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Etzkorn LH, Heravi AS, Knuth ND, Wu KC, Post WS, Urbanek JK, Crainiceanu CM. Classification of Free-Living Body Posture with ECG Patch Accelerometers: Application to the Multicenter AIDS Cohort Study. STATISTICS IN BIOSCIENCES 2024; 16:25-44. [PMID: 38715709 PMCID: PMC11073799 DOI: 10.1007/s12561-023-09377-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 05/11/2023] [Accepted: 05/30/2023] [Indexed: 05/12/2024]
Abstract
Purpose As health studies increasingly monitor free-living heart performance via ECG patches with accelerometers, researchers will seek to investigate cardio-electrical responses to physical activity and sedentary behavior, increasing demand for fast, scalable methods to process accelerometer data. We extend a posture classification algorithm for accelerometers in ECG patches when researchers do not have ground-truth labels or other reference measurements (i.e., upright measurement). Methods Men living with and without HIV in the Multicenter AIDS Cohort study wore the Zio XT® for up to two weeks (n = 1,250). Our novel extensions for posture classification include (1) estimation of an upright posture for each individual without a reference upright measurement; (2) correction of the upright estimate for device removal and re-positioning using novel spherical change-point detection; and (3) classification of upright and recumbent periods using a clustering and voting process rather than a simple inclination threshold used in other algorithms. As no posture labels exist in the free-living environment, we perform numerous sensitivity analyses and evaluate the algorithm against labelled data from the Towson Accelerometer Study, where participants wore accelerometers at the waist. Results On average, 87.1% of participants were recumbent at 4am and 15.5% were recumbent at 1pm. Participants were recumbent 54 minutes longer on weekends compared to weekdays. Performance was good in comparison to labelled data in a separate, controlled setting (accuracy = 96.0%, sensitivity = 97.5%, specificity = 95.9%). Conclusions Posture may be classified in the free-living environment from accelerometers in ECG patches even without measuring a standard upright position. Furthermore, algorithms that fail to account for individuals who rotate and re-attach the accelerometer may fail in the free-living environment.
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Affiliation(s)
| | | | | | | | | | - Jacek K. Urbanek
- School of Medicine, Johns Hopkins University
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Rd, Tarrytown NY 10591
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Beauchamp M, Kirkwood R, Cooper C, Brown M, Newbold KB, Scott D. Monitoring mobility in older adults using a Global Positioning System (GPS) smartwatch and accelerometer: A validation study. PLoS One 2023; 18:e0296159. [PMID: 38128015 PMCID: PMC10735177 DOI: 10.1371/journal.pone.0296159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023] Open
Abstract
There is growing interest in identifying valid and reliable methods for detecting early mobility limitations in aging populations. A multi-sensor approach that combines accelerometry with Global Positioning System (GPS) devices could provide valuable insights into late-life mobility decline; however, this innovative approach requires more investigation. We conducted a series of two experiments with 25 older participants (66.2±8.5 years) to determine the validity of a GPS enabled smartwatch (TicWatch S2 and Pro 3 Ultra GPS) and separate accelerometer (ActiGraph wGT3X-BT) to collect movement, navigation and body posture data relevant to mobility. In experiment 1, participants wore the TicWatchS2 and ActiGraph simultaneously on the wrist for 3 days. In experiment 2, participants wore the TicWatch Pro 2 Ultra GPS on the wrist and ActiGraph on the thigh for 3 days. In both experiments participants also carried a Qstarz data logger for trips outside the home. The TicWatch Pro 3 Ultra GPS performed better than the S2 model and was similar to the Qstarz in all tested trip-related measures, and it was able to estimate both passive and active trip modes. Both models showed similar results to the gold standard Qstarz in life-space-related measures. The TicWatch S2 demonstrated good to excellent overall agreement with the ActiGraph algorithms for the time spent in sedentary and non-sedentary activities, with 84% and 87% agreement rates, respectively. Under controlled conditions, the TicWatch Pro 3 Ultra GPS consistently measured step count in line with the participants' self-reported data, with a bias of 0.4 steps. The thigh-worn ActiGraph algorithm accurately classified sitting and lying postures (97%) and standing postures (90%). Our multi-sensor approach to monitoring mobility has the potential to capture both accelerometer-derived movement data and trip/life-space data only available through GPS. In this study, we found that the TicWatch models were valid devices for capturing GPS and raw accelerometer data, making them useful tools for assessing real-life mobility in older adults.
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Affiliation(s)
- Marla Beauchamp
- School of Rehabilitation Science, McMaster University, Hamilton, Ontario, Canada
| | - Renata Kirkwood
- School of Rehabilitation Science, McMaster University, Hamilton, Ontario, Canada
| | - Cody Cooper
- School of Rehabilitation Science, McMaster University, Hamilton, Ontario, Canada
| | - Matthew Brown
- School of Rehabilitation Science, McMaster University, Hamilton, Ontario, Canada
| | - K. Bruce Newbold
- School of Earth, Environment & Society, McMaster University, Hamilton, Ontario, Canada
| | - Darren Scott
- School of Earth, Environment & Society, McMaster University, Hamilton, Ontario, Canada
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Arguello D, Rogers E, Denmark GH, Lena J, Goodro T, Anderson-Song Q, Cloutier G, Hillman CH, Kramer AF, Castaneda-Sceppa C, John D. Companion: A Pilot Randomized Clinical Trial to Test an Integrated Two-Way Communication and Near-Real-Time Sensing System for Detecting and Modifying Daily Inactivity among Adults >60 Years-Design and Protocol. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23042221. [PMID: 36850822 PMCID: PMC9965440 DOI: 10.3390/s23042221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/31/2023] [Accepted: 02/10/2023] [Indexed: 05/14/2023]
Abstract
Supervised personal training is most effective in improving the health effects of exercise in older adults. Yet, low frequency (60 min, 1-3 sessions/week) of trainer contact limits influence on behavior change outside sessions. Strategies to extend the effect of trainer contact outside of supervision and that integrate meaningful and intelligent two-way communication to provide complex and interactive problem solving may motivate older adults to "move more and sit less" and sustain positive behaviors to further improve health. This paper describes the experimental protocol of a 16-week pilot RCT (N = 46) that tests the impact of supplementing supervised exercise (i.e., control) with a technology-based behavior-aware text-based virtual "Companion" that integrates a human-in-the-loop approach with wirelessly transmitted sensor-based activity measurement to deliver behavior change strategies using socially engaging, contextually salient, and tailored text message conversations in near-real-time. Primary outcomes are total-daily and patterns of habitual physical behaviors after 16 and 24 weeks. Exploratory analyses aim to understand Companion's longitudinal behavior effects, its user engagement and relationship to behavior, and changes in cardiometabolic and cognitive outcomes. Our findings may allow the development of a more scalable hybrid AI Companion to impact the ever-growing public health epidemic of sedentariness contributing to poor health outcomes, reduced quality of life, and early death.
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Affiliation(s)
- Diego Arguello
- Bouvé College of Health Sciences, Northeastern University, Boston, MA 02115, USA
- Correspondence:
| | - Ethan Rogers
- Bouvé College of Health Sciences, Northeastern University, Boston, MA 02115, USA
| | - Grant H. Denmark
- Philadelphia College of Osteopathic Medicine, Philadelphia, PA 19131, USA
| | - James Lena
- Manning College of Nursing and Health Sciences, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Troy Goodro
- Manning College of Nursing and Health Sciences, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Quinn Anderson-Song
- Bouvé College of Health Sciences, Northeastern University, Boston, MA 02115, USA
| | - Gregory Cloutier
- Bouvé College of Health Sciences, Northeastern University, Boston, MA 02115, USA
| | - Charles H. Hillman
- Bouvé College of Health Sciences, Northeastern University, Boston, MA 02115, USA
| | - Arthur F. Kramer
- College of Science, Northeastern University, Boston, MA 02115, USA
- Beckman Institute, University of Illinois, Urbana, IL 61801, USA
| | | | - Dinesh John
- Bouvé College of Health Sciences, Northeastern University, Boston, MA 02115, USA
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Thapa-Chhetry B, Jose Arguello D, John D, Intille S. Detecting Sleep and Nonwear in 24-h Wrist Accelerometer Data from the National Health and Nutrition Examination Survey. Med Sci Sports Exerc 2022; 54:1936-1946. [PMID: 36007161 PMCID: PMC9615811 DOI: 10.1249/mss.0000000000002973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
INTRODUCTION Estimating physical activity, sedentary behavior, and sleep from wrist-worn accelerometer data requires reliable detection of sensor nonwear and sensor wear during both sleep and wake. PURPOSE This study aimed to develop an algorithm that simultaneously identifies sensor wake-wear, sleep-wear, and nonwear in 24-h wrist accelerometer data collected with or without filtering. METHODS Using sensor data labeled with polysomnography ( n = 21) and directly observed wake-wear data ( n = 31) from healthy adults, and nonwear data from sensors left at various locations in a home ( n = 20), we developed an algorithm to detect nonwear, sleep-wear, and wake-wear for "idle sleep mode" (ISM) filtered data collected in the 2011-2014 National Health and Nutrition Examination Survey. The algorithm was then extended to process original raw data collected from devices without ISM filtering. Both algorithms were further validated using a polysomnography-based sleep and wake-wear data set ( n = 22) and diary-based wake-wear and nonwear labels from healthy adults ( n = 23). Classification performance (F1 scores) was compared with four alternative approaches. RESULTS The F1 score of the ISM-based algorithm on the training data set using leave-one-subject-out cross-validation was 0.95 ± 0.13. Validation on the two independent data sets yielded F1 scores of 0.84 ± 0.60 for the data set with sleep-wear and wake-wear and 0.94 ± 0.04 for the data set with wake-wear and nonwear. The F1 score when using original, raw data was 0.96 ± 0.08 for the training data sets and 0.86 ± 0.18 and 0.97 ± 0.04 for the two independent validation data sets. The algorithm performed comparably or better than the alternative approaches on the data sets. CONCLUSIONS A novel machine-learning algorithm was designed to recognize wake-wear, sleep-wear, and nonwear in 24-h wrist-worn accelerometer data that are applicable for ISM-filtered data or original raw data.
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Affiliation(s)
- Binod Thapa-Chhetry
- Bouvé College of Health Sciences, Northeastern University, Boston, MA
- Khoury College of Computer Sciences, Northeastern University, Boston, MA
| | | | - Dinesh John
- Khoury College of Computer Sciences, Northeastern University, Boston, MA
| | - Stephen Intille
- Bouvé College of Health Sciences, Northeastern University, Boston, MA
- Khoury College of Computer Sciences, Northeastern University, Boston, MA
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Wang S, Intille S, Ponnada A, Do B, Rothman A, Dunton G. Investigating Microtemporal Processes Underlying Health Behavior Adoption and Maintenance: Protocol for an Intensive Longitudinal Observational Study (Preprint). JMIR Res Protoc 2022; 11:e36666. [PMID: 35834296 PMCID: PMC9335174 DOI: 10.2196/36666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 06/07/2022] [Accepted: 06/08/2022] [Indexed: 11/13/2022] Open
Abstract
Background Objective Methods Results Conclusions International Registered Report Identifier (IRRID)
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Affiliation(s)
- Shirlene Wang
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Stephen Intille
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States
- Bouvé College of Health Sciences, Northeastern University, Boston, MA, United States
| | - Aditya Ponnada
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States
- Bouvé College of Health Sciences, Northeastern University, Boston, MA, United States
| | - Bridgette Do
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Alexander Rothman
- Department of Psychology, University of Minnesota, Minneapolis, MN, United States
| | - Genevieve Dunton
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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Ponnada A, Cooper S, Tang Q, Thapa-Chhetry B, Miller JA, John D, Intille S. Signaligner Pro: A Tool to Explore and Annotate Multi-day Raw Accelerometer Data. PROCEEDINGS OF THE ... IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS. IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS 2021; 2021:10.1109/percomworkshops51409.2021.9431110. [PMID: 34458663 PMCID: PMC8389719 DOI: 10.1109/percomworkshops51409.2021.9431110] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Human activity recognition using wearable accelerometers can enable in-situ detection of physical activities to support novel human-computer interfaces. Many of the machine-learning-based activity recognition algorithms require multi-person, multi-day, carefully annotated training data with precisely marked start and end times of the activities of interest. To date, there is a dearth of usable tools that enable researchers to conveniently visualize and annotate multiple days of high-sampling-rate raw accelerometer data. Thus, we developed Signaligner Pro, an interactive tool to enable researchers to conveniently explore and annotate multi-day high-sampling rate raw accelerometer data. The tool visualizes high-sampling-rate raw data and time-stamped annotations generated by existing activity recognition algorithms and human annotators; the annotations can then be directly modified by the researchers to create their own, improved, annotated datasets. In this paper, we describe the tool's features and implementation that facilitate convenient exploration and annotation of multi-day data and demonstrate its use in generating activity annotations.
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Affiliation(s)
| | | | - Qu Tang
- Northeastern University, Boston, MA, USA
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Ahmadi MN, O’Neil ME, Baque E, Boyd RN, Trost SG. Machine Learning to Quantify Physical Activity in Children with Cerebral Palsy: Comparison of Group, Group-Personalized, and Fully-Personalized Activity Classification Models. SENSORS 2020; 20:s20143976. [PMID: 32708963 PMCID: PMC7411900 DOI: 10.3390/s20143976] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 07/09/2020] [Accepted: 07/16/2020] [Indexed: 11/16/2022]
Abstract
Pattern recognition methodologies, such as those utilizing machine learning (ML) approaches, have the potential to improve the accuracy and versatility of accelerometer-based assessments of physical activity (PA). Children with cerebral palsy (CP) exhibit significant heterogeneity in relation to impairment and activity limitations; however, studies conducted to date have implemented “one-size fits all” group (G) models. Group-personalized (GP) models specific to the Gross Motor Function Classification (GMFCS) level and fully-personalized (FP) models trained on individual data may provide more accurate assessments of PA; however, these approaches have not been investigated in children with CP. In this study, 38 children classified at GMFCS I to III completed laboratory trials and a simulated free-living protocol while wearing an ActiGraph GT3X+ on the wrist, hip, and ankle. Activities were classified as sedentary, standing utilitarian movements, or walking. In the cross-validation, FP random forest classifiers (99.0–99.3%) exhibited a significantly higher accuracy than G (80.9–94.7%) and GP classifiers (78.7–94.1%), with the largest differential observed in children at GMFCS III. When evaluated under free-living conditions, all model types exhibited significant declines in accuracy, with FP models outperforming G and GP models in GMFCS levels I and II, but not III. Future studies should evaluate the comparative accuracy of personalized models trained on free-living accelerometer data.
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Affiliation(s)
- Matthew N. Ahmadi
- Institute of Health and Biomedical Innovation at Queensland Centre for Children’s Health Research, Queensland University of Technology, South Brisbane 4101, Australia; (M.N.A.); (E.B.)
- Faculty of Health, School of Exercise and Nutrition Sciences, Queensland University of Technology, Kelvin Grove 4059, Australia
| | - Margaret E. O’Neil
- Department of Rehabilitation and Regenerative Medicine, Columbia University Irving Medical Center, New York, NY 10032, USA;
| | - Emmah Baque
- Institute of Health and Biomedical Innovation at Queensland Centre for Children’s Health Research, Queensland University of Technology, South Brisbane 4101, Australia; (M.N.A.); (E.B.)
- School of Allied Health Sciences, Griffith University, Gold Coast 4215, Queensland, Australia
| | - Roslyn N. Boyd
- Queensland Cerebral Palsy and Rehabilitation Research Centre, UQ Child Health Research Centre, Faculty of Medicine, The University of Queensland, South Brisbane 4101, Australia;
| | - Stewart G. Trost
- Institute of Health and Biomedical Innovation at Queensland Centre for Children’s Health Research, Queensland University of Technology, South Brisbane 4101, Australia; (M.N.A.); (E.B.)
- Faculty of Health, School of Exercise and Nutrition Sciences, Queensland University of Technology, Kelvin Grove 4059, Australia
- Correspondence: ; Tel.: +61-7-3069-7301
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