Video-Based Automated Assessment of Movement Parameters Consistent with MDS-UPDRS III in Parkinson’s Disease

(1) Background: A noticeable association between the motor activity (MA) profiles of persons living together has been found in previous studies. Social actigraphy methods have shown that this association, in marital dyads composed of healthy individuals, is greater than that of a single person compared to itself. This study aims at verifying the association of MA profiles in dyads where one component is affected by Parkinson’s disease (PD). (2) Methods: Using a wearable sensor-based social actigraphy approach, we continuously monitored, for 7 days, the activities of 27 marital dyads including one component with PD. (3) Results: The association of motor activity profiles within a marital dyad (cross-correlation coefficient 0.344) is comparable to the association of any participant with themselves (0.325). However, when considering the disease severity quantified by the UPDRS III score, it turns out that the less severe the symptoms, the more associated are the MA profiles. (4) Conclusions: Our findings suggest that PD treatment could be improved by leveraging the MA of the healthy spouse, thus promoting lifestyles also beneficial for the component affected by PD. The actigraphy approach provided valuable information on habitual functions and motor fluctuations, and could be useful in investigating the response to treatment.

Section: Discussionsupporting

confidence: 71%

Association of 7-Day Profiles of Motor Activity in Marital Dyads with One Component Affected by Parkinson’s Disease

Rabuffetti

Giovannini²,

Carpinella

et al. 2023

“…Park et al [32] extracted kinematic features related to the amplitude, velocity, and decremental response of bradykinesia from 110 videos of finger tapping and utilized a Support Vector Machine (SVM) classifier to show good reliability with clinical ratings. Vignoud et al [38] proposed a method using seven parameters extracted from finger tapping and hand movement videos representing speed, amplitude, fatigue, and periodicity to predict the appropriate UDPRS score with linear regression and Decision Tree models. Liu et al [39] incorporated a more-novel network architecture, a Global Temporal-difference Shift Network (GTSN), to estimate the MDS-UPDRS tremor scores from video recordings.…”

Section: The Current Landscape Of Video-based Assessment For Parkinso...mentioning

confidence: 99%

Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson’s Disease

Yu,

Park,

McKeown

et al. 2023

The utilization of Artificial Intelligence (AI) for assessing motor performance in Parkinson’s Disease (PD) offers substantial potential, particularly if the results can be integrated into clinical decision-making processes. However, the precise quantification of PD symptoms remains a persistent challenge. The current standard Unified Parkinson’s Disease Rating Scale (UPDRS) and its variations serve as the primary clinical tools for evaluating motor symptoms in PD, but are time-intensive and prone to inter-rater variability. Recent work has applied data-driven machine learning techniques to analyze videos of PD patients performing motor tasks, such as finger tapping, a UPDRS task to assess bradykinesia. However, these methods often use abstract features that are not closely related to clinical experience. In this paper, we introduce a customized machine learning approach for the automated scoring of UPDRS bradykinesia using single-view RGB videos of finger tapping, based on the extraction of detailed features that rigorously conform to the established UPDRS guidelines. We applied the method to 75 videos from 50 PD patients collected in both a laboratory and a realistic clinic environment. The classification performance agreed well with expert assessors, and the features selected by the Decision Tree aligned with clinical knowledge. Our proposed framework was designed to remain relevant amid ongoing patient recruitment and technological progress. The proposed approach incorporates features that closely resonate with clinical reasoning and shows promise for clinical implementation in the foreseeable future.

“…Aside from inertial sensors, smartphone-based methods for capturing finger-tapping tasks have shown reliable correlations with MDS-UPDRS motor scores [26,33]. Similarly, video-based recordings of movements are useful for predicting expert-rated MDS-UPDRS motor scores [34,35]. Their reliance on patient self-recording and lack of external validation, however, limits comparability and reproducibility [34].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, video-based recordings of movements are useful for predicting expert-rated MDS-UPDRS motor scores [34,35]. Their reliance on patient self-recording and lack of external validation, however, limits comparability and reproducibility [34].…”

Section: Introductionmentioning

confidence: 99%

Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson’s Disease Using Machine Learning

Bremm,

Pavelka,

Garcia

et al. 2024

Wearable sensors could be beneficial for the continuous quantification of upper limb motor symptoms in people with Parkinson’s disease (PD). This work evaluates the use of two inertial measurement units combined with supervised machine learning models to classify and predict a subset of MDS-UPDRS III subitems in PD. We attached the two compact wearable sensors on the dorsal part of each hand of 33 people with PD and 12 controls. Each participant performed six clinical movement tasks in parallel with an assessment of the MDS-UPDRS III. Random forest (RF) models were trained on the sensor data and motor scores. An overall accuracy of 94% was achieved in classifying the movement tasks. When employed for classifying the motor scores, the averaged area under the receiver operating characteristic values ranged from 68% to 92%. Motor scores were additionally predicted using an RF regression model. In a comparative analysis, trained support vector machine models outperformed the RF models for specific tasks. Furthermore, our results surpass the literature in certain cases. The methods developed in this work serve as a base for future studies, where home-based assessments of pharmacological effects on motor function could complement regular clinical assessments.