scholarly journals DPSleep: Open-Source Longitudinal Sleep Analysis From Accelerometer Data (Preprint)

10.2196/29849 ◽  
2021 ◽  
Author(s):  
Habiballah Rahimi-Eichi ◽  
Garth Coombs 3rd ◽  
Constanza M. Vidal Bustamante ◽  
Jukka-Pekka Onnela ◽  
Justin T. Baker ◽  
...  
Keyword(s):  
Open Source ◽  
Accelerometer Data ◽  
Sleep Analysis

DPSleep: Open-Source Longitudinal Sleep Analysis From Accelerometer Data (Preprint)

2021 ◽  
Author(s):  
Habiballah Rahimi-Eichi ◽  
Garth Coombs 3rd ◽  
Constanza M. Vidal Bustamante ◽  
Jukka-Pekka Onnela ◽  
Justin T. Baker ◽  
...  
Keyword(s):  
Open Source ◽  
Self Report ◽  
Activity Levels ◽  
Accelerometer Data ◽  
Time Zone ◽  
Sleep Episode ◽  
Sleep Timing ◽  
Sleep Analysis ◽  
Smartphone Use ◽  
Deep Phenotyping

UNSTRUCTURED Wearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep. Here we introduce an open-source pipeline for the deep phenotyping of sleep, "DPSleep", that uses algorithms to detect missing data, calculate activity levels, and finally estimate the major Sleep Episode onset and offset. The pipeline allows for manual quality control adjustment and correction for time zone changes. We illustrate the utility of the pipeline with data from participants studied for more than 200 days. Actigraphy-based measures of sleep duration are associated with self-report rating of sleep quality. Simultaneous measures of smartphone use and GPS data support the sleep timing inferences and reveal how phone measures of sleep can differ from actigraphy data. We discuss the uses of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep dynamic longitudinal phenotyping associated with mental illness.

Converting Raw Accelerometer Data to Activity Counts Using Open-Source Code: Implementing a MATLAB Code in Python and R, and Comparing the Results to ActiLife

2021 ◽  
pp. 1-7
Author(s):  
Ruben Brondeel ◽  
Yan Kestens ◽  
Javad Rahimipour Anaraki ◽  
Kevin Stanley ◽  
Benoit Thierry ◽  
...  
Keyword(s):  
Open Source ◽  
Pearson Correlation ◽  
Vertical Axis ◽  
Accelerometer Data ◽  
Accelerometer Sensor ◽  
Interclass Correlation ◽  
Acceleration Data ◽  
The Right ◽  
Pearson Correlations ◽  
Closed Source

Background: Closed-source software for processing and analyzing accelerometer data provides little to no information about the algorithms used to transform acceleration data into physical activity indicators. Recently, an algorithm was developed in MATLAB that replicates the frequently used proprietary ActiLife activity counts. The aim of this software profile was (a) to translate the MATLAB algorithm into R and Python and (b) to test the accuracy of the algorithm on free-living data. Methods: As part of the INTErventions, Research, and Action in Cities Team, data were collected from 86 participants in Victoria (Canada). The participants were asked to wear an integrated global positioning system and accelerometer sensor (SenseDoc) for 10 days on the right hip. Raw accelerometer data were processed in ActiLife, MATLAB, R, and Python and compared using Pearson correlation, interclass correlation, and visual inspection. Results: Data were collected for a combined 749 valid days (>10 hr wear time). MATLAB, Python, and R counts per minute on the vertical axis had Pearson correlations with the ActiLife counts per minute of .998, .998, and .999, respectively. All three algorithms overestimated ActiLife counts per minute, some by up to 2.8%. Conclusions: A MATLAB algorithm for deriving ActiLife counts was implemented in R and Python. The different implementations provide similar results to ActiLife counts produced in the closed source software and can, for all practical purposes, be used interchangeably. This opens up possibilities to comparing studies using similar accelerometers from different suppliers, and to using free, open-source software.

scholarly journals SleepPy: A python package for sleep analysis from accelerometer data

2019 ◽  
Vol 4 (44) ◽  
pp. 1663
Author(s):  
Yiorgos Christakis ◽  
Nikhil Mahadevan ◽  
Shyamal Patel
Keyword(s):  
Accelerometer Data ◽  
Sleep Analysis ◽  
Python Package

scholarly journals P161 Regularity of sleep-wake patterns in the UK Biobank (N = 86 624) and an open-source tool to calculate the Sleep Regularity Index

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A73-A74
Author(s):  
D Windred ◽  
A Russell ◽  
A Burns ◽  
S Cain ◽  
A Phillips
Keyword(s):  
Mental Health ◽  
Open Source ◽  
Sleep Onset ◽  
R Package ◽  
Sleep Diary ◽  
Accelerometer Data ◽  
Uk Biobank ◽  
Physical And Mental Health ◽  
Regularity Index ◽  
Wake Patterns

Abstract Introduction Regular sleep-wake patterns aid in the maintenance of optimal physical and mental health, by helping to align environmental, behavioural, and physiological rhythms. The distribution of sleep regularity across the population has not been well documented. Furthermore, researchers currently lack tools to easily quantify sleep regularity. Method We have described sleep regularity in 86 624 UK Biobank participants (age (M±SD) = 62.45±7.84; 56.2% female) using data from wrist-worn accelerometers. Regularity was measured using the Sleep Regularity Index (SRI), which quantifies day-to-day similarity in sleep-wake patterns, and which is linked to cardio-metabolic and mental health outcomes. We developed an R package to calculate SRI from accelerometer data, which works in conjunction with GGIR (a validated accelerometer processing tool) to identify sleep-wake state, including naps and broken sleep. Results The SRI distribution had M±SD = 78.02±11.53, and median = 80.49. The least regular quintile (SRI<70.2) had standard deviation of sleep onset = 2.23h, offset = 2.14h, and duration = 1.95h, compared with onset = 0.78h, offset = 0.85h, and duration = 0.95h in the most regular quintile (SRI>87.3). Approximately 14% of participants exhibited large day-to-day shifts in sleep timing (>3h) at least once per week. Discussion This is the largest description of sleep regularity to-date. The norms established here provide a reference for researchers and clinicians intending to quantify sleep regularity with the SRI. We have combined methods described here into an open-source R package to calculate SRI from accelerometer or sleep diary data, available for download via GitHub.

scholarly journals An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing

2019 ◽  
Vol 2 (4) ◽  
pp. 268-281 ◽  
Author(s):  
Dinesh John ◽  
Qu Tang ◽  
Fahd Albinali ◽  
Stephen Intille
Keyword(s):  
Open Source ◽  
Dynamic Range ◽  
Sampling Rate ◽  
Human Motion ◽  
Motion Sensors ◽  
Accelerometer Data ◽  
Sedentary Behaviors ◽  
Independent Manner ◽  
Raw Data ◽  
Physical Behavior

Background: Physical behavior researchers using motion sensors often use acceleration summaries to visualize, clean, and interpret data. Such output is dependent on device specifications (e.g., dynamic range, sampling rate) and/or are proprietary, which invalidate cross-study comparison of findings when using different devices. This limits flexibility in selecting devices to measure physical activity, sedentary behavior, and sleep. Purpose: Develop an open-source, universal acceleration summary metric that accounts for discrepancies in raw data among research and consumer devices. Methods: We used signal processing techniques to generate a Monitor-Independent Movement Summary unit (MIMS-unit) optimized to capture normal human motion. Methodological steps included raw signal harmonization to eliminate inter-device variability (e.g., dynamic g-range, sampling rate), bandpass filtering (0.2–5.0 Hz) to eliminate non-human movement, and signal aggregation to reduce data to simplify visualization and summarization. We examined the consistency of MIMS-units using orbital shaker testing on eight accelerometers with varying dynamic range (±2 to ±8 g) and sampling rates (20–100 Hz), and human data (N = 60) from an ActiGraph GT9X. Results: During shaker testing, MIMS-units yielded lower between-device coefficient of variations than proprietary ActiGraph and ENMO acceleration summaries. Unlike the widely used ActiGraph activity counts, MIMS-units were sensitive in detecting subtle wrist movements during sedentary behaviors. Conclusions: Open-source MIMS-units may provide a means to summarize high-resolution raw data in a device-independent manner, thereby increasing standardization of data cleaning and analytical procedures to estimate selected attributes of physical behavior across studies.

CARL: a running recognition algorithm for free-living accelerometer data

2021 ◽  
Author(s):  
John J Davis IV ◽  
Marcin Straczkiewicz ◽  
Jaroslaw Harezlak ◽  
Allison H Gruber
Keyword(s):  
Physical Activity ◽  
Confidence Interval ◽  
Open Source ◽  
Recognition Algorithm ◽  
Physical Activities ◽  
Great Promise ◽  
Accelerometer Data ◽  
Free Living ◽  
Acceleration Data ◽  
Wearable Accelerometers

Abstract Wearable accelerometers hold great promise for physical activity epidemiology and sports biomechanists. However, identifying and extracting data from specific physical activities, such as running, remains challenging. Objective: To develop and validate an algorithm to identify bouts of running in raw, free-living accelerometer data from devices worn at the wrist or torso (waist, hip, chest). Approach: The CARL (continuous amplitude running logistic) classifier identifies acceleration data with amplitude and frequency characteristics consistent with running. The CARL classifier was trained on data from 31 adults wearing accelerometers on the waist and wrist, then validated on free-living data from 30 new, unseen subjects plus 166 subjects from previously-published datasets using different devices, wear locations, and sample frequencies. Main Results: On free-living data, the CARL classifier achieved mean accuracy (F1 score) of 0.984 (95% confidence interval 0.962-0.996) for data from the waist and 0.994 (95% CI 0.991-0.996) for data from the wrist. In previously-published datasets, the CARL classifier identified running with mean accuracy (F1 score) of 0.861 (95% CI 0.836-0.884) for data from the chest, 0.911 (95% CI 0.884-0.937) for data from the hip, 0.916 (95% CI 0.877-0.948) for data from the waist, and 0.870 (95% CI 0.834-0.903) for data from the wrist. Misclassification primarily occurred during activities with similar torso acceleration profiles to running, such as rope jumping and elliptical machine use. Significance: The CARL classifier can accurately identify bouts of running as short as three seconds in free-living accelerometry data. An open-source implementation of the CARL classifier is available at <<GITHUBURL>>.

261 Accelerometer-Derived Sleep and Circadian Domains and Sociodemographic Correlates in the UK Biobank

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A105-A105
Author(s):  
Philip Gehrman ◽  
Susan Malone ◽  
Freda Patterson ◽  
Jonathan Mitchell ◽  
Diego Mazzotti
Keyword(s):  
Open Source ◽  
Linear Models ◽  
Activity Patterns ◽  
Population Based ◽  
Accelerometer Data ◽  
Uk Biobank ◽  
Sleep Health ◽  
Variable Clustering ◽  
Clinically Significant ◽  
The Uk

Abstract Introduction Sleep health encompasses sleep regularity, duration, timing, efficiency and satisfaction. Accelerometry is an established method to estimate sleep in ecologically valid contexts, capturing key characteristic of rest-activity patterns, and facilitating population sleep health research. While hundreds of traits can be generated from open-source algorithms applied to raw acceleration data, the lack of clarity around their meaningful use beyond conventional measures limit the ability of these data to systematically inform evidence-based practices promoting sleep health. Here, we propose a method to identify key sleep and circadian domains, using data reduction methods for hundreds of accelerometer-derived traits to inform population-based sleep heath research. We also aimed to validate our findings by assessing whether the identified domains captured known sociodemographic associations. Methods We analyzed sociodemographic and raw triaxial accelerometer data recorded for 7 days from 79,876 adults (mean age 56.3±2.1 years, 56.3% women) participating in the UK Biobank. Standardized data processing using the open-source package GGIR (v1.7-1) resulted in the generation of 107 sleep and circadian traits. Variable clustering was used to identify key sleep and circadian domains, pertinent to sleep health, representing interpretable biological constructs minimizing correlation with other domains. Associations between identified domains and sociodemographic factors were evaluated using general linear models, and clinically significant differences were determined by standardized mean differences (SMD) ≥0.3. Results We identified 25 sleep and circadian domains explaining ≥80% of the variability of all 107 included traits. Domains capturing measures of variability tended to cluster together. The most clinically significant associations with sociodemographic characteristics were: women (vs. men) had higher sleep efficiency and lower accumulation of diurnal sleep periods; older (vs. younger) individuals had earlier most active starting time, lower acceleration amplitude and lower number of nocturnal sleep periods; and shift (vs. non-shift) workers had higher variability in sleep timing on weekends. Conclusion We demonstrate that variable clustering on accelerometer-derived data can identify meaningful sleep and circadian domains. In addition, identified domains captured known sociodemographic associations commonly observed in the sleep and circadian literature, suggesting that they could be relevant to inform public health practices that promote sleep health. Support (if any) NHLBI 5R01HL143790-02(PG); NIMHHD R01MD012734(FP); NIDA R01DA051321(FP); NIH/NHLBI K01HL123612(JM)

scholarly journals GGIR: A Research Community–Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes From Multi-Day Raw Accelerometer Data

2019 ◽  
Vol 2 (3) ◽  
pp. 188-196 ◽  
Author(s):  
Jairo H. Migueles ◽  
Alex V. Rowlands ◽  
Florian Huber ◽  
Séverine Sabia ◽  
Vincent T. van Hees
Keyword(s):  
Physical Activity ◽  
Open Source ◽  
R Package ◽  
Research Community ◽  
Accelerometer Data ◽  
Scientific Publications ◽  
Research Areas ◽  
Technological Advances ◽  
Sleep Parameters ◽  
One Stop

Recent technological advances have transformed the research on physical activity initially based on questionnaire data to the most recent objective data from accelerometers. The shift to availability of raw accelerations has increased measurement accuracy, transparency, and the potential for data harmonization. However, it has also shifted the need for considerable processing expertise to the researcher. Many users do not have this expertise. The R package GGIR has been made available to all as a tool to convermulti-day high resolution raw accelerometer data from wearable movement sensors into meaningful evidence-based outcomes and insightful reports for the study of human daily physical activity and sleep. This paper aims to provide a one-stop overview of GGIR package, the papers underpinning the theory of GGIR, and how research contributes to the continued growth of the GGIR package. The package includes a range of literature-supported methods to clean the data and provide day-by-day, as well as full recording, weekly, weekend, and weekday estimates of physical activity and sleep parameters. In addition, the package also comes with a shell function that enables the user to process a set of input files and produce csv summary reports with a single function call, ideal for users less proficient in R. GGIR has been used in over 90 peer-reviewed scientific publications to date. The evolution of GGIR over time and widespread use across a range of research areas highlights the importance of open source software development for the research community and advancing methods in physical behavior research.

Open Source

2007 ◽  
Author(s):  
Fadi P. Deek ◽  
James A. M. McHugh
Keyword(s):  
Open Source
Sign in / Sign up

Export Citation Format

Share Document