Decomposition of complex movements into primitives for Parkinson's disease assessment

Pissadaki, Eleftheria Kyriaki; Abrami, Avner; Heisig, Stephen; Bilal, Erhan; Cavallo, Marco; Wacnik, Paul W.; Erb, Kelley; Karlin, Daniel R.; Bergethon, Peter R.; Amato, Stephen; Zhang, Hao; Ramos, Vesper; Hameed, Farhan; Rice, John Jeremy

doi:10.1147/jrd.2017.2768739

Cited by 16 publications

(16 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This justification can be further supported by the data in Figure 4 (as well as the data in Supplement Figure S1 ), which indicates that the features representing the changes in the clusters of the symptom-based features had the most importance to the classifier’s decision. This supports the novelty aspect (3) of our proposed algorithm meaning that an incremental feature extraction approach as developed in our work is more effective than the conventional approaches, which attempt to individually model each segment of data [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 26 ].…”

Section: Discussionsupporting

confidence: 70%

“…However, they generate abstract indices about motor impairments (i.e., tremor and bradykinesia), which have not been associated with the degree of motor fluctuation severity that is required for the treating physician to effectively adjust therapy. Other approaches attempt to estimate UPDRS III scores from each time point based on some symptom-based features (e.g., spectral power in 4–6 Hz frequencies for tremor and 1–4 Hz frequencies for bradykinesia) [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ] or deep learning algorithms [ 22 , 23 ]. Such approaches could be useful in detection of PD from healthy subjects.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Feature Extraction for Detection of Degree of Motor Fluctuation Severity in Parkinson’s Disease Patients

Hssayeni

Jimenez-Shahed

Ghoraani

2019

Entropy

View full text Add to dashboard Cite

The success of medication adjustment in Parkinson’s disease (PD) patients with motor fluctuation relies on the knowledge about their fluctuation severity. However, because of the temporal and spatial variability in motor fluctuations, a single clinical examination often fails to capture the spectrum of motor impairment experienced in routine daily life. In this study, we developed an algorithm to estimate the degree of motor fluctuation severity from two wearable sensors’ data during subjects’ free body movements. Specifically, we developed a new hybrid feature extraction method to represent the longitudinal changes of motor function from the sensor data. Next, we developed a classification model based on random forest to learn the changes in the patterns of the sensor data as the severity of the motor function changes. We evaluated our algorithm using data from 24 subjects with idiopathic PD as they performed a variety of daily routine activities. A leave-one-subject-out assessment of the algorithm resulted in 83.33% accuracy, indicating that our approach holds a great promise to passively detect degree of motor fluctuation severity from continuous monitoring of an individual’s free body movements. Such a sensor-based assessment system and algorithm combination could provide the objective and comprehensive information about the fluctuation severity that can be used by the treating physician to effectively adjust therapy for PD patients with troublesome motor fluctuation.

show abstract

Section: Discussionsupporting

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Hybrid Feature Extraction for Detection of Degree of Motor Fluctuation Severity in Parkinson’s Disease Patients

Hssayeni

Jimenez-Shahed

Ghoraani

2019

Entropy

View full text Add to dashboard Cite

show abstract

“…A portion of the protocol has been described previously. 46 All assessments were conducted in a controlled laboratory environment. Participants donned 14 wearable devices from two manufacturers (APDM and MC10; Supplementary Fig.…”

Section: Study 1: Healthy Volunteersmentioning

confidence: 99%

mHealth and wearable technology should replace motor diaries to track motor fluctuations in Parkinson’s disease

Erb

Karlin

et al. 2020

npj Digit. Med.

View full text Add to dashboard Cite

Accurately monitoring motor and non-motor symptoms as well as complications in people with Parkinson's disease (PD) is a major challenge, both during clinical management and when conducting clinical trials investigating new treatments. A variety of strategies have been relied upon including questionnaires, motor diaries, and the serial administration of structured clinical exams like part III of the MDS-UPDRS. To evaluate the potential use of mobile and wearable technologies in clinical trials of new pharmacotherapies targeting PD symptoms, we carried out a project (project BlueSky) encompassing four clinical studies, in which 60 healthy volunteers (aged 23-69; 33 females) and 95 people with PD (aged 42-80; 37 females; years since diagnosis 1-24 years; Hoehn and Yahr 1-3) participated and were monitored in either a laboratory environment, a simulated apartment, or at home and in the community. In this paper, we investigated (i) the utility and reliability of self-reports for describing motor fluctuations; (ii) the agreement between participants and clinical raters on the presence of motor complications; (iii) the ability of video raters to accurately assess motor symptoms, and (iv) the dynamics of tremor, dyskinesia, and bradykinesia as they evolve over the medication cycle. Future papers will explore methods for estimating symptom severity based on sensor data. We found that 38% of participants who were asked to complete an electronic motor diary at home missed~25% of total possible entries and otherwise made entries with an average delay of >4 h. During clinical evaluations by PD specialists, self-reports of dyskinesia were marked bỹ 35% false negatives and 15% false positives. Compared with live evaluation, the video evaluation of part III of the MDS-UPDRS significantly underestimated the subtle features of tremor and extremity bradykinesia, suggesting that these aspects of the disease may be underappreciated during remote assessments. On the other hand, live and video raters agreed on aspects of postural instability and gait. Our results highlight the significant opportunity for objective, high-resolution, continuous monitoring afforded by wearable technology to improve upon the monitoring of PD symptoms.

show abstract

“…Clinical trial research has observed that subtle changes in cognition, sensory, and motor function precede clinical manifestations of neurodegenerative disease by several years, 78 and there are a number of trials exploring feasibility of digital biomarkers for Parkinson's [79][80][81][82][83] and Alzheimer's disease. 84,85 While digital health tools offer the promise of remote, high-resolution, and high-frequency clinical observations, there continues to be a significant delay in the pharmaceutical industry to embrace technology.…”

Section: Accepted Articlementioning

confidence: 99%

Digitally Enabled, Patient‐Centric Clinical Trials: Shifting the Drug Development Paradigm

Dockendorf

Hansen

Bateman

et al. 2020

Clinical Translational Sci

View full text Add to dashboard Cite

The rapidly advancing field of digital health technologies provides a great opportunity to radically transform the way clinical trials are conducted and to shift the clinical trial paradigm from a site-centric to a patientcentric model. Merck & Co., Inc.'s (Kenilworth, NJ, USA) digitally-enabled clinical trial initiative is focused on introduction of digital technologies into the clinical trial paradigm to reduce patient burden, improve drug adherence, provide a means of more closely engaging with the patient, and enable higher quality, faster, and Accepted Article This article is protected by copyright. All rights reserved more frequent data collection. This paper will describe the following four key areas of focus from Merck & Co., Inc.'s (Kenilworth, NJ, USA) digitally-enabled clinical trials initiative, along with corresponding enabling technologies: (1) use of technologies that can monitor and improve drug adherence (smart dosing), (2) collection of pharmacokinetic, pharmacodynamic, and biomarker samples in an outpatient setting (patientcentric sampling), (3) use of digital devices to collect and measure physiological and behavioral data (digital biomarkers), and (4) use of data platforms that integrate digital data streams, visualize data in real-time, and provide a means of greater patient engagement during the trial (digital platform). Furthermore, this paper will discuss the synergistic power in implementation of these approaches jointly within a trial to enable better understanding of adherence, safety, efficacy, pharmacokinetics, pharmacodynamics, and corresponding exposure-response relationships of investigational therapies as well as reduced patient burden for clinical trial participation. Obstacle and challenges to adoption and full realization of the vision of patient-centric, digitally-enabled trials will also be discussed.

show abstract

Decomposition of complex movements into primitives for Parkinson's disease assessment

Cited by 16 publications

References 30 publications

Hybrid Feature Extraction for Detection of Degree of Motor Fluctuation Severity in Parkinson’s Disease Patients

Hybrid Feature Extraction for Detection of Degree of Motor Fluctuation Severity in Parkinson’s Disease Patients

mHealth and wearable technology should replace motor diaries to track motor fluctuations in Parkinson’s disease

Digitally Enabled, Patient‐Centric Clinical Trials: Shifting the Drug Development Paradigm

Contact Info

Product

Resources

About