Towards the Applicability of Measuring the Electrodermal Activity in the Context of Process Model Comprehension: Feasibility Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Electrodermal Activity
2.2. Context Selection
2.3. Instrumentation
2.4. Preliminary Test Run
2.5. Feasibility Study
- Mean SCL: The tonic SCL describes the changing level of skin conductivity over a period of time (see Section 2.1). A state of physiological or psychological arousal usually leads to variations (e.g., increase) in the SCL. In our context, the comprehension of process models constituted a cognitively challenging task that created a state of arousal (e.g., attentive). Therefore, the analysis of the SCL allowed for the assumption about whether the comprehension of differently complex process models results in variations (e.g., elevation) in the SCL.
- Number of SCR peaks: SCR peaks are parts of the phasic component that are indications of short-term processes with high physiological (e.g., the wait for the go-ahead) or psychological (e.g., decision making) demands. In process model comprehension, the correct interpretation of process information must be ensured and, therefore, decisions (e.g., which activities may run in parallel) must be made, which are decisive for the perception as well as the correct comprehension of the process model. For this reason, it was interesting to evaluate whether the number of SCR peaks was higher in the complex process model juxtaposed with the easy model. Importantly, only SCR peaks with an amplitude height of >0.1 μs were considered (i.e., special case with >0.05 μs; see Section 2.3).
- Mean of SCR amplitudes energy level: The mean of SCR amplitudes energy level is a measure in order to record the degree of stress (e.g., cognitive load) a stimulus or event provokes. The higher perceived stress is, the higher is the amplitude and vice versa. In our study, the evaluation of the SCR amplitudes revealed insights about the cognitive load and related processes during the comprehension of process models.
3. Results
3.1. Inferential Statistics
- Mean SCL: The Wilcoxon signed-rank test indicated that the mean SCL in the complex process model (M = 4.34 (2.71), Mdn = 3.04) was not significantly higher than the mean SCL in the easy process model (M = 4.34 (2.72), Mdn = 4.82), .
- Number of SCR peaks: The Wilcoxon signed-rank test indicated that the number of SCR peaks in the complex process model (M = 7.00 (1.94, Mdn = 7.00)) was significantly higher than the number of SCR peaks in the easy process model (M = 4.89 (1.27), Mdn = 5.00), .
- Mean of SCR amplitudes energy level: The Wilcoxon signed-rank test indicated that the mean of SCR amplitudes energy level in the complex process model (M = 0.29 (0.12), Mdn = 0.29) was not significantly higher than the mean of SCR amplitudes energy level in the easy process model (M = 0.34 (0.23), Mdn = 0.23), .
3.2. Discussion
3.3. Limitations
3.4. Lessons Learned
- Baseline measurement: The baseline represents the average skin conductance level during rest and without the presence of any stimulus. Moreover, the baseline varies over time depending on various factors (i.e., physiological or psychological arousal). Therefore, it is of importance to identify a baseline level for each individual separately before the start of an EDA measurement. There are different recommendations regarding the duration of the baseline measurement, but most of the research recommend a duration between 10 and 15 min [47,51]. In our studies, we could observe that the EDA signal stabilized at a low level after about 8 minutes. In addition, the baseline measurement can be used for a more fine-grained analysis of the EDA. For example, individuals can be identified that are hyper- or hypo-responders to a stimulus. Further, during relaxation, the identification of the frequency of non-SCR (see Section 2.1) is simplified [76].
- Recording of both EDA components: The initial research only considered the phasic SCR, while the tonic SCL was not taken into account. For short-term observations (e.g., neural reaction), the SCL can be neglected. In turn, for long-term observations, both EDA components should be recorded, since both rely on different neural mechanisms. In our context, the consideration of both components allowed for the interpretation that the comprehension of process models resulted in a state of higher cognitive arousal. Finally, with the SCR, we were able to show that the comprehension of a complex process model requires more cognitive effort.
- Limit physical activity: The EDA is a very sensitive signal, and even small movements (e.g., finger movement) may cause changes in the respective signal. Depending on the accuracy of the EDA sensor device used, even contemplation may change the EDA signal. Therefore, in order to avoid such changes, we ensured that the participants in our studies did not have to perform any additional activities and could, therefore, concentrate on the comprehension of the presented process models.
- Avoid external stimuli: Similar to the activity limitation, any external stimuli (e.g., bird calls, light changes) may affect the EDA signal: several times, we could observe this effect in the test run as well (e.g., voices in the other room). Therefore, we accepted this and tried to avoid external stimuli. Hence, the recommendation is to conduct further EDA measurements in special labs (e.g., light and soundproof) to ensure a proper recording of the respective EDA components.
- Constant setting: Another important factor that needs to be considered in the measurement of the EDA is keeping a constant setting across all participants. In particular, this ensures a valid comparability of the recorded EDA signals obtained from all participants. In this context, among others, the room temperature is a critical factor that has a very strong effect on the EDA signal. A high room temperature leads to a faster increase in both EDA components (i.e., due to increased sweat production). Hence, according to existing literature, we kept the room temperature at about 22 degrees Celsius [47].
- Attention to physiological and psychological condition: Different physiological as well as psychological conditions (e.g., tiredness, digestion) affect the EDA signal. Since it is impossible to have participants with the same physiological and psychological condition, attention should be paid that EDA measurements do not directly follow strongly perceptible sensations (e.g., hunger).
- Signal decomposition: The accurate decomposition of the tonic (i.e., SCL) and phasic (i.e., SCR) component from a raw EDA signal has created a vast body of research in this context [45]. Since the two EDA components are located at sensitive frequencies, it is important to ensure that the respective methods for analysis are capable of working with fine-grained frequency ranges (e.g., >0.05 μs as the amplitude threshold for SCR detection, as recommended in the literature [77]). Therefore, the application of further robust methods for EDA analysis as proposed in the literature is recommended. However, for gaining first experiences (e.g., ambulatory setting) and in the context of the feasibility study, the sensor used (i.e., EdaMove 3) and related software (i.e., DataAnalyzer) seem to be appropriate.
- Signal transformation: Each individual has a different skin conductivity level depending on various factors (see Section 2.1). As a result, despite the similar setting, significant differences in the baseline measurement as well as SCR amplitudes may occur between individuals. For this reason, the obtained EDA results should be standardized. Established methods are log or square root transformation fostering the comparisons between individuals [47]. Moreover, physiological factors (e.g., skin thickness) as well as potential disruptive factors (e.g., non-SCR) can be disregarded with specialized transformations.
- Consideration of more factors: The measurement of the EDA allows for the interpretation about physiological as well as psychological arousal in the presence of a stimulus. For many research purposes (e.g., neural reactions on short-term events), the analysis of the EDA components is adequate. However, in our context, the sole measurement of the EDA allowed only for limited interpretation. With the tonic component SCL, we were able to show that the comprehension of process models poses demands on cognitive effort. Regarding the phasic component SCR, we observed in the feasibility study a higher number of SCR peaks during the comprehension of the complex process model, but we can only make assumptions (e.g., they may be due to decision making) regarding their appearance. Therefore, with the addition of further measurements, a better interpretation of the EDA can be assumed. For example, with sensors recording eye movements, the appearance of SCR peaks can be associated with the gaze of an individual at the time of a peak.
4. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
Abbreviations
BPMN | Business Process Model and Notation |
DC | Direct current |
EDA | Electrodermal activity |
EEG | Electroencephalography |
GAPED | Geneva Affective Picture Database |
SCL | Skin conductance level |
SCR | Skin conductance response |
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Process Modeling Elements | |||||||
---|---|---|---|---|---|---|---|
Process Model | Activity | Event | Gateway | Edge | Pool | Lane | Total |
Easy | 6 | 2 | 4 | 13 | 1 | 1 | 27 |
Complex | 10 | 2 | 6 | 19 | 1 | 3 | 41 |
Group A | Group B | |||||
---|---|---|---|---|---|---|
PM | Mean SCL | SCR Peak | SCR Amp | Mean SCL | SCR Peak | SCR Amp |
1 | 11.12 (0.49) | 5.00 | 0.21 (0.09) | 5.83 (0.70) | 6.00 | 0.75 (0.27) |
2 | 4.83 (0.32) | 7.00 | 0.58 (0.32) | 2.82 (0.13) | 4.00 | 0.15 (0.09) |
3 | 3.16 (0.15) | 3.00 | 0.18 (0.12) | 4.82 (0.14) | 5.00 | 0.23 (0.10) |
4 | 2.79 (0.20) | 6.00 | 0.29 (0.08) | 4.87 (0.26) | 4.00 | 0.58 (0.31) |
5 | 2.01 (0.13) | 4.00 | 0.12 (0.06) | – | – | – |
Avg | 4.78 (3.31) | 5.00 (1.41) | 0.28 (0.25) | 4.59 (1.16) | 4.75 (0.83) | 0.43 (0.33) |
Group A | Group B | |||||
---|---|---|---|---|---|---|
PM | Mean SCL | SCR Peak | SCR Amp | Mean SCL | SCR Peak | SCR Amp |
1 | 10.44 (0.1) | 4.00 | 0.35 (0.06) | 6.46 (0.24) | 9.00 | 0.30 (0.15) |
2 | 4.45 (0.28) | 6.00 | 0.56 (0.23) | 2.74 (0.19) | 7.00 | 0.26 (0.23) |
3 | 2.24 (0.05) | 8.00 | 0.12 (0.04) | 4.90 (0.43) | 10.00 | 0.34 (0.21) |
4 | 3.04 (0.01) | 6.00 | 0.24 (0.12) | 2.98 (0.18) | 5.00 | 0.29 (0.09) |
5 | 1.83 (0.11) | 8.00 | 0.19 (0.10) | – | – | – |
All | 4.40 (3.15) | 6.40 (1.50) | 0.29 (0.20) | 4.27 (1.54) | 7.75 (1.92) | 0.29 (0.19) |
Easy Process Model | Complex Process Model | ||||||
---|---|---|---|---|---|---|---|
P | BM SCL | P | BM SCL | P | BM SCL | P | BM SCL |
A1 | 11.64 (0.45) | B1 | 5.42 (0.78) | A1 | 10.60 (0.40) | B1 | 5.14 (0.52) |
A2 | 4.12 (0.43) | B2 | 2.87 (0.22) | A2 | 4.10 (0.47) | B2 | 2.17 (0.39) |
A3 | 3.53 (0.52) | B3 | 4.66 (0.69) | A3 | 2.51 (0.23) | B3 | 3.72 (0.84) |
A4 | 2.47 (0.28) | B4 | 4.19 (0.67) | A4 | 2.64 (0.36) | B4 | 2.88 (0.61) |
A5 | 2.14 (0.27) | - | - | A5 | 2.18 (0.53) | - | - |
All | 4.56 (2.74) | All | 3.99 (2.56) |
Easy Process Model | Complex Process Model | ||||||
---|---|---|---|---|---|---|---|
P | BM SCR | P | BM SCR | P | BM SCR | P | BM SCR |
A1 | 5.05 (0.74) | B1 | 5.75 (0.94) | A1 | 3.80 (0.75) | B1 | 4.55 (1.40) |
A2 | 4.15 (1.01) | B2 | 4.20 (0.79) | A2 | 4.15 (0.96) | B2 | 4.65 (1.01) |
A3 | 2.55 (0.74) | B3 | 4.75 (0.83) | A3 | 5.05 (1.24) | B3 | 4.50 (1.86) |
A4 | 5.30 (1.68) | B4 | 3.65 (0.79) | A4 | 4.60 (1.59) | B4 | 4.70 (1.42) |
A5 | 3.95 (1.16) | - | - | A5 | 4.90 (1.30) | - | - |
All | 4.39 (0.95) | All | 4.55 (1.30) |
Easy Process Model | Complex Process Model | ||||||
---|---|---|---|---|---|---|---|
P | BM Amp | P | BM Amp | P | BM Amp | P | BM Amp |
A1 | 0.22 (0.09) | B1 | 0.47 (0.19) | A1 | 0.33 (0.14) | B1 | 0.24 (0.09) |
A2 | 0.55 (0.18) | B2 | 0.20 (0.05) | A2 | 0.60 (0.18) | B2 | 0.26 (0.15) |
A3 | 0.16 (0.04) | B3 | 0.23 (0.12) | A3 | 0.18 (0.07) | B3 | 0.31 (0.12) |
A4 | 0.26 (0.08) | B4 | 0.53 (0.17) | A4 | 0.24 (0.08) | B4 | 0.29 (0.12) |
A5 | 0.15 (0.05) | - | - | A5 | 0.20 (0.07) | - | - |
All | 0.31 (0.11) | All | 0.27 (0.11) |
PM | PM SCL | SCR Peak | SCR Amp |
---|---|---|---|
Easy | 4.70 (2.56) | 4.89 (1.20) | 0.37 (0.30) |
Complex | 4.34 (2.57) | 7.00 (1.83) | 0.29 (0.19) |
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Winter, M.; Pryss, R.; Probst, T.; Reichert, M. Towards the Applicability of Measuring the Electrodermal Activity in the Context of Process Model Comprehension: Feasibility Study. Sensors 2020, 20, 4561. https://doi.org/10.3390/s20164561
Winter M, Pryss R, Probst T, Reichert M. Towards the Applicability of Measuring the Electrodermal Activity in the Context of Process Model Comprehension: Feasibility Study. Sensors. 2020; 20(16):4561. https://doi.org/10.3390/s20164561
Chicago/Turabian StyleWinter, Michael, Rüdiger Pryss, Thomas Probst, and Manfred Reichert. 2020. "Towards the Applicability of Measuring the Electrodermal Activity in the Context of Process Model Comprehension: Feasibility Study" Sensors 20, no. 16: 4561. https://doi.org/10.3390/s20164561
APA StyleWinter, M., Pryss, R., Probst, T., & Reichert, M. (2020). Towards the Applicability of Measuring the Electrodermal Activity in the Context of Process Model Comprehension: Feasibility Study. Sensors, 20(16), 4561. https://doi.org/10.3390/s20164561