Armband EMG-based Lifting Detection and Load Classification Algorithms using Static and Dynamic Lifting Trials
| dc.contributor.author | Taori, Sakshi Pranay | en |
| dc.contributor.committeechair | Jia, Xiaoting | en |
| dc.contributor.committeechair | Lim, Sol Ie | en |
| dc.contributor.committeemember | Yu, Guoqiang | en |
| dc.contributor.department | Electrical Engineering | en |
| dc.date.accessioned | 2023-06-09T08:00:29Z | en |
| dc.date.available | 2023-06-09T08:00:29Z | en |
| dc.date.issued | 2023-06-08 | en |
| dc.description.abstract | The high prevalence of work-related musculoskeletal disorders in occupational settings necessitates the development of economic, accurate, and convenient methods for quantifying biomechanical risk exposures. In terms of lifting, the occupational work environment does not provide resources for recording the start and end times of lifting tasks performed by individual workers. As a result, automatic detection of lift starts and ends is required for practical purposes. Occupational lifting styles vary depending on the asymmetry angle, which is the degree of shoulder or trunk rotation required by the lifting task. Predictive or machine learning (ML) algorithms have been increasingly used in the ergonomics field to identify occupational risk factors, such as lifting loads. However, such algorithms are often developed and validated using the dataset collected from the same lab-based experimental set-up, which limits their external validity. The recent development of wearable armbands with surface electromyography (sEMG) electrodes provides a low-cost, wireless, and non-invasive way to collect EMG data beyond laboratory settings. Despite their tremendous potential for field-based workload estimation, these armbands have not been widely implemented yet in automated lift detection and occupational workload estimation. The objective of this study was to evaluate the performance of machine learning (ML) algorithms in the automatic detection of lifts and classification of hand loads during manual lifting tasks from the data acquired by a wearable armband sensor with eight surface electromyography (sEMG) electrodes. Twelve healthy participants (six male and six female) performed repetitive symmetric (S), asymmetric (A), and free dynamic (F) lifts with three different hand-load levels (5 lb, 10 lb and 15 lb) at two origin (24" and 36") and two destination heights (6" and 36"). Three ML algorithms were utilized: Random Forest (RF), Support Vector Machines (SVM) and Gaussian Naïve Bayes (GNB). For lift detection, a subset of four participants was analyzed as a preliminary investigation. RF showed the best performance with the mean start and end errors of 0.53 ± 0.25 seconds and 0.76 ± 0.28 seconds, respectively. The accuracy score of 84.3 ± 3.3% was reported for lift start and 83.3 ± 9.9% for lift end. For hand-load classification, prediction models were developed using four different lifting datasets (S, A, S+A, and F) and were cross-validated using F as the test dataset. Mean classification accuracy was significantly lower in models developed with the S dataset (78.8 ± 7.3%) compared to A (83.3 ± 7.2%), S+A (82.1 ± 7.3%), and F (83.4 ± 8.1%). Overall, findings indicate that the implementation of ML algorithms with wearable EMG armbands for automatic lift detection in occupational settings can be promising. In hand-load classification, models developed with only controlled symmetric lifts were less accurate in predicting loads of more dynamic, unconstrained lifts, which is common in real-world settings. However, since both A and S+A demonstrated equivalent model accuracy with F, EMG armbands possess strong potential for estimating the hand loads of free-dynamic lifts using constrained lift trials involving asymmetric lifts. | en |
| dc.description.abstractgeneral | Naturalistic occupational settings involve prolonged, frequent, and physically heavy lifting-lowering tasks that are associated with a high prevalence of musculoskeletal disorders. This necessitates the development of economic, accurate, and convenient methods for quantifying risk exposures such as load magnitude, repetitiveness and duration. In terms of lifting, the occupational work environment does not provide resources for recording the start and end of lifting tasks performed by individual workers for analysis. As a result, automatic detection of lift starts and ends is required for practical purposes. Occupational lifting styles vary depending on the asymmetry angle, which is the degree of shoulder or trunk rotation required by the lifting task. Predictive or machine learning (ML) algorithms have been increasingly used in the ergonomics field to identify occupational risk factors, such as lifting loads. However, such algorithms are often developed and validated using the dataset collected from the same lab-based experimental set-up, which limits their external validity. Electromyographic (EMG) signals representing the neuromuscular activity associated with muscular contractions can be valuable for exposure assessment. The recent development of wearable armbands with surface electromyography (sEMG) electrodes provides a low-cost, wireless, and non-invasive way to collect EMG data beyond laboratory settings. Despite their tremendous potential for field-based workload estimation, these armbands have not been widely implemented yet in automated lift detection and occupational workload estimation. The objective of this study was to evaluate the performance of machine learning (ML) algorithms in the automatic detection of lifts and classification of hand loads during manual lifting tasks from the data acquired by a wearable armband sensor with eight surface electromyography (sEMG) electrodes. Twelve healthy participants (six male and six female) performed repetitive symmetric (S), asymmetric (A), and free dynamic (F) lifts with three different hand-load levels (5 lb, 10 lb and 15 lb) at two origin (24" and 36") and two destination heights (6" and 36"). Three ML algorithms were utilized: Random Forest (RF), Support Vector Machines (SVM) and Gaussian Naïve Bayes (GNB). For lift detection, a subset of four participants was analyzed as a preliminary investigation. RF showed the best performance with the mean start and end errors of 0.53 ± 0.25 seconds and 0.76 ± 0.28 seconds, respectively. The accuracy score of 84.3 ± 3.3% was reported for lift start and 83.3 ± 9.9% for lift end. For hand-load classification, prediction models were developed using four different lifting datasets (S, A, S+A, and F) and were cross-validated using F as the test dataset. Mean classification accuracy was significantly lower in models developed with the S dataset (78.8 ± 7.3%) compared to A (83.3 ± 7.2%), S+A (82.1 ± 7.3%), and F (83.4 ± 8.1%). Overall, findings indicate that the implementation of ML algorithms with wearable EMG armbands for automatic lift detection in occupational settings can be promising. In hand-load classification, models developed with only controlled symmetric lifts were less accurate in predicting loads of more dynamic, unconstrained lifts, which is common in real-world settings. However, since both A and S+A demonstrated equivalent model accuracy with F, EMG armbands possess strong potential for estimating the hand loads of free-dynamic lifts using constrained lift trials involving asymmetric lifts. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:37576 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/115383 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Surface Electromyography | en |
| dc.subject | Wearable Armband | en |
| dc.subject | Lifting-Lowering | en |
| dc.subject | Load Classification | en |
| dc.subject | Lift Detection | en |
| dc.subject | Occupational Ergonomics | en |
| dc.title | Armband EMG-based Lifting Detection and Load Classification Algorithms using Static and Dynamic Lifting Trials | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Electrical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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