Continuous Assessment of Levodopa Response in Parkinson's Disease Using Wearable Motion Sensors

Pulliam, Christopher L.; Heldman, Dustin A.; Brokaw, Elizabeth B.; Mera, Thomas O.; Mari, Zoltán; Burack, Michelle A.

doi:10.1109/tbme.2017.2697764

Cited by 99 publications

(91 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A large body of literature shows that algorithms designed to analyze the data collected during the performance of scripted motor tasks provide accurate estimates, compared with clinician ratings, of the severity of PD symptoms. [27][28][29][30][31][32][33][34] However, future work is needed to derive accurate estimates of the severity of PD symptoms from the data collected during the performance of unscripted activities, namely during the performance of ADL's. 17,35 One possible approach suitable to analyzing data collected during the performance of unscripted activities consists of implementing the cascade of two modules.…”

Section: Discussionmentioning

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

Section: Discussionmentioning

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

show abstract

“…A survey of prior work focused on development and validation of methods aimed at monitoring tremor and bradykinesia during free-living activities can be found in Supplementary Table 1. Many of these systems use multiple sensing devices [24][25][26] and modalities [27][28][29][30] to measure a number of motor symptoms that are present in PD. For example, Zwartjes et al 29 proposed a hierarchical framework for context specific assessment of tremor and bradykinesia using inertial sensors (accelerometer and gyroscope) located at four locations on the body.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, commercially available systems like Kinesia 32 and Personal KinetiGraph (PKG) 33 aim to minimize patient burden by reducing the number of devices for monitoring patients under free-living conditions. Using multi-modal (accelerometer and gyroscope) data recorded from devices located on the wrist and ankle of the most affected side, Pulliam et al 27 were able to achieve good accuracy (AUC > 0.8) for detection of tremor, bradykinesia, and dyskinesia as well as ability to differentiate (p < 0.01) between treatment states (ON and OFF). The PKG is a wrist-worn device for continuous at-home monitoring, which implements heuristic algorithms that process raw accelerometer data to generate a score for motor symptoms (e.g., dyskinesia and bradykinesia 34 ).…”

Section: Related Workmentioning

confidence: 99%

Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device

et al. 2020

View full text Add to dashboard Cite

Objective assessment of Parkinson's disease symptoms during daily life can help improve disease management and accelerate the development of new therapies. However, many current approaches require the use of multiple devices, or performance of prescribed motor activities, which makes them ill-suited for free-living conditions. Furthermore, there is a lack of open methods that have demonstrated both criterion and discriminative validity for continuous objective assessment of motor symptoms in this population. Hence, there is a need for systems that can reduce patient burden by using a minimal sensor setup while continuously capturing clinically meaningful measures of motor symptom severity under free-living conditions. We propose a method that sequentially processes epochs of raw sensor data from a single wrist-worn accelerometer by using heuristic and machine learning models in a hierarchical framework to provide continuous monitoring of tremor and bradykinesia. Results show that sensor derived continuous measures of resting tremor and bradykinesia achieve good to strong agreement with clinical assessment of symptom severity and are able to discriminate between treatment-related changes in motor states.

show abstract

“…The thesis is organized in five chapters. Brief information about each chapter is mentioned below: The second dataset that is used in this thesis was collected under a protocol [35], which was designed to continuously monitor PD symptoms while PD patients are engaging in daily living activities. Thirteen PD patients participated in the study.…”

Section: Organization Of the Thesismentioning

confidence: 99%

Automatic assessment of medication states of patients with Parkinson's disease using wearable sensors

Hssayeni

Burack

Ghoraani

2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

Motor fluctuations are a major focus of clinical managements in patients with mid-stage and advance Parkinson's disease (PD). In this paper, we develop a new patient-specific algorithm that can classify those fluctuations during a variety of activities. We extract a set of temporal and spectral features from the ambulatory signals and then introduce a semi-supervised classification algorithm based on K-means and self-organizing tree map clustering methods. Two different types of cluster labeling are introduced: hard and fuzzy labeling. The developed algorithm is evaluated on a dataset from triaxial gyroscope sensors for 12 PD patients. The average result of using K-means and fuzzy labeling on the trunk and the more affected leg sensors' readings was 75.96%, 70.57%, and 86.93% for accuracy, sensitivity, and specificity, respectively. The accuracy for individual patients varied from 99.95% to 42.53%, which was correlated with dyskinesia severity and the improvement of the PD symptoms with medication.

show abstract

Continuous Assessment of Levodopa Response in Parkinson's Disease Using Wearable Motion Sensors

Abstract: 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.

Cited by 99 publications

References 27 publications

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

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

Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device

Automatic assessment of medication states of patients with Parkinson's disease using wearable sensors

Contact Info

Product

Resources

About