Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition
Open accessAbstract
The workforce shortage is one of the significant problems in the construction industry. To overcome the challenges due to workforce shortage, various researchers have proposed wearable sensor-based systems in the area of construction safety and health. Although sensors provide rich and detailed information, not all sensors can be used for construction applications. This study evaluates the data quality and reliability of forearm electromyography (EMG) and inertial measurement unit (IMU) of armband sensors for construction activity classification. To achieve the proposed objective, the forearm EMG and IMU data collected from eight participants while performing construction activities such as screwing, wrenching, lifting, and carrying on two different days were used to analyze the data quality and reliability for activity recognition through seven different experiments. The results of these experiments show that the armband sensor data quality is comparable to the conventional EMG and IMU sensors with excellent relative and absolute reliability between trials for all the five activities. The activity classification results were highly reliable, with minimal change in classification accuracies for both the days. Moreover, the results conclude that the combined EMG and IMU models classify activities with higher accuracies compared to individual sensor models.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The quality of data can be expressed in many different ways, and data quality measures may also depend on the aims of the related analysis and project.
“the quality of data can be expressed in many different ways, and data quality measures may also depend on the aims of the related analysis and project8–13”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [10]
Signal quality assessments have been done under more general circumstances, but wearable data quality in the context of epilepsy has not been a widely researched topic so far.
“Signal quality assessments have been done under more general circumstances10,34–37, but wearable data quality in the context of epilepsy has not been a widely researched topic so far8,9,38.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [10]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A guideline of selecting and reporting intraclass correlation coefficients for reliability research
Koo, T. K., & Li, M. Y. (2016) · Journal of Chiropractic Medicine · doi:10.1016/j.jcm.2016.02.012 · Cited by 29,639 · see citing works
Forming inferences about some intraclass correlation coefficients.
McGraw, K. O., & Wong, S. P. (1996) · Psychological Methods · doi:10.1037/1082-989x.1.1.30 · Cited by 6,903 · see citing works
Measures of Reliability in Sports Medicine and Science
Hopkins, W. G. (2000) · Sports Medicine · doi:10.2165/00007256-200030010-00001 · Cited by 4,635 · see citing works
Activity recognition using cell phone accelerometers
Kwapisz, J. R., Weiss, G. M., & Moore, S. A. (2011) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1964897.1964918 · Cited by 2,912 · see citing works
Reliability: what is it, and how is it measured?
Bruton, A., Conway, J. H., & Holgate, S. T. (2000) · Physiotherapy · doi:10.1016/s0031-9406(05)61211-4 · Cited by 763 · cited 1× · see citing works
“The reliability was assessed between the trials and between the days using test-retest reliability, which consists of relative and absolute reliability [31,53].”
MethodA Study on Human Activity Recognition Using Accelerometer Data from Smartphones
Bayat, A., Pomplun, M., & Tran, D. A. (2014) · Procedia Computer Science · doi:10.1016/j.procs.2014.07.009 · Cited by 531 · see citing works
Awolusi, I., Marks, E., & Hallowell, M. R. (2017) · Automation in Construction · doi:10.1016/j.autcon.2017.10.010 · Cited by 499 · see citing works
Bujang, M. A., & Baharum, N. (2017) · Archives of Orofacial Sciences · Cited by 456 · see citing works
Monitoring fatigue in construction workers using physiological measurements
Aryal, A., Ghahramani, A., & Becerik-Gerber, B. (2017) · Automation in Construction · doi:10.1016/j.autcon.2017.03.003 · Cited by 404 · see citing works
Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors
Phinyomark, A., Khushaba, R. N., & Scheme, E. (2018) · Sensors · doi:10.3390/s18051615 · Cited by 365 · see citing works
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2020) · arXiv · doi:10.48550/arxiv.2001.07416 · Cited by 314 · see citing works
Wearable Sensing Technology Applications in Construction Safety and Health
Ahn, C. R., Lee, S. H., Sun, C., Jebelli, H., Yang, K., & Choi, B. (2019) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001708 · Cited by 290 · see citing works
Ergonomic analysis of construction worker's body postures using wearable mobile sensors
Nath, N. D., Akhavian, R., & Behzadan, A. H. (2017) · Applied Ergonomics · doi:10.1016/j.apergo.2017.02.007 · Cited by 238 · see citing works
Measuring Workers’ Emotional State during Construction Tasks Using Wearable EEG
Hwang, S., Jebelli, H., Choi, B., Choi, M., & Lee, S. H. (2018) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001506 · Cited by 223 · see citing works
A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition
Janidarmian, M., Roshan Fekr, A., Radecka, K., & Zilic, Z. (2017) · Sensors · doi:10.3390/s17030529 · Cited by 217 · see citing works
Dynamic Modeling for Analyzing Impacts of Skilled Labor Shortage on Construction Project Management
Kim, S., Chang, S., & Castro‐Lacouture, D. (2019) · Journal of Management in Engineering · doi:10.1061/(asce)me.1943-5479.0000720 · Cited by 195 · see citing works
Automated Action Recognition Using an Accelerometer-Embedded Wristband-Type Activity Tracker
Ryu, J., Seo, J., Jebelli, H., & Lee, S. H. (2018) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001579 · Cited by 156 · see citing works
Tao, W., Lai, Z.-H., Leu, M. C., & Yin, Z. (2018) · Procedia Manufacturing · doi:10.1016/j.promfg.2018.07.152 · Cited by 145 · see citing works
Valero, E., Sivanathan, A., Bosché, F., et al. (2017) · Automation in Construction · doi:10.1016/j.autcon.2017.08.001 · Cited by 130 · see citing works
Düking, P., Fuss, F. K., Holmberg, H., & Sperlich, B. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.9341 · Cited by 128 · see citing works
Real-time hand gesture recognition using the Myo armband and muscle activity detection
Benalcázar, M. E., Motoche, C., Zea, J. A., Jaramillo, A. G., Anchundia, C. E., Zambrano, P., Segura, M., Palácios, F., & Pérez, M. (2017) · IEEE Ecuador Technical Chapters Meeting (ETCM) · doi:10.1109/etcm.2017.8247458 · Cited by 124 · see citing works
Visconti, P., Gaetani, F., Zappatore, G. A., & Primiceri, P. (2018) · International Journal on Smart Sensing and Intelligent Systems · doi:10.21307/ijssis-2018-005 · Cited by 122 · see citing works
Evaluation of the Myo armband for the classification of hand motions
Guerra, I. M., Hansen, B. W., Grabow, C., Smedegaard, E. J. L., Skogberg, N. B., Uth, X. J., Bruhn, A., Geng, B., & Kamavuako, E. N. (2017) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr.2017.8009414 · Cited by 83 · see citing works
Mumyo - Evaluating And Exploring The Myo Armband For Musical Interaction
Nymoen, K., Haugen, M. R., & Jensenius, A. R. (2015) · NORA - Norwegian Open Research Archives · doi:10.5281/zenodo.1179149 · Cited by 79 · see citing works
IoT-based safety monitoring from the perspective of construction site workers
Häikiö, J., Kallio, J., Mäkelä, S.-M., & Keränen, J. S. (2020) · International Journal of Occupational and Environmental Safety · doi:10.24840/2184-0954_004.001_0001 · Cited by 77 · see citing works
Comparison of five time series EMG features extractions using Myo Armband
Arief, Z., Sulistijono, I. A., & Ardiansyah, R. A. (2015) · International Electronics Symposium (IES) · doi:10.1109/elecsym.2015.7380805 · Cited by 72 · see citing works
Bangaru, S. S., Wang, C., Hassan, M., et al. (2019) · Advanced Engineering Informatics · doi:10.1016/j.aei.2019.100975 · Cited by 67 · see citing works
Self-citationArtificial Neural Network–Based Slip-Trip Classifier Using Smart Sensor for Construction Workplace
Lim, T., Park, S.-M., Lee, H.-C., & Lee, D. (2015) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001049 · Cited by 66 · see citing works
Automated Detection of Near-miss Fall Incidents in Iron Workers Using Inertial Measurement Units
Yang, K., Aria, S., Ahn, C. R., & Stentz, T. L. (2014) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784413517.096 · Cited by 58 · see citing works
Influence of smoothing window length on electromyogram amplitude estimates
St-Amant, Y., Rancourt, D., & Clancy, E. A. (1998) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.678614 · Cited by 56 · see citing works
Assessment of Construction Workers’ Labor Intensity Based on Wearable Smartphone System
Yang, Z., Yuan, Y., Zhang, M., Zhao, X., & Tian, B. (2019) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001666 · Cited by 55 · see citing works
Research on Construction Workers’ Activity Recognition Based on Smartphone
Zhang, M., Chen, S., Zhao, X., & Yang, Z. C. (2018) · Sensors · doi:10.3390/s18082667 · Cited by 54 · see citing works
Automated recognition of construction labour activity using accelerometers in field situations
Joshua, L., & Varghese, K. (2014) · International Journal of Productivity and Performance Management · doi:10.1108/ijppm-05-2013-0099 · Cited by 52 · see citing works
Angle Estimation of Simultaneous Orthogonal Rotations from 3D Gyroscope Measurements
Stančin, S., & Tomažič, S. (2011) · Sensors · doi:10.3390/s110908536 · Cited by 50 · see citing works
Kubota, A., Iqbal, T., Shah, J., & Riek, L. D. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793954 · Cited by 49 · see citing works
MyoGym: introducing an open gym data set for activity recognition collected using myo armband
Koskimäki, H., Siirtola, P., & Röning, J. (2017) · Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers · doi:10.1145/3123024.3124400 · Cited by 46 · see citing works
Reliability Testing Procedure for MEMS IMUs Applied to Vibrating Environments
Pasquale, G. D., & Somà, A. (2010) · Sensors · doi:10.3390/s100100456 · Cited by 38 · see citing works
Hand gesture recognition using MYO armband
He, S., Yang, C., Wang, M., et al. (2017) · Chinese Automation Congress · doi:10.1109/cac.2017.8243637 · Cited by 38 · see citing works
Test–retest reliability of sensor-based sit-to-stand measures in young and older adults
Regterschot, G. R. H., Zhang, W., Baldus, H., Stevens, M. J., & Zijlstra, W. (2014) · Gait & Posture · doi:10.1016/j.gaitpost.2014.03.193 · Cited by 37 · see citing works
Development of a hybrid motion capture method using MYO armband with application to teleoperation
Xu, Y., Yang, C., Liang, P., et al. (2016) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2016.7558729 · Cited by 32 · see citing works
Coupling human activity recognition and wearable sensors for data-driven construction simulation
Akhavian, R., & Behzadan, A. H. (2018) · Journal of Information Technology in Construction · Cited by 32 · see citing works
Zhang, W., Regterschot, G. J. K., Schaabová, H., Baldus, H., & Zijlstra, W. (2014) · Sensors · doi:10.3390/s140508705 · Cited by 30 · see citing works
Exploring Symmetric and Asymmetric Bimanual Eating Detection with Inertial Sensors on the Wrist
Thomaz, E., Bedri, A., Prioleau, T., Essa, I. A., & Abowd, G. D. (2017) · doi:10.1145/3089341.3089345 · Cited by 26 · see citing works
A Quantitative Comparison Of Position Trackers For The Development Of A Touch-Less Musical Interface
Vigliensoni, G., & Wanderley, M. M. (2012) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1178444 · Cited by 22 · see citing works
Frank, A. E., Kubota, A., & Riek, L. D. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968615 · Cited by 22 · see citing works
Hsu, C., Tsai, Y., Yau, C.-S., et al. (2016) · Sensors · doi:10.3390/s16081156 · Cited by 21 · see citing works
A Study Of The Noise-Level In Two Infrared Marker-Based Motion Capture Systems
Jensenius, A. R., Nymoen, K., Skogstad, S. A. V. D., et al. (2012) · NORA - Norwegian Open Research Archives · doi:10.5281/zenodo.850037 · Cited by 18 · see citing works
Accelerometer vs. Electromyogram in Activity Recognition
Koskimäki, H., & Siirtola, P. (2016) · ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL · doi:10.14201/adcaij2016533142 · Cited by 17 · see citing works
Arm Motion Estimation Algorithm Using MYO Armband
Kim, H. J., Lee, Y. S., & Kim, D. (2017) · IEEE International Conference on Robotic Computing (IRC) · doi:10.1109/irc.2017.32 · Cited by 16 · see citing works
Activity monitoring of workers using single wearable inertial sensor
Khan, S. H., & Sohail, M. (2013) · International Conference on Open Source Systems and Technologies (ICOSST) · doi:10.1109/icosst.2013.6720607 · Cited by 14 · see citing works
Validity and Reliability of the Wristband Activity Monitor in Free-living Children Aged 10-17 Years.
Yang, X., Jago, R., Zhang, Q., Wang, Y. Y., Zhang, J., & Zhao, W. H. (2019) · PubMed · doi:10.3967/bes2019.103 · Cited by 14 · see citing works
Genetic Algorithm-Based Motion Estimation Method using Orientations and EMGs for Robot Controls
Chae, J., Jin, Y., Sung, Y., et al. (2018) · Sensors · doi:10.3390/s18010183 · Cited by 12 · see citing works
Nymoen, K., Voldsund, A., Skogstad, S. A. V. D., Jensenius, A. R., & Tørresen, J. (2012) · NORA - Norwegian Open Research Archives · doi:10.5072/zenodo.207011 · Cited by 9 · see citing works
Ho, B.-J., Liu, R., Tseng, H.-Y., & Srivastava, M. B. (2017) · doi:10.1145/3089341.3089346 · Cited by 9 · see citing works
Performance Evaluation of Different Denoising Techniques for Physiological Signals
Muppalla, V., Suraj, N. S. S. K., Reddy, V. Y. S., et al. (2017) · IEEE India Council International Conference (INDICON) · doi:10.1109/indicon.2017.8487739 · Cited by 8 · see citing works
Test-retest reliability of a smartphone app for measuring core stability for two dynamic exercises
Guillén-Rogel, P., Franco-Escudero, C., & Marin, P. J. (2019) · PeerJ · doi:10.7717/peerj.7485 · Cited by 7 · see citing works
Evaluating the placement of arm-worn devices for recognizing variations of dynamic hand gestures
Kefer, K., Holzmann, C., & Findling, R. D. (2017) · Journal of Multimedia · doi:10.5555/3177188.3177192 · Cited by 6 · see citing works
Real-Time Auditory Biofeedback System for Learning a Novel Arm Trajectory: A Usability Study
Hall, S., Wild, F., & Scheper, T. V. O. (2019) · Springer eBooks · doi:10.1007/978-3-319-64301-4_18 · Cited by 6 · see citing works
A Personal Activity Recognition System Based on Smart Devices
Murcia, H., & Triana, J. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-31019-6_41 · Cited by 1 · see citing works
Biomechanical Analysis of Manual Material Handling Tasks on Scaffold
Bangaru, S. S., Chao, W., & Aghazadeh, F. (2019) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/9780784482438.072 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- [11]Akhavian R. Behzadan A.H. Productivity analysis of construction worker activities using smartphone sensors Proceedings of the 16th International Conference on Computing in Civil and Building Engineering Osaka, Japan 6–8 July 2016
- [28]Skogstad S.A. Nymoen K. Høvin M.E. Comparing inertial and optical mocap technologies for synthesis control Proceedings of the SMC 2011 8th Sound and Music Computing Conference “Creativity Rethinks Science” Padova, Italy 6–9 July 2011
- [64]Ho B.-J. Liu R. Tseng H.-Y. Srivastava M. Myobuddy: Detecting barbell weight using electromyogram sensors Proceedings of the 1st Workshop on Digital Biomarkers Niagara Falls, NY, USA 23 June 2017 27 32 PMC5831554 29505038
Cited by
Sorted by citations
ANN-based automated scaffold builder activity recognition through wearable EMG and IMU sensors
Bangaru, S. S., Wang, C., Busam, S. A., & Aghazadeh, F. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103653 · Cited by 114 · see citing works
Self-citationData quality evaluation in wearable monitoring
Böttcher, S., Vieluf, S., Bruno, E., Joseph, B., Epitashvili, N., Biondi, A., Zabler, N., Glasstetter, M., Dümpelmann, M., Van Laerhoven, K., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-25949-x · Cited by 91 · see citing works
- Authors
- Bangaru, S. S., Wang, C., & Aghazadeh, F.
- Affiliations
- Louisiana State University (US)
- Corresponding author
- Wang, Chao
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-09-15
- Volume
- 20
- Issue
- 18
- Pages
- 5264
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7570501
- OpenAlex
- W3085923853
- Cited by
- 58
- References
- 60
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 13 · 2023: 7 · 2022: 9 · 2021: 7
- FWCI
- 2.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Civil War Book Review (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Health Sciences › Health Professions › Radiological and Ultrasound Technology › Occupational Health and Safety Research
- Keywords
- wearable sensors, electromyography, inertial measurement unit, data quality, reliability statistics, construction safety, workforce shortage
- MeSH terms
- Data Accuracy, Wearable Electronic Devices, Electromyography, Humans, Movement, Occupations, Reproducibility of Results, Construction Industry
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
