A jerk-based algorithm ACCEL for the accurate classification of sleep-wake states from arm acceleration.
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ABSTRACT:
SUBMITTER: Ode KL
PROVIDER: S-EPMC8784328 | biostudies-literature | 2022 Feb
REPOSITORIES: biostudies-literature
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A jerk-based algorithm ACCEL for the accurate classification of sleep-wake states from arm acceleration.
Ode Koji L KL Shi Shoi S Katori Machiko M Mitsui Kentaro K Takanashi Shin S Oguchi Ryo R Aoki Daisuke D Ueda Hiroki R HR
iScience 20220101 2
Arm acceleration data have been used to measure sleep-wake rhythmicity. Although several methods have been developed for the accurate classification of sleep-wake episodes, a method with both high sensitivity and specificity has not been fully established. In this study, we developed an algorithm, named ACceleration-based Classification and Estimation of Long-term sleep-wake cycles (ACCEL) that classifies sleep and wake episodes using only raw accelerometer data, without relying on device-specif ...[more]
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Project description:BACKGROUND:Accelerometers have become common for evaluating the efficacy of rehabilitation for patients with neurologic disorders. For example, metrics like use ratio (UR) and magnitude ratio (MR) have been shown to differentiate movement patterns of children with cerebral palsy (CP) compared to typically-developing (TD) peers. However, these metrics are calculated from "activity counts" - a measure based on proprietary algorithms that approximate movement duration and intensity from raw accelerometer data. Algorithms used to calculate activity counts vary between devices, limiting comparisons of clinical and research results. The goal of this research was to develop complementary metrics based on raw accelerometer data to analyze arm movement after neurologic injury. METHOD:We calculated jerk, the derivative of acceleration, to evaluate arm movement from accelerometer data. To complement current measures, we calculated jerk ratio (JR) as the relative jerk magnitude of the dominant (non-paretic) and non-dominant (paretic) arms. We evaluated the JR distribution between arms and calculated the 50th percentile of the JR distribution (JR50). To evaluate these metrics, we analyzed bimanual accelerometry data for five children with hemiplegic CP who underwent Constraint-Induced Movement Therapy (CIMT) and five typically developing (TD) children. We compared JR between the CP and TD cohorts, and to activity count metrics. RESULTS:The JR50 differentiated between the CP and TD cohorts (CP?=?0.578?±?0.041 before CIMT, TD?=?0.506?±?0.026), demonstrating increased reliance on the dominant arm for the CP cohort. Jerk metrics also quantified changes in arm use during and after therapy (e.g., JR50?=?0.378?±?0.125 during CIMT, 0.591?±?0.057 after CIMT). The JR was strongly correlated with UR and MR (r =?-?0.92, 0.89) for the CP cohort. For the TD cohort, JR50 was repeatable across three data collection periods with an average similarity of 0.945?±?0.015. CONCLUSIONS:Acceleration-derived jerk captured differences in motion between TD and CP cohorts and correlated with activity count metrics. The code for calculating and plotting JR is open-source and available for others to use and build upon. By identifying device-independent metrics that can quantify arm movement in daily life, we hope to facilitate collaboration for rehabilitation research using wearable technologies.
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