Abstract
BackgroundHand tremor typically has a negative impact on a person’s ability to complete many common daily activities. Previous research has investigated how to quantify hand tremor with smartphones and wearable sensors, mainly under controlled data collection conditions. Solutions for daily real-life settings remain largely underexplored.ObjectiveOur objective was to monitor and assess hand tremor severity in patients with Parkinson disease (PD), and to better understand the effects of PD medications in a naturalistic environment.MethodsUsing the Welch method, we generated periodograms of accelerometer data and computed signal features to compare patients with varying degrees of PD symptoms.ResultsWe introduced and empirically evaluated the tremor intensity parameter (TIP), an accelerometer-based metric to quantify hand tremor severity in PD using smartphones. There was a statistically significant correlation between the TIP and self-assessed Unified Parkinson Disease Rating Scale (UPDRS) II tremor scores (Kendall rank correlation test: z=30.521, P<.001, τ=0.5367379; n=11). An analysis of the “before” and “after” medication intake conditions identified a significant difference in accelerometer signal characteristics among participants with different levels of rigidity and bradykinesia (Wilcoxon rank sum test, P<.05).ConclusionsOur work demonstrates the potential use of smartphone inertial sensors as a systematic symptom severity assessment mechanism to monitor PD symptoms and to assess medication effectiveness remotely. Our smartphone-based monitoring app may also be relevant for other conditions where hand tremor is a prevalent symptom.
Highlights
BackgroundParkinson disease (PD) is a neurodegenerative condition that affects patients’ physical and mental health [1,2] and has a wide variety of motor and nonmotor symptoms
We analyzed the data set’s accelerometer and medication intake data to answer the following research questions: (1) how feasible is it to characterize hand tremor using inertial data captured during our smartphone game?, and (2) can the effects of PD medication be detected using the same inertial data captured during game sessions played before and after medication intake?
We study our two research questions using the power spectral densities (PSDs) features described in the previous section: (1) how feasible is it to characterize tremor using inertial data captured during our smartphone game?, and (2) can the effects of PD medication be detected using the same inertial data captured during game sessions played before and after medication?
Summary
BackgroundParkinson disease (PD) is a neurodegenerative condition that affects patients’ physical and mental health [1,2] and has a wide variety of motor and nonmotor symptoms. Tremor can be categorized based on its activation conditions into rest and action tremor; in turn, action tremor is further divided into kinetic, postural, or isometric subtypes [4]. Among patients with PD, approximately 75% suffer from rest tremor, around 50% from moderately severe postural tremor [7], and an undetermined percentage from kinetic tremor [6]. These three types of tremor are pivotal in understanding PD. Objective: Our objective was to monitor and assess hand tremor severity in patients with Parkinson disease (PD), and to better understand the effects of PD medications in a naturalistic environment. Our smartphone-based monitoring app may be relevant for other conditions where hand tremor is a prevalent symptom
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