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

<div><p>Objective</p><p>Numerous 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.</p><p>Methods</p><p>Nine 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.</p><p>Results</p><p>Regions 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%.</p><p>Conclusion</p><p>These 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.</p><p>Significance</p><p>Active 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.</p></div

Average muscle activity for different loading conditions.

<p>The average muscle activity of the two left EMG channels for all nine subjects normalized to each subject’s maximum voluntary contraction (MVC) baseline. Time zero is the time of load-onset. The shaded regions show the region of ±1 standard deviation around the average. There is a clear spike in average activity around the load-onset time point that is more prominent with increasing lifted load values. The average of the right EMG channels showed similar activity patterns.</p

Data analysis steps.

<p>Flowcharts showing the steps involved in (a) data processing, segmentation and feature extraction, and (b) dimensionality reduction, feature selection, cross-validation and, finally, testing.</p

Average classification recall for each weight class.

<p>The average testing recall for each weight class at each time window for all nine test subjects. Time zero indicates the time of load-onset.</p

The experimental setup.

<p>(a) Schematic showing a subject lifting a weight from a table by following a posture sequence shown on the screen. The table height was adjusted such that trunk flexion was near 30° for each subject. (b) The sequence of posture images shown to the subject on the screen accompanied by a timed sound cue. At the start of each lift, the screen displayed a prompt informing the subject which weight to lift—no-weight, 10-lbs, or 24-lbs, then it displayed the posture sequence with a 1 second delay between each of the numbered (1)-(8) posture images. (c) Low-back muscle activity was measured from four surface EMG bipolar electrodes placed at L4/L5 vertebrae. (d) A subject performing a 24-lbs lift with a close up of the weights on the table. The no-weight case consisted of two sticks wrapped in foil and positioned to close a circuit between two charged foil railings. The force plate under the table can also be seen, flush with the floor, in the larger image.</p

A summary of relevant response times of muscles, classifiers, controllers and actuators commonly used in assistive device applications.

<p>A summary of relevant response times of muscles, classifiers, controllers and actuators commonly used in assistive device applications.</p

Classification recall was affected by subject and weight class.

<p>Classification recall for each subject during the optimal time windows before (Pre) and after (Post) load-onset. The interaction between subject and weight class had statistically significant affects on recall percentage.</p

Selected features and their selection frequency.

<p>The percentage of times (out of 711) that each feature from each channel was selected for the optimal feature set as input by the greedy feedforward algorithm during cross-validation of the MLR classifier. The mean feature was selected much more frequently than the others.</p

Participant and agility drill experimental setup and exemplar horizontal foot trajectory smoothing.

<p>(A) Photograph of the IMU selected for this study. (B) Participant wearing foot-mounted IMUs taped to the top of both shoes, additional body-worn sensors, and military accessories (because this study was part of an experiment designed to assess soldier performance of an outdoor obstacle course). (C) Agility drill setup with exemplar high performer (left) foot x-y trajectory: pre-smoothed (magenta) and and-post smoothed (blue). Cones (orange circles) are separated with 5m distance.</p

Exemplar average horizontal foot trajectory curvature.

<p>Average foot path curvature as a function of time for an exemplar high performer. Agility drill time is the time that elapses between a performer passing cone 1 and passing cone 5. The approximate time that the performer circumvented each cone is represented with numbered orange circles.</p