Abstract
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.
Highlights
Parkinson’s disease (PD) is one of the most common neurodegenerative disorders affecting approximately 10 million people worldwide.[1,2,3] The loss of dopaminergic neurons in the substantia nigra region of the midbrain, which is critical for motor control, is a primary contributor to the pathophysiology of PD.[3,4,5] Tremor, bradykinesia, postural instability and rigidity are the cardinal motor symptoms of PD.[1]
A multi-modal system proposed by Roy et al.[28] uses accelerometer and surface EMG data recorded bradykinesia analysis because motor symptoms associated with gait are generally assessed separately.[41]
Data from the wrist-worn from four wearable devices placed on the limbs for monitoring device located on the most affected side spanning the duration tremor and dyskinesia during unconstrained activity
Summary
Parkinson’s disease (PD) is one of the most common neurodegenerative disorders affecting approximately 10 million people worldwide.[1,2,3] The loss of dopaminergic neurons in the substantia nigra region of the midbrain, which is critical for motor control, is a primary contributor to the pathophysiology of PD.[3,4,5] Tremor, bradykinesia (i.e., slowness of movement), postural instability and rigidity (i.e., stiffness and resistance to passive movement) are the cardinal motor symptoms of PD.[1]. The proposed method follows a hierarchical paradigm by first determining activity periods of interest (i.e., context) and applying context specific processing steps to detect the presence the challenges highlighted by the MDS Taskforce on Technology While these efforts have certainly advanced the field, there are still significant gaps that need to be addressed.[39] Several of these of motor symptoms and derive objective measures of their approaches rely on the use of multiple devices across different severity. Zwartjes et al.[29] proposed a
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