfinansiär
Knowledge Foundation, 20130041
Tilgjengelig fra: 2014-08-27 Laget: 2014-08-27 Sist oppdatert: 2018-01-11bibliografisk kontrollert
Khan, T., Nyholm, D., Westin, J. & Dougherty, M. (2014). A computer vision framework for finger-tapping evaluation in Parkinson’s disease. Artificial Intelligence in Medicine, 60(1), 27-40
Åpne denne publikasjonen i ny fane eller vindu >>A computer vision framework for finger-tapping evaluation in Parkinson’s disease
2014 (engelsk)Inngår i: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 60, nr 1, s. 27-40Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Objectives: The rapid finger-tapping test (RFT) is an important method for clinical evaluation of movementdisorders, including Parkinson’s disease (PD). In clinical practice, the naked-eye evaluation of RFT results in a coarse judgment of symptom scores. We introduce a novel computer-vision (CV) method forquantification of tapping symptoms through motion analysis of index fingers. The method is unique asit utilizes facial features to calibrate tapping amplitude for normalization of distance variation betweenthe camera and subject.

Methods: The study involved 387 video footages of RFT recorded from 13 patients diagnosed with advanced PD. Tapping performance in these videos was rated by two clinicians between the symptom severity levels (‘0: normal’ to ‘3: severe’) using the unified Parkinson’s disease rating scale motor examination of finger-tapping (UPDRS-FT). Another set of recordings in this study consisted of 84 videos of RFT recorded from 6 healthy controls. These videos were processed by a CV algorithm that tracks the index-finger motion between the video-frames to produce a tapping time-series. Different features were computed from this time series to estimate speed, amplitude, rhythm and fatigue in tapping. The features were trained in a support vector machine (1) to categorize the patient group between UPDRS-FT symptom severity levels, and (2) to discriminate between PD patients and healthy controls.

Results: A new representative feature of tapping rhythm, ‘cross-correlation between the normalized peaks’ showed strong Guttman correlation (u2 =−0.80) with the clinical ratings. The classification oftapping features using the support vector machine classifier and 10-fold cross validation categorized the patient samples between UPDRS-FT levels with an accuracy of 88%. The same classification scheme discriminated between RFT samples of healthy controls and PD patients with an accuracy of 95%.

Conclusion: The work supports the feasibility of the approach, which is presumed suitable for PD monitoringin the home environment. The system offers advantages over other technologies (e.g. magneticsensors, accelerometers, etc.) previously developed for objective assessment of tapping symptoms.

sted, utgiver, år, opplag, sider
Elsevier, 2014
Emneord
Computer vision; Motion analysis; Face detection; Parkinson's disease; Finger-tapping
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys, PAULINA - Uppföljning av Parkinsonsymptom från hemmet
Identifikatorer
urn:nbn:se:du-13358 (URN)10.1016/j.artmed.2013.11.004 (DOI)000331506200003 ()
Prosjekter
PAULINA
Forskningsfinansiär
Knowledge Foundation
Tilgjengelig fra: 2013-11-28 Laget: 2013-11-28 Sist oppdatert: 2017-12-06bibliografisk kontrollert
Khan, T., Westin, J. & Dougherty, M. (2014). Cepstral separation difference: a novel approach for speech impairment quantification in Parkinson’s disease. Biocybernetics and Biomedical Engineering, 34(1), 25-34
Åpne denne publikasjonen i ny fane eller vindu >>Cepstral separation difference: a novel approach for speech impairment quantification in Parkinson’s disease
2014 (engelsk)Inngår i: Biocybernetics and Biomedical Engineering, ISSN 0208-5216, Vol. 34, nr 1, s. 25-34Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper introduces a novel approach, Cepstral Separation Difference (CSD), for quantification of speech impairment in Parkinson’s disease (PD). CSD represents a ratio between the magnitudes of glottal (source) and supra-glottal (filter) log-spectrums acquired using the source-filter speech model. The CSD-based features were tested on a database consisting of 240 clinically rated running speech samples acquired from 60 PD patients and 20 healthy controls. The Guttmann (µ2) monotonic correlations between the CSD features and the speech symptom severity ratings were strong (up to 0.78). This correlation increased with the increasing textual difficulty in different speech tests. CSD was compared with some non-CSD speech features (harmonic ratio, harmonic-to-noise ratio and Mel-frequency cepstral coefficients) for speech symptom characterization in terms of consistency and reproducibility. The high intra-class correlation coefficient (>0.9) and analysis of variance indicates that CSD features can be used reliably to distinguish between severity levels of speech impairment. Results motivate the use of CSD in monitoring speech symptoms in PD.

sted, utgiver, år, opplag, sider
Elsevier, 2014
Emneord
Parkinson's disease; Speech processing; Dysarthria; Acoustic analysis; Speech cepstrum
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys, PAULINA - Uppföljning av Parkinsonsymptom från hemmet
Identifikatorer
urn:nbn:se:du-12730 (URN)10.1016/j.bbe.2013.06.001 (DOI)000333226500005 ()
Forskningsfinansiär
Knowledge Foundation
Tilgjengelig fra: 2013-07-29 Laget: 2013-07-29 Sist oppdatert: 2015-07-01bibliografisk kontrollert
Khan, T., Westin, J. & Dougherty, M. (2014). Classification of speech intelligibility in Parkinson's disease: Speech Impairment Classification. Biocybernetics and Biomedical Engineering, 34(1), 35-45
Åpne denne publikasjonen i ny fane eller vindu >>Classification of speech intelligibility in Parkinson's disease: Speech Impairment Classification
2014 (engelsk)Inngår i: Biocybernetics and Biomedical Engineering, ISSN 0208-5216, Vol. 34, nr 1, s. 35-45Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

A problem in the clinical assessment of running speech in Parkinson's disease (PD) is to track underlying deficits in a number of speech components including respiration, phonation, articulation and prosody, each of which disturbs the speech intelligibility. A set of 13 features, including the cepstral separation difference and Mel-frequency cepstral coefficients were computed to represent deficits in each individual speech component. These features were then used in training a support vector machine (SVM) using n-fold cross validation. The dataset used for method development and evaluation consisted of 240 running speech samples recorded from 60 PD patients and 20 healthy controls. These speech samples were clinically rated using the Unified Parkinson's Disease Rating Scale Motor Examination of Speech (UPDRS-S). The classification accuracy of SVM was 85% in 3 levels of UPDRS-S scale and 92% in 2 levels with the average area under the ROC (receiver operating characteristic) curves of around 91%. The strong classification ability of selected features and the SVM model supports suitability of this scheme to monitor speech symptoms in PD

sted, utgiver, år, opplag, sider
Elsevier, 2014
Emneord
Parkinson's disease, Speech processing, Dysarthria, Support vector machine, Tele-monitoring.
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys, PAULINA - Uppföljning av Parkinsonsymptom från hemmet
Identifikatorer
urn:nbn:se:du-13130 (URN)10.1016/j.bbe.2013.10.003 (DOI)000333226500006 ()
Prosjekter
PAULINA
Forskningsfinansiär
Knowledge Foundation
Tilgjengelig fra: 2013-10-10 Laget: 2013-10-10 Sist oppdatert: 2015-07-01bibliografisk kontrollert
Khan, T. (2014). First-principle data-driven models for assessment of motor disorders in Parkinson’s disease. (Doctoral dissertation). Sweden: Mälardalen University
Åpne denne publikasjonen i ny fane eller vindu >>First-principle data-driven models for assessment of motor disorders in Parkinson’s disease
2014 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic. 

The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait.

The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

sted, utgiver, år, opplag, sider
Sweden: Mälardalen University, 2014. s. 102
Serie
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 153
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys
Identifikatorer
urn:nbn:se:du-16679 (URN)978-91-7485-142-7 (ISBN)
Disputas
2014-04-16, Clas Ohlson, Studenternas Hus Tenoren, Campus Borlänge, 13:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2015-01-13 Laget: 2015-01-13 Sist oppdatert: 2015-06-29bibliografisk kontrollert
Khan, T., Nyholm, D., Westin, J. & Dougherty, M. (2013). A Computer Vision Framework For Finger-Tapping Evaluation In Parkinson's Disease. In: Movement Disorders: Supplement: Abstracts of the Seventeenth International Congress of Parkinson's Disease and Movement Disorders. Paper presented at 17th International Congress of Parkinson's Disease and Movement Disorders, June 16-20 2013 (pp. 110-111). Movement Disorder Society
Åpne denne publikasjonen i ny fane eller vindu >>A Computer Vision Framework For Finger-Tapping Evaluation In Parkinson's Disease
2013 (engelsk)Inngår i: Movement Disorders: Supplement: Abstracts of the Seventeenth International Congress of Parkinson's Disease and Movement Disorders, Movement Disorder Society , 2013, s. 110-111Konferansepaper, Poster (with or without abstract) (Fagfellevurdert)
Abstract [en]

Objective:

To define and evaluate a Computer-Vision (CV) method for scoring Paced Finger-Tapping (PFT) in Parkinson's disease (PD) using quantitative motion analysis of index-fingers and to compare the obtained scores to the UPDRS (Unified Parkinson's Disease Rating Scale) finger-taps (FT).

Background:

The naked-eye evaluation of PFT in clinical practice results in coarse resolution to determine PD status. Besides, sensor mechanisms for PFT evaluation may cause patients discomfort. In order to avoid cost and effort of applying wearable sensors, a CV system for non-invasive PFT evaluation is introduced.

Methods:

A database of 221 PFT videos from 6 PD patients was processed. The subjects were instructed to position their hands above their shoulders besides the face and tap the index-finger against the thumb consistently with speed. They were facing towards a pivoted camera during recording. The videos were rated by two clinicians between symptom levels 0-to-3 using UPDRS-FT.

The CV method incorporates a motion analyzer and a face detector. The method detects the face of testee in each video-frame. The frame is split into two images from face-rectangle center. Two regions of interest are located in each image to detect index-finger motion of left and right hands respectively. The tracking of opening and closing phases of dominant hand index-finger produces a tapping time-series. This time-series is normalized by the face height. The normalization calibrates the amplitude in tapping signal which is affected by the varying distance between camera and subject (farther the camera, lesser the amplitude). A total of 15 features were classified using K-nearest neighbor (KNN) classifier to characterize the symptoms levels in UPDRS-FT. The target ratings provided by the raters were averaged.

Results:

A 10-fold cross validation in KNN classified 221 videos between 3 symptom levels with 75% accuracy. An area under the receiver operating characteristic curves of 82.6% supports feasibility of the obtained features to replicate clinical assessments.

Conclusions:

The system is able to track index-finger motion to estimate tapping symptoms in PD. It has certain advantages compared to other technologies (e.g. magnetic sensors, accelerometers etc.) for PFT evaluation to improve and automate the ratings

sted, utgiver, år, opplag, sider
Movement Disorder Society, 2013
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys, PAULINA - Uppföljning av Parkinsonsymptom från hemmet
Identifikatorer
urn:nbn:se:du-13113 (URN)10.1002/mds.25605 (DOI)000320940501046 ()
Konferanse
17th International Congress of Parkinson's Disease and Movement Disorders, June 16-20 2013
Prosjekter
PAULINA
Forskningsfinansiär
Knowledge Foundation
Tilgjengelig fra: 2013-10-07 Laget: 2013-10-07 Sist oppdatert: 2015-06-29bibliografisk kontrollert
Memedi, M., Khan, T., Grenholm, P., Nyholm, D. & Westin, J. (2013). Automatic and objective assessment of alternating tapping performance in Parkinson’s disease. Sensors, 13(12), 16965-16984
Åpne denne publikasjonen i ny fane eller vindu >>Automatic and objective assessment of alternating tapping performance in Parkinson’s disease
Vise andre…
2013 (engelsk)Inngår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, nr 12, s. 16965-16984Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper presents the development and evaluation of a method for enabling quantitative and automatic scoring of alternating tapping performance of patients with Parkinson’s disease (PD). Ten healthy elderly subjects and 95 patients in different clinical stages of PD have utilized a touch-pad handheld computer to perform alternate tapping tests in their home environments. First, a neurologist used a web-based system to visually assess impairments in four tapping dimensions (‘speed’, ‘accuracy’, ‘fatigue’ and ‘arrhythmia’) and a global tapping severity (GTS). Second, tapping signals were processed with time series analysis and statistical methods to derive 24 quantitative parameters. Third, principal component analysis was used to reduce the dimensions of these parameters and to obtain scores for the four dimensions. Finally, a logistic regression classifier was trained using a 10-fold stratified cross-validation to map the reduced parameters to the corresponding visually assessed GTS scores. Results showed that the computed scores correlated well to visually assessed scores and were significantly different across Unified Parkinson’s Disease Rating Scale scores of upper limb motor performance. In addition, they had good internal consistency, had good ability to discriminate between healthy elderly and patients in different disease stages, had good sensitivity to treatment interventions and could reflect the natural disease progression over time. In conclusion, the automatic method can be useful to objectively assess the tapping performance of PD patients and can be included in telemedicine tools for remote monitoring of tapping.

Emneord
alternating tapping, touch-pad, handheld computer, telemedicine, Parkinson’s disease, remote monitoring, automatic assessment, objective assessment, visual assessment
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys, PAULINA - Uppföljning av Parkinsonsymptom från hemmet
Identifikatorer
urn:nbn:se:du-13473 (URN)10.3390/s131216965 (DOI)000330220600061 ()
Merknad

Open Access

Tilgjengelig fra: 2013-12-09 Laget: 2013-12-09 Sist oppdatert: 2017-12-06bibliografisk kontrollert
Khan, T., Grenholm, P. & Nyholm, D. (2013). Computer Vision Methods for Parkinsonian Gait Analysis: A Review on Patents. Recent Patents on Biomedical Engineering, 6(2), 97-108
Åpne denne publikasjonen i ny fane eller vindu >>Computer Vision Methods for Parkinsonian Gait Analysis: A Review on Patents
2013 (engelsk)Inngår i: Recent Patents on Biomedical Engineering, ISSN 1874-7647, Vol. 6, nr 2, s. 97-108Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Gait disturbance is an important symptom of Parkinson’s disease (PD). This paper presents a review of patents reported in the area of computerized gait disorder analysis. The feasibility of marker-less vision based systems has been examined for ‘at-home’ self-evaluation of gait taking into account the physical restrictions of patients arise due to PD. A three tier review methodology has been utilized to synthesize gait applications to investigate PD related gait features and to explore methods for gait classification based on symptom severities. A comparison between invasive and non-invasive methods for gait analysis revealed that marker-free approach can provide resource efficient, convenient and accurate gait measurements through the use of image processing methods. Image segmentation of human silhouette is the major challenge in the marker-free systems which can possibly be comprehended through the use of Microsoft Kinect application and motion estimation algorithms. Our synthesis further suggests that biorhythmic features in gait patterns have potential to discriminate gait anomalies based on the clinical scales. 

sted, utgiver, år, opplag, sider
Netherlands: Bentham Science Publishers, 2013
Emneord
Gait Impairment, Parkinson’s disease, Gait Video Analysis, and Image Processing.
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys, PAULINA - Uppföljning av Parkinsonsymptom från hemmet
Identifikatorer
urn:nbn:se:du-12731 (URN)10.2174/1874764711306020004 (DOI)
Forskningsfinansiär
Knowledge Foundation
Tilgjengelig fra: 2013-07-29 Laget: 2013-07-29 Sist oppdatert: 2015-06-29bibliografisk kontrollert
Khan, T., Westin, J. & Dougherty, M. (2013). Motion cue analysis for parkinsonian gait recognition. The open biomedical engineering journal, 7, 1-8
Åpne denne publikasjonen i ny fane eller vindu >>Motion cue analysis for parkinsonian gait recognition
2013 (engelsk)Inngår i: The open biomedical engineering journal, ISSN 1874-1207, Vol. 7, s. 1-8Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper presents a computer-vision based marker-free method for gait-impairment detection in Patients with Parkinson's disease (PWP). The system is based upon the idea that a normal human body attains equilibrium during the gait by aligning the body posture with Axis-of-Gravity (AOG) using feet as the base of support. In contrast, PWP appear to be falling forward as they are less-able to align their body with AOG due to rigid muscular tone. A normal gait exhibits periodic stride-cycles with stride-angle around 45o between the legs, whereas PWP walk with shortened stride-angle with high variability between the stride-cycles. In order to analyze Parkinsonian-gait (PG), subjects were videotaped with several gait-cycles. The subject's body was segmented using a color-segmentation method to form a silhouette. The silhouette was skeletonized for motion cues extraction. The motion cues analyzed were stride-cycles (based on the cyclic leg motion of skeleton) and posture lean (based on the angle between leaned torso of skeleton and AOG). Cosine similarity between an imaginary perfect gait pattern and the subject gait patterns produced 100% recognition rate of PG for 4 normal-controls and 3 PWP. Results suggested that the method is a promising tool to be used for PG assessment in home-environment.

HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys, E-MOTIONS, Beslutsstöd för Parkinsonbehandling
Identifikatorer
urn:nbn:se:du-11881 (URN)10.2174/1874120701307010001 (DOI)23407764 (PubMedID)
Merknad

Open Access

Tilgjengelig fra: 2013-02-22 Laget: 2013-02-22 Sist oppdatert: 2018-01-11bibliografisk kontrollert
Khan, T., Westin, J., Funk, P. & Dougherty, M. (2012). Quantification of speech impairment in Parkinson's disease. Paper presented at 16th International Congress of Parkinson's Disease and Movement Disorders, JUN 17-21, 2012, Dublin, IRELAND. Movement Disorders, 27, S510-S511
Åpne denne publikasjonen i ny fane eller vindu >>Quantification of speech impairment in Parkinson's disease
2012 (engelsk)Inngår i: Movement Disorders, ISSN 0885-3185, E-ISSN 1531-8257, Vol. 27, s. S510-S511Artikkel i tidsskrift, Meeting abstract (Fagfellevurdert) Published
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys, E-MOTIONS, Beslutsstöd för Parkinsonbehandling
Identifikatorer
urn:nbn:se:du-10775 (URN)000305507704329 ()
Konferanse
16th International Congress of Parkinson's Disease and Movement Disorders, JUN 17-21, 2012, Dublin, IRELAND
Tilgjengelig fra: 2012-09-28 Laget: 2012-09-20 Sist oppdatert: 2018-01-12bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-2752-3712