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. 2017 Sep 1;17(9):2003.
doi: 10.3390/s17092003.

Wearable Devices for Classification of Inadequate Posture at Work Using Neural Networks

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Free PMC article

Wearable Devices for Classification of Inadequate Posture at Work Using Neural Networks

Eya Barkallah et al. Sensors (Basel). .
Free PMC article

Abstract

Inadequate postures adopted by an operator at work are among the most important risk factors in Work-related Musculoskeletal Disorders (WMSDs). Although several studies have focused on inadequate posture, there is limited information on its identification in a work context. The aim of this study is to automatically differentiate between adequate and inadequate postures using two wearable devices (helmet and instrumented insole) with an inertial measurement unit (IMU) and force sensors. From the force sensors located inside the insole, the center of pressure (COP) is computed since it is considered an important parameter in the analysis of posture. In a first step, a set of 60 features is computed with a direct approach, and later reduced to eight via a hybrid feature selection. A neural network is then employed to classify the current posture of a worker, yielding a recognition rate of 90%. In a second step, an innovative graphic approach is proposed to extract three additional features for the classification. This approach represents the main contribution of this study. Combining both approaches improves the recognition rate to 95%. Our results suggest that neural network could be applied successfully for the classification of adequate and inadequate posture.

Keywords: IMU; center of pressure; feature selection; instrumented insole; neural networks; posture; supervised classification.

Conflict of interest statement

The authors declare no conflict of interest. The founding organization (FRQ-NT) had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Figures

Figure 1
Figure 1
Block diagram of the overall interactive measurement tools (only the grey blocs are analyzed in this study).
Figure 2
Figure 2
The instrumented safety helmet prototype.
Figure 3
Figure 3
The prototype of the enactive insole with the preferred sensor location (the vibrating motor having the function of a rhythmic pattern is not used in this study).
Figure 4
Figure 4
Two different designs for the permanent magnet supports: (a) First design; (b) Second design.
Figure 5
Figure 5
Output voltage of Hall effect sensor with a one millimeter magnet.
Figure 6
Figure 6
Graphic of force sensors measures with different FSR’s resistor compared to a Hall Effect sensor.
Figure 7
Figure 7
The current consumption of FSR (with 10 kΩ) and Hall Effect sensors.
Figure 8
Figure 8
Resistance of conductive supports as function of its length.
Figure 9
Figure 9
Six situations for evaluating adequate and inadequate posture for handling tasks, this figure is adapted from [53] with permission from Caroline Merola and the publisher.
Figure 10
Figure 10
COP’s displacements on the instrumented insole.
Figure 11
Figure 11
Acceleration signals of the head in three axes (five tests in each case).
Figure 12
Figure 12
Areas containing the COP displacements measured in different situations.
Figure 13
Figure 13
The proposed approach for classifying postures, comprised of three phases: (1) data acquisition; (2) data preprocessing and (3) classification.
Figure 14
Figure 14
Representation of the area of the insole occupied by the COP.
Figure 15
Figure 15
The matrix of pixels in two different situations with a resolution of 1564: (a) Adequate posture in Task 2; (b) Inadequate posture in Task 2.
Figure 16
Figure 16
The hybrid model for feature selection.
Figure 17
Figure 17
Averages of the recognition rates obtained by the filter selection method.
Figure 18
Figure 18
Comparison between the performances of the filter and the hybrid selection methods.
Figure 19
Figure 19
Performance of the neural network with the graphical method according to different resolutions.
Figure 20
Figure 20
The integration of the direct and graphical methods.

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