This research involved 28 volunteers from a farm in El Caimo, Quindio, Colombia, and only coffee harvesters participated. The demographic characteristics of the study population are shown in Table 1.

Table 1. Table 1. Demographic data Source: Authors own elaboration.

The experimental study analyzed the working conditions of coffee harvesting activities, considering the discomfort of the workers in their daily tasks. The self-discomfort report was administered for five days, three times during the working day [12]. A postural and muscular analysis was applied using electrogoniometry (EGM) and electromyography (EMG), while the worker performed the activity for 20 minutes. A video recording was made to analyze three specific postures: arms up, arms in a neutral position and arms down [8], [12].

A self-report questionnaire of discomfort was administered at the beginning of the workday and at the middle and at the end of the day for each day on a 5-day workweek. The questionnaire consisted of a human figure, on which 16 parts of the human body were shown. The volunteer had to indicate the level of discomfort for each part of the body using a scale from 0 to 10 (Borg’s Scale).

To analyze the mechanical demand associated with the postural load and muscular activity, it was necessary to take 20-minute measurements for each person at the beginning of the workday. The variables for postural load were flexion-extension and radial-ulnar deviations of the wrist (SG65, Biometrics Ltd, UK) and flexion and extension of the shoulder (SG150, Biometrics Ltd., UK). Signal tests were recorded at an initial reference position of 0°. The muscular activity was evaluated using EMG sensors (SX230, BioMetrics Ltd., UK) on the skin surface; they had a 10 mm diameter and were separated by a distance of 20 mm to preamplify the signal. The sensors were placed on the extensor carpi radialis (ECR), extensor carpi ulnaris (ECU), flexor carpi radialis (FCR), flexor carpi ulnaris (FCU), biceps (B), deltoid (D), extensor carpi radialis nondominant arm (ECR ND) and biceps nondominant arm (B ND). The correct positions of the sensors were determined by touch, according to the length of each muscle. Before the sensors were attached, the skin was cleansed following European standardized norms [21], [22], [23]. The signal was filtered with a bandwidth of 20-460 Hz, and a band reject filter at 60 Hz was used to reduce the interference induced by the electric network (i.e., interference below 5 μV and input impedance higher than 1000 Ω).

Depending on the number of reports greater than 3 on the level of discomfort reported by the volunteers, a color was assigned to classify the parts of the body that were identified as the most affected by the participants, as shown in Table 2.

Table 2. Table 2. Classification of the discomfort levels Source: Authors own elaboration.

The video recordings were analyzed using chronicle activities with the software Actogram Kronos 2 (France), where three arm postures were evaluated (arms up, arms in a neutral position and arms down). The ergonomic chronicle, actogram software and video, were used when the arms were in the different positions mentioned above.

Surface electromyography signals recorded in ASCII were processed using algorithms developed in MATLAB R 2013a (USA). The RMS signal (root mean square) was estimated from a moving window of 200 ms [8] and normalized with each muscle’s MVC (Maximum Voluntary Contraction) record. P10 (static level), P50 (medium level) and P90 (dynamic level) were estimated using the amplitude probability distribution function (APDF).

Posture load information signals were processed by the data link software from the equipment (Biometrics Ltd., UK), recorded in ASCII (after filter) and measured in degrees. The angles of interest were associated with the corresponding APDF analyses, particularly emphasizing the averages and considering P10, P50 and P90 data. All records were processed and analyzed in SPSS 23.0 (IBM Corp. Released 2015. IBM SPSS Statistics for Windows, Version 23.0. Armonk, NY: IBM Corp.). Given the particular task, the study prioritized the body segments that fell out of the neutral ranges.

At the beginning of the workday, the discomfort was very low, except for in the knees, in which the discomfort was medium. The other two reports from the same workday showed an increase in discomfort due to exposure to the working conditions and the fatigue of the human body. The last report from the working day indicated in high discomfort, as shown in Figure 1.

Figure 1. Figure 1. Self discomfort report results Source: Authors own elaboration.

The results from the corporal segments showed that during coffee harvesting activities, the flexion and extension of the wrist was in a neutral posture 72.2% of the time. The ulnar deviations of the wrist in the neutral range represented 37.2% of the time. However, the deviation presented out of the range was divided into two ranges: 15% of the ulnar deviation was greater than 40°, and 22.2% of the time in ulnar deviation was between 15° and 40°. In addition, the radial deviation was out of the neutral range 36.7% of the time. Finally, shoulder flexion and extension were in the neutral range 84.7% of the time, and only 15.3% of the time did the work involve flexion greater than 90° (Figure 2 and Table 3).

Figura 2a Figura 2a Corporal segments: a) wrist deviation Source: Authors own elaboration.

Figura 2b Figura 2b Corporal segments: b) wrist flexion and extension Source: Authors own elaboration.

Figura 2c Figura 2c Corporal segments: c) shoulder flexion and extension Source: Authors own elaboration.

Table 3 Table 3 Statistics of the GNM analysis Source: Authors own elaboration.

The extensor carpi ulnaris was the most strained muscle in the static level (P10) with an MVC of 4.6% and a dynamic level (P90) of 29.3% of the MVC. This was the highest level reached during the neutral posture of the arms activity. In addition, it was identified that the biceps muscle of the nondominant arm had less muscle activity, as shown in Figure 3 and Table 4.

Figure 3. Figure 3. Maximum Voluntary Contraction by arm positions Source: Authors own elaboration.

Table 4. Table 4. EMG analysis (percent) Source: Authors own elaboration.