Low-back electromyography (EMG) data-driven load classification for dynamic lifting tasks

Totah, Deema; Ojeda, Lauro; Johnson, Daniel D.; Gates, Deanna H.; Provost, Emily Mower; Barton, Kira

doi:10.1371/journal.pone.0192938

Cited by 18 publications

(8 citation statements)

References 33 publications

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“…sEMG will allow the detection of the early preparatory muscle activities to classify muscle loading and to initiate appropriate device activation. It has been shown that preparatory muscle activity can be leveraged to identify the intent to lift a weight up to 100 ms prior to load-onset [ 217 ]. The reduction of the effort will also be guaranteed by highly adaptive production processes.…”

Section: Discussionmentioning

confidence: 99%

Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges—A Systematic Review

Ranavolo

Draicchio

Varrecchia

et al. 2018

IJERPH

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Background: In order to reduce the risk of work-related musculoskeletal disorders (WMSDs) several methods have been developed, accepted by the international literature and used in the workplace. The purpose of this systematic review was to describe recent implementations of wearable sensors for quantitative instrumental-based biomechanical risk assessments in prevention of WMSDs. Methods: Articles written until 7 May 2018 were selected from PubMed, Scopus, Google Scholar and Web of Science using specific keywords. Results: Instrumental approaches based on inertial measurement units and sEMG sensors have been used for direct evaluations to classify lifting tasks into low and high risk categories. Wearable sensors have also been used for direct instrumental evaluations in handling of low loads at high frequency activities by using the local myoelectric manifestation of muscle fatigue estimation. In the field of the rating of standard methods, on-body wireless sensors network-based approaches for real-time ergonomic assessment in industrial manufacturing have been proposed. Conclusions: Few studies foresee the use of wearable technologies for biomechanical risk assessment although the requirement to obtain increasingly quantitative evaluations, the recent miniaturization process and the need to follow a constantly evolving manual handling scenario is prompting their use.

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Section: Discussionmentioning

confidence: 99%

Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges—A Systematic Review

Ranavolo

Draicchio

Varrecchia

et al. 2018

IJERPH

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“…Although loads were calculated using a biomechanical model, actual muscle activity generated in the shoulder muscles was not recorded. Our next step would explore using electromyography to obtain better results such as rate of fatigue (Totah et al, 2018). Moreover, objective measure such as time measurement could be used for comparing the performance between the tables.…”

Section: Discussionmentioning

confidence: 99%

Assessing the Ergonomic Design of a New Back Table for Perioperative Nurses

Thakur¹,

Kuber²,

Abdollahi³

et al. 2021

Proceedings of the International Symposium on Human Factors and

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Operating Rooms can be complex and stressful, wherein personnel are required to perform a variety of physically demanding tasks. Among the nursing care, perioperative nurses are more susceptible to musculoskeletal discomfort in low-back, shoulder and neck as they are required to perform an extensive number of demanding tasks before, during and after surgery on the “back table”. To improve the performance and mitigate the risk of injury in perioperative nurses, a new ergonomic design of back table has been developed recently. The purpose of this study was to assess the efficiency of the new table in improving the performance of nurses and reducing their risk of injury in comparison with the traditional table. The study involved 8 participants (4 Male, 4 Female) who performed simulated tasks of lifting/holding/lowering on both tables and finding instruments, similar to the actual work done by perioperative nurses. Our results showed that using the new table, the shoulder moment was reduced by 12%, while based on NASA-TLX scale, mental demand (14%), physical demand (20%), frustration (12%), effort (18%), and performance (15%) all showed reduced values. The outcomes of this study may facilitate reduction in risk of injury in the healthcare domain and may also be beneficial in improving the design of tables, not only in operating rooms but also in industrial environments.

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“…Payload estimation and compensation could be achieved by exploiting classical inverse dynamics 16 or electromyography 17,18 , or a combination of the two. To achieve this, the exoskeleton needs to be equipped either with force/torque sensors or EMG electrodes, respectively, jeopardizing wearability, task-technology fit, and enduser willingness-to-use, already critical in the industrial context 19,20 .…”

Section: Related Work and Paper Contributionmentioning

confidence: 99%

IMU-based human activity recognition and payload classification for low-back exoskeletons

Pesenti

Invernizzi

Mazzella

et al. 2023

Sci Rep

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Nowadays, work-related musculoskeletal disorders have a drastic impact on a large part of the world population. In particular, low-back pain counts as the leading cause of absence from work in the industrial sector. Robotic exoskeletons have great potential to improve industrial workers’ health and life quality. Nonetheless, current solutions are often limited by sub-optimal control systems. Due to the dynamic environment in which they are used, failure to adapt to the wearer and the task may be limiting exoskeleton adoption in occupational scenarios. In this scope, we present a deep-learning-based approach exploiting inertial sensors to provide industrial exoskeletons with human activity recognition and adaptive payload compensation. Inertial measurement units are easily wearable or embeddable in any industrial exoskeleton. We exploited Long-Short Term Memory networks both to perform human activity recognition and to classify the weight of lifted objects up to 15 kg. We found a median F1 score of $$90.80\%$$ 90.80 % (activity recognition) and $$87.14\%$$ 87.14 % (payload estimation) with subject-specific models trained and tested on 12 (6M-6F) young healthy volunteers. We also succeeded in evaluating the applicability of this approach with an in-lab real-time test in a simulated target scenario. These high-level algorithms may be useful to fully exploit the potential of powered exoskeletons to achieve symbiotic human–robot interaction.

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Low-back electromyography (EMG) data-driven load classification for dynamic lifting tasks

Cited by 18 publications

References 33 publications

Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges—A Systematic Review

Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges—A Systematic Review

Assessing the Ergonomic Design of a New Back Table for Perioperative Nurses

IMU-based human activity recognition and payload classification for low-back exoskeletons

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