NiMBaLWear analytics pipeline for wearable sensors: a modular, open-source platform for evaluating multiple domains of health and behaviour
Abstract Background Recent technological advances have led to a surge in the use of wearable devices for personal health and fitness monitoring; however, clinical uptake of wearable devices for remote or ‘free-living’ measurement of daily health-related behavior has lagged. To advance the field, the...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-02-01
|
| Series: | BMC Digital Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s44247-024-00062-3 |
| _version_ | 1797273203185287168 |
|---|---|
| author | Kit B. Beyer Kyle S. Weber Benjamin F. Cornish Adam Vert Vanessa Thai F. Elizabeth Godkin William E. McIlroy Karen Van Ooteghem |
| author_facet | Kit B. Beyer Kyle S. Weber Benjamin F. Cornish Adam Vert Vanessa Thai F. Elizabeth Godkin William E. McIlroy Karen Van Ooteghem |
| author_sort | Kit B. Beyer |
| collection | DOAJ |
| description | Abstract Background Recent technological advances have led to a surge in the use of wearable devices for personal health and fitness monitoring; however, clinical uptake of wearable devices for remote or ‘free-living’ measurement of daily health-related behavior has lagged. To advance the field, there is need for valid and reliable outcomes across multiple health domains specific to the cohorts or patients of interest and centralized tools to build capacity for use of these data. The NiMBaLWear pipeline provides a flexible and integrated approach to wearables analytics applied to raw sensor data that considers multiple, inter-related physiological and behavioral signals to provide a holistic view of health status. Results & discussion NiMBaLWear is a modular, open-source, wearable sensor analytic pipeline that quantifies physical activity, mobility, and sleep from raw single- or multi-sensor free-living data collected over multiple days. Data captured from any device, in different possible formats, are standardized prior to processing. Data preparation includes accelerometer autocalibration, cross-device synchronization, and non-wear detection. Validated, domain-specific algorithms detect events, generate outcome measures, and output standardized tabular data and user-friendly summary collection reports. NiMBaLWear was developed in Python using an iterative and incremental software development process, which included a combination of semi-automated inspection and expert review of data collected from 286 participants across two remote-measurement studies. A comparative analysis revealed a paucity of open-source packages capable of deriving and sharing health-related behavioral outcomes across multiple domains from multi-sensor wearables data. Forthcoming improvements to the pipeline will leverage sensor fusion techniques to add new, and refine existing, domain- and disease-specific analytics, and optimize pipeline accessibility and reporting. Conclusion The NiMBaLWear pipeline transforms raw multi-sensor wearables data into accurate and relevant outcomes across multiple health domains to objectively characterize and measure an individual’s daily health-related behavior. NiMBaLWear’s focus on high-quality, clinically relevant outcomes, as well as end-user optimization, provides a foundation for innovation to improve the utility of wearables for clinical care and self-management of health. |
| first_indexed | 2024-03-07T14:40:12Z |
| format | Article |
| id | doaj.art-dab908cbdada452abba23e7948c3d352 |
| institution | Directory Open Access Journal |
| issn | 2731-684X |
| language | English |
| last_indexed | 2024-03-07T14:40:12Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Digital Health |
| spelling | doaj.art-dab908cbdada452abba23e7948c3d3522024-03-05T20:26:02ZengBMCBMC Digital Health2731-684X2024-02-012111510.1186/s44247-024-00062-3NiMBaLWear analytics pipeline for wearable sensors: a modular, open-source platform for evaluating multiple domains of health and behaviourKit B. Beyer0Kyle S. Weber1Benjamin F. Cornish2Adam Vert3Vanessa Thai4F. Elizabeth Godkin5William E. McIlroy6Karen Van Ooteghem7Department of Kinesiology and Health Sciences, Faculty of Health, University of WaterlooDepartment of Kinesiology and Health Sciences, Faculty of Health, University of WaterlooDepartment of Kinesiology and Health Sciences, Faculty of Health, University of WaterlooDepartment of Kinesiology and Health Sciences, Faculty of Health, University of WaterlooDepartment of Kinesiology and Health Sciences, Faculty of Health, University of WaterlooDepartment of Kinesiology and Health Sciences, Faculty of Health, University of WaterlooDepartment of Kinesiology and Health Sciences, Faculty of Health, University of WaterlooDepartment of Kinesiology and Health Sciences, Faculty of Health, University of WaterlooAbstract Background Recent technological advances have led to a surge in the use of wearable devices for personal health and fitness monitoring; however, clinical uptake of wearable devices for remote or ‘free-living’ measurement of daily health-related behavior has lagged. To advance the field, there is need for valid and reliable outcomes across multiple health domains specific to the cohorts or patients of interest and centralized tools to build capacity for use of these data. The NiMBaLWear pipeline provides a flexible and integrated approach to wearables analytics applied to raw sensor data that considers multiple, inter-related physiological and behavioral signals to provide a holistic view of health status. Results & discussion NiMBaLWear is a modular, open-source, wearable sensor analytic pipeline that quantifies physical activity, mobility, and sleep from raw single- or multi-sensor free-living data collected over multiple days. Data captured from any device, in different possible formats, are standardized prior to processing. Data preparation includes accelerometer autocalibration, cross-device synchronization, and non-wear detection. Validated, domain-specific algorithms detect events, generate outcome measures, and output standardized tabular data and user-friendly summary collection reports. NiMBaLWear was developed in Python using an iterative and incremental software development process, which included a combination of semi-automated inspection and expert review of data collected from 286 participants across two remote-measurement studies. A comparative analysis revealed a paucity of open-source packages capable of deriving and sharing health-related behavioral outcomes across multiple domains from multi-sensor wearables data. Forthcoming improvements to the pipeline will leverage sensor fusion techniques to add new, and refine existing, domain- and disease-specific analytics, and optimize pipeline accessibility and reporting. Conclusion The NiMBaLWear pipeline transforms raw multi-sensor wearables data into accurate and relevant outcomes across multiple health domains to objectively characterize and measure an individual’s daily health-related behavior. NiMBaLWear’s focus on high-quality, clinically relevant outcomes, as well as end-user optimization, provides a foundation for innovation to improve the utility of wearables for clinical care and self-management of health.https://doi.org/10.1186/s44247-024-00062-3Wearable technologyRemote monitoringAnalyticsMulti-sensorOpen-sourceOlder adults |
| spellingShingle | Kit B. Beyer Kyle S. Weber Benjamin F. Cornish Adam Vert Vanessa Thai F. Elizabeth Godkin William E. McIlroy Karen Van Ooteghem NiMBaLWear analytics pipeline for wearable sensors: a modular, open-source platform for evaluating multiple domains of health and behaviour BMC Digital Health Wearable technology Remote monitoring Analytics Multi-sensor Open-source Older adults |
| title | NiMBaLWear analytics pipeline for wearable sensors: a modular, open-source platform for evaluating multiple domains of health and behaviour |
| title_full | NiMBaLWear analytics pipeline for wearable sensors: a modular, open-source platform for evaluating multiple domains of health and behaviour |
| title_fullStr | NiMBaLWear analytics pipeline for wearable sensors: a modular, open-source platform for evaluating multiple domains of health and behaviour |
| title_full_unstemmed | NiMBaLWear analytics pipeline for wearable sensors: a modular, open-source platform for evaluating multiple domains of health and behaviour |
| title_short | NiMBaLWear analytics pipeline for wearable sensors: a modular, open-source platform for evaluating multiple domains of health and behaviour |
| title_sort | nimbalwear analytics pipeline for wearable sensors a modular open source platform for evaluating multiple domains of health and behaviour |
| topic | Wearable technology Remote monitoring Analytics Multi-sensor Open-source Older adults |
| url | https://doi.org/10.1186/s44247-024-00062-3 |
| work_keys_str_mv | AT kitbbeyer nimbalwearanalyticspipelineforwearablesensorsamodularopensourceplatformforevaluatingmultipledomainsofhealthandbehaviour AT kylesweber nimbalwearanalyticspipelineforwearablesensorsamodularopensourceplatformforevaluatingmultipledomainsofhealthandbehaviour AT benjaminfcornish nimbalwearanalyticspipelineforwearablesensorsamodularopensourceplatformforevaluatingmultipledomainsofhealthandbehaviour AT adamvert nimbalwearanalyticspipelineforwearablesensorsamodularopensourceplatformforevaluatingmultipledomainsofhealthandbehaviour AT vanessathai nimbalwearanalyticspipelineforwearablesensorsamodularopensourceplatformforevaluatingmultipledomainsofhealthandbehaviour AT felizabethgodkin nimbalwearanalyticspipelineforwearablesensorsamodularopensourceplatformforevaluatingmultipledomainsofhealthandbehaviour AT williamemcilroy nimbalwearanalyticspipelineforwearablesensorsamodularopensourceplatformforevaluatingmultipledomainsofhealthandbehaviour AT karenvanooteghem nimbalwearanalyticspipelineforwearablesensorsamodularopensourceplatformforevaluatingmultipledomainsofhealthandbehaviour |