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Lendt C, Hettiarachchi P, Johansson PJ, Duncan S, Lund Rasmussen C, Narayanan A, Stewart T. Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of ActiPASS in School-Aged Children. J Phys Act Health 2024:1-8. [PMID: 39159934 DOI: 10.1123/jpah.2024-0259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 06/14/2024] [Accepted: 06/29/2024] [Indexed: 08/21/2024]
Abstract
BACKGROUND The ActiPASS software was developed from the open-source Acti4 activity classification algorithm for thigh-worn accelerometry. However, the original algorithm has not been validated in children or compared with a child-specific set of algorithm thresholds. This study aims to evaluate the accuracy of ActiPASS in classifying activity types in children using 2 published sets of Acti4 thresholds. METHODS Laboratory and free-living data from 2 previous studies were used. The laboratory condition included 41 school-aged children (11.0 [4.8] y; 46.5% male), and the free-living condition included 15 children (10.0 [2.6] y; 66.6% male). Participants wore a single accelerometer on the dominant thigh, and annotated video recordings were used as a reference. Postures and activity types were classified with ActiPASS using the original adult thresholds and a child-specific set of thresholds. RESULTS Using the original adult thresholds, the mean balanced accuracy (95% CI) for the laboratory condition ranged from 0.62 (0.56-0.67) for lying to 0.97 (0.94-0.99) for running. For the free-living condition, accuracy ranged from 0.61 (0.48-0.75) for lying to 0.96 (0.92-0.99) for cycling. Mean balanced accuracy for overall sedentary behavior (sitting and lying) was ≥0.97 (0.95-0.99) across all thresholds and conditions. No meaningful differences were found between the 2 sets of thresholds, except for superior balanced accuracy of the adult thresholds for walking under laboratory conditions. CONCLUSIONS The results indicate that ActiPASS can accurately classify different basic types of physical activity and sedentary behavior in children using thigh-worn accelerometer data.
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Affiliation(s)
- Claas Lendt
- Human Potential Centre, School of Sport and Recreation, Auckland University of Technology, Auckland, New Zealand
- Institute for Movement Therapy and Movement-Oriented Prevention and Rehabilitation, German Sport University Cologne, Cologne, Germany
| | - Pasan Hettiarachchi
- Occupational and Environmental Medicine, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Peter J Johansson
- Occupational and Environmental Medicine, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- Occupational and Environmental Medicine, Uppsala University Hospital, Uppsala, Sweden
| | - Scott Duncan
- Human Potential Centre, School of Sport and Recreation, Auckland University of Technology, Auckland, New Zealand
| | | | - Anantha Narayanan
- Human Potential Centre, School of Sport and Recreation, Auckland University of Technology, Auckland, New Zealand
| | - Tom Stewart
- Human Potential Centre, School of Sport and Recreation, Auckland University of Technology, Auckland, New Zealand
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Islam B, McElwain NL, Li J, Davila MI, Hu Y, Hu K, Bodway JM, Dhekne A, Roy Choudhury R, Hasegawa-Johnson M. Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations. SENSORS (BASEL, SWITZERLAND) 2024; 24:901. [PMID: 38339617 PMCID: PMC10857055 DOI: 10.3390/s24030901] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 01/19/2024] [Accepted: 01/19/2024] [Indexed: 02/12/2024]
Abstract
Across five studies, we present the preliminary technical validation of an infant-wearable platform, LittleBeats™, that integrates electrocardiogram (ECG), inertial measurement unit (IMU), and audio sensors. Each sensor modality is validated against data from gold-standard equipment using established algorithms and laboratory tasks. Interbeat interval (IBI) data obtained from the LittleBeats™ ECG sensor indicate acceptable mean absolute percent error rates for both adults (Study 1, N = 16) and infants (Study 2, N = 5) across low- and high-challenge sessions and expected patterns of change in respiratory sinus arrythmia (RSA). For automated activity recognition (upright vs. walk vs. glide vs. squat) using accelerometer data from the LittleBeats™ IMU (Study 3, N = 12 adults), performance was good to excellent, with smartphone (industry standard) data outperforming LittleBeats™ by less than 4 percentage points. Speech emotion recognition (Study 4, N = 8 adults) applied to LittleBeats™ versus smartphone audio data indicated a comparable performance, with no significant difference in error rates. On an automatic speech recognition task (Study 5, N = 12 adults), the best performing algorithm yielded relatively low word error rates, although LittleBeats™ (4.16%) versus smartphone (2.73%) error rates were somewhat higher. Together, these validation studies indicate that LittleBeats™ sensors yield a data quality that is largely comparable to those obtained from gold-standard devices and established protocols used in prior research.
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Affiliation(s)
- Bashima Islam
- Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA
| | - Nancy L. McElwain
- Department of Human Development and Family Studies, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; (Y.H.); (K.H.); (J.M.B.)
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Jialu Li
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; (J.L.); (R.R.C.)
| | - Maria I. Davila
- Research Triangle Institute, Research Triangle Park, NC 27709, USA;
| | - Yannan Hu
- Department of Human Development and Family Studies, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; (Y.H.); (K.H.); (J.M.B.)
| | - Kexin Hu
- Department of Human Development and Family Studies, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; (Y.H.); (K.H.); (J.M.B.)
| | - Jordan M. Bodway
- Department of Human Development and Family Studies, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; (Y.H.); (K.H.); (J.M.B.)
| | - Ashutosh Dhekne
- School of Computer Science, Georgia Institute of Technology, Atlanta, GA 30332, USA;
| | - Romit Roy Choudhury
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; (J.L.); (R.R.C.)
| | - Mark Hasegawa-Johnson
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; (J.L.); (R.R.C.)
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