Quantitative trunk muscle electromyography during lifting at different speeds

Kim, J.Y.; Marras, William S.

doi:10.1016/0169-8141(87)90016-3

Cited by 19 publications

(6 citation statements)

References 13 publications

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“…Increased sagittal flexion is expected to increase spinal loads by either increasing trunk moment (e.g., increased moment and moment arm) or muscle activity (as found by those researchers). Other researchers have found that increased muscle coactivity (Kim & Marras, 1987; and spinal loads (Granata & Marras, 1995a, 1995bMarras & Granata, 1997b;Marras & Sommerich, 1991b) are associated with increased sagittal trunk velocity. This suggests that trunk kinematics can be altered to adjust for changes in trunk moment (box weight), and the resulting spinal loads might be affected.…”

Section: Introductionmentioning

confidence: 92%

Assessment of the Relationship between Box Weight and Trunk Kinematics: Does a Reduction in Box Weight Necessarily Correspond to a Decrease in Spinal Loading?

Davis

Marras

2000

Hum Factors

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Typically, the simplest and most cost-efficient ergonomic solution to offset the rising costs of low back injuries is to reduce the box weight that is lifted. However, there is limited research on how a worker interacts with the box. In the present study, we quantify the utility of reducing the weight that is lifted - specifically, how changes in the box weight affect trunk kinematics, trunk moments, and ultimately, spinal loads. In the experiment, 15 participants lifted a variety of box weights (from 9.1 to 41.7 kg) from knee height, carried it a distance of 5 feet (1.5 m), and placed it on a shelf at elbow height. For the lower weights, small increases in box weight (3-9 kg) were offset by the trunk dynamics (sagittal velocity), resulting in no difference in spinal loads. At the same time, spinal loads were found to be significantly higher for weights above 25 kg. Thus, when making ergonomic changes (reduction of box weight), it is important to consider how workers will interact with the box. These results indicate that purely weight-based ergonomic controls might not sufficiently reduce the risk of low back disorders. Furthermore, this study provides additional evidence of the utility of using more complex spinal load models (dynamic, multiple muscle models) when evaluating highly dynamic and complex tasks.

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Section: Introductionmentioning

confidence: 92%

Assessment of the Relationship between Box Weight and Trunk Kinematics: Does a Reduction in Box Weight Necessarily Correspond to a Decrease in Spinal Loading?

Davis

Marras

2000

Hum Factors

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“…Skeletal muscle can generate large internal forces on the joints, tendons and nerve during movement that may lead to MSDs (Cutlip et al, 2009). Surface electromyography (EMG) and EMG-assisted spine loading models have been used to assess internal forces acting on the spine and the risk of low back disorders (Kim and Marras, 1987; Marras and Sommerich, 1991a; McGill and Norman, 1986; Garnder-Morse et al, 1995; Potvin, 2008). Furthermore, EMG of the shoulder muscles has also been used to assess exposure to physical demands in the workplace and subsequent risk of shoulder injury (Lee et al, 1997; Southard et al, 2007; Bao et al, 2009; Porter et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Musculoskeletal disorder risk during automotive assembly: current vs. seated

et al. 2012

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Musculoskeletal disorder risk was assessed during automotive assembly processes. The risk associated with current assembly processes was compared to using a cantilever chair intervention. Spine loads and normalized shoulder muscle activity were evaluated during assembly in eight regions of the vehicle. Eight interior cabin regions of the vehicle were classified by reach distance, height from vehicle floor and front to back. The cantilever chair intervention tool was most effective in the far reach regions regardless of the height. In the front far reach regions both spine loads and normalized shoulder muscle activity levels were reduced. In the middle and close reach regions spine loads were reduced, however, shoulder muscle activity was not, thus an additional intervention would be necessary to reduce shoulder risk. In the back far reach region, spine loads were not significantly different between the current and cantilever chair conditions. Thus, the effectiveness of the cantilever chair was dependent on the region of the vehicle.

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“…Sensor fusion has been shown to improve classification in similar applications for lower-extremity assistive devices [ 35 , 36 ]. A limitation of this work is that the lifting pace was controlled through subject training and instruction and the EMG patterns could change with varying lifting speeds [ 37 ]. Sensor fusion and Bayesian models could further improve the accuracy and account for varying lifting speeds.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2018

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ObjectiveNumerous devices have been designed to support the back during lifting tasks. To improve the utility of such devices, this research explores the use of preparatory muscle activity to classify muscle loading and initiate appropriate device activation. The goal of this study was to determine the earliest time window that enabled accurate load classification during a dynamic lifting task.MethodsNine subjects performed thirty symmetrical lifts, split evenly across three weight conditions (no-weight, 10-lbs and 24-lbs), while low-back muscle activity data was collected. Seven descriptive statistics features were extracted from 100 ms windows of data. A multinomial logistic regression (MLR) classifier was trained and tested, employing leave-one subject out cross-validation, to classify lifted load values. Dimensionality reduction was achieved through feature cross-correlation analysis and greedy feedforward selection. The time of full load support by the subject was defined as load-onset.ResultsRegions of highest average classification accuracy started at 200 ms before until 200 ms after load-onset with average accuracies ranging from 80% (±10%) to 81% (±7%). The average recall for each class ranged from 69–92%.ConclusionThese inter-subject classification results indicate that preparatory muscle activity can be leveraged to identify the intent to lift a weight up to 100 ms prior to load-onset. The high accuracies shown indicate the potential to utilize intent classification for assistive device applications.SignificanceActive assistive devices, e.g. exoskeletons, could prevent back injury by off-loading low-back muscles. Early intent classification allows more time for actuators to respond and integrate seamlessly with the user.

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Quantitative trunk muscle electromyography during lifting at different speeds

Cited by 19 publications

References 13 publications

Assessment of the Relationship between Box Weight and Trunk Kinematics: Does a Reduction in Box Weight Necessarily Correspond to a Decrease in Spinal Loading?

Assessment of the Relationship between Box Weight and Trunk Kinematics: Does a Reduction in Box Weight Necessarily Correspond to a Decrease in Spinal Loading?

Musculoskeletal disorder risk during automotive assembly: current vs. seated

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

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