Remote at-home wearable-based gait assessments in Progressive Supranuclear Palsy compared to Parkinson’s Disease

Sharma, Mansi; Mishra, Ram kinker; Hall, Anna J.; Casado, Jose; Cole, Rylee; Nunes, Adonay S.; Barchard, Gregory; Vaziri, Ashkan; Pantelyat, Alexander; Wills, Anne-Marie

doi:10.1186/s12883-023-03466-2

Cited by 1 publication

(1 citation statement)

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“…In this context, frequent at-home monitoring emerges as a critical tool for tracking disease progression and assessing treatment efficacy. Automated remote monitoring offers several advantages over traditional clinic-based assessments, including increased frequency, reduced subjectivity, and the ability to capture subtle changes in motor function ( Andrzejewski et al, 2016 ; Bennasar et al, 2018 ; Dorsey et al, 2017 ; Ó Breasail et al, 2021 ; Sharma et al, 2023 ). In addition, remote monitoring technologies have the potential to reduce the burden of clinical care and research by moving assessments from the clinic to the home, potentially expanding access for diverse patient populations.…”

Section: Introductionmentioning

confidence: 99%

Using Wearable Sensors and Machine Learning to Assess Upper Limb Function in Huntington's Disease

Vaziri,

Nunes,

Potter

et al. 2024

Preprint

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Huntington's disease (HD), like many other neurological disorders, affects both lower and upper limb function that is typically assessed in the clinic - providing a snapshot of disease symptoms. Wearable sensors enable the collection of real-world data that can complement such clinical assessments and provide a more comprehensive insight into disease symptoms. In this context, almost all studies are focused on assessing lower limb function via monitoring of gait, physical activity and ambulation. In this study, we monitor upper limb function during activities of daily living in individuals with HD (n = 16), prodromal HD (pHD, n = 7), and controls (CTR, n = 16) using a wrist-worn wearable sensor, called PAMSys ULM, over seven days. The participants were highly compliant in wearing the sensor with an average daily compliance of 99% (100% for HD, 98% for pHD, and 99% for CTR). Goal-directed movements (GDM) of the hand were detected using a deep learning model, and kinematic features of each GDM were estimated. The collected data was used to predict disease groups (i.e., HD, pHD, and CTR) and clinical scores using a combination of statistical and machine learning-based models. Significant differences in GDM features were observed between the groups. HD participants performed fewer GDMs with long duration (> 7.5 seconds) compared to CTR (p-val = 0.021, d = -0.86). In velocity and acceleration metrics, the highest effect size feature was the entropy of the velocity zero-crossing length segments (HD vs CTR p-val <0.001, d = -1.67; HD vs pHD p-val = 0.043, d=-0.98; CTR vs pHD p-val = 0.046, d=0.96). In addition, this same variable showed a strongest correlation with clinical scores. Classification models achieved good performance in distinguishing HD, pHD and CTR individuals with a balanced accuracy of 67% and a 0.72 recall for the HD group, while regression models accurately predicted clinical scores. Notably the explained variance for the upper extremity function subdomain scale of Unified Huntington’s Disease Rating Scale (UHDRS) was the highest, with the model capturing 60% of the variance. Our findings suggest the potential of wearables and machine learning for early identification of phenoconversion, remote monitoring in HD, and evaluating new treatments efficacy in clinical trials and medicine.

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