School combo-desk comfort assessment: A method for weighing postural factors that affect the overall perceived comfort

Abstract

BACKGROUND:

In recent years, a growing interest in ergonomics and comfort perception in secondary schools and universities can be detected, to go beyond the UNI-EN regulations and understanding how practically improve students’ perceived comfort during lessons.

OBJECTIVE:

This study aimed to analyse the (dis)comfort perceived by students while sitting in a combo-desk during lessons; it proposed a method for understanding and weighing the influence of postural factors on overall (dis)comfort.

METHODS:

Twenty healthy students performed a random combination of three different tasks in two sessions - listening, reading on a tablet and writing. Subjective perceptions were investigated through questionnaires, in which the expected and the overall comfort were evaluated; postural angles were gathered by processing photos through Kinovea® software and were used for the virtual-postural analysis, using a DHM (Digital Human Modelling) software; statistical analysis was used to investigate the influence of subjective comfort of each body part on the overall perceived comfort.

RESULTS:

The statistical correlations were used to perform an optimization problem in order to create a general law to formulate the overall comfort function, for each task, as a weighted sum of the comfort perceived in each body part. The test procedure, additionally, evaluated the influence on comfort over time. The results showed how the upper back and the task-related upper limb are the most influencing factors in the overall comfort perception.

CONCLUSIONS:

The paper revealed a precise and straightforward analysis method that can be easily repeated for other design applications. Obtained results can suggest to designers easy solution to re-design the combo-desk.

Keywords

Perceived (dis)comfort school seat combo-desk school activities students wellbeing

1 Introduction

Ergonomics studies the interface between people and activities they perform, the products they use and the environments in which they work, travel or play; as stated by Mokdad and Al-Ansari [1], the use of ergonomic principles allows developing guidelines for improving and redesign old/new products. The interdisciplinary nature of ergonomics makes it markedly applicable to various fields that involve human performances. Education is one field where ergonomics can give a significant contribution, but, in the past, the application of ergonomics to education receive only limited attention. Educational ergonomics is that branch of ergonomics/human factors concerned with the interaction of educational performance and educational design [2].

Much research on physical comfort and discomfort in the workplace were conducted; most papers discuss on relationships among environmental factors such as temperature, humidity, applied forces and so on that can affect perceived levels of comfort/discomfort [3]. Several papers follow the assumption that a relationship exists between self-reported discomfort and musculoskeletal injuries since these injuries affect perceived comfort [4, 5]; however, theories relating comfort to products and product design characteristics are still in development.

In this work, the authors want to investigate the problems related to the adoption of a common combo-desk used by university students.

The classroom is a learning environment in which the furniture is an essential physical element that is expected to facilitate learning by providing a comfortable and stress-free environment. Poor sitting posture in the classroom is one of the main adverse effects of bad furniture design on students [6].

Since students spend a considerable part of the day at school, sitting on a chair [7, 8], school furniture should match students’ requirements. However, studying in fixed-type furniture may induce constrained postures [9, 10]. Since people differ in size and postural preferences, workstations with adjustable seats are preferred as they have a significant positive effect on muscle tension and sitting posture, promoting health, comfort and concentration [11, 12].

Commonly schools and universities prefer fixed-type chairs than adjustable chairs due to the higher price and maintenance costs of the first one [13]. Side-mounted desktop chairs are often used in university classrooms. However, their correct design is neglected, and Thariq’s study [12] showed that side-mounted chairs in their learning environment do not meet postural and comfort requirements of university students. About that, Naddeo et al. [14 –16] identified that a custom seat influenced positively the comfort perceived by students.

It is generally accepted that continuous static muscle activity results in discomfort [17]. Regarding the number of movements, Graf et al. [18] suggested that natural movements are desirable and necessary as long as they are within an acceptable range; another study [19] stressed the importance of variation between severable stable and healthy body postures. Several studies on seating, in general, describe a relation between seating time, discomfort and body movement; Telfer et al. [20] found that subjective discomfort and movement increase over time; Vergara and Page [21] stated that macro-movements are a good indicator of discomfort, Fujimaki and Noro [22] also found discomfort to increase over time but argued that macro-movements occur in order to decrease discomfort in a repeating pattern during prolonged sitting; Callaghan and McGill [23] suggested that humans redistribute their muscular loads according to their comfort level using posture adjustment. Finally, Fasulo et al. [24] suggested that the number of movements was a good indicator of perceived lower-body (dis)comfort, particularly, it was demonstrated that an increase in discomfort causes an increase in the number of movements.

Certain medical studies showed that each joint has its own natural Rest Posture (RP) [25, 26], wherein the muscles are completely relaxed or at minimum strain level: when this occurs, the geometrical configuration corresponds to the natural position of the resting arms, legs, neck, etc. This position appears to minimize musculoskeletal disease and optimize comfort perception [3].

So, it is widely demonstrated that the type of seat, the human behaviour in terms of movements while seated, the muscular-skeletal loads and the limbs’ and back posture are the main factors that affect the discomfort experience in school/university seating. Some of them had been taken into account for our investigation.

The human state of feeling along the “comfort dimension”, where the range of postures or motions are voluntarily adopted, can roughly be classified into states of comfort and discomfort [27]. While discomfort is more related to pain, fatigue, stiffness, thus physical sensations, comfort is more associated with the physiological well-being [28, 29]. Even though comfort and discomfort are independent characteristics, they can happen simultaneously [28]. The comfort models describing sitting discomfort considers that the individual capacity and physical processes of the humans is influenced by the seat during the initial contact, short- and long-term comfort and the physical capabilities of the seats (e.g. upholstery, materials, cushions, foams), but also by the context [30 –32].

One area in which comfort studies can be applied is public offices and public furniture like those used in schools. Our study evaluates the level of comfort perceived by students while using university furniture (combo-desks). A study published in 2014 [25], involved a classroom of 126 Portuguese students and demonstrated that their university classrooms were not well-designed for the students.

In this paper, critical issues shown by the combo-desk are analysed using the statistical comfort correlations. Once identified these critical issues, several methods to check the optimised posture minimising the postural discomfort [33, 34] could be applied, or sensitivity analysis to investigate the influence of spinal posture on postural (dis)comfort perception through the correlation analysis [35]. This work aimed to identify which part of the body mainly affected the overall subjective comfort. Thus, the results were useful to perform optimization problems in order to quantify the comfort of each part of the body and to create new global comfort indexes.

2 Materials and methods

To investigate the problems related to the adoption of a common combo-desk used by university students, a method based on photo/video recording and photogrammetry, image processing using Kinovea® software, coupled with the use of DHM commercial software (CATIA® for modelling, DELMIA® for simulation) has been used. Throughout the Spearman correlation analysis, the influence of subjective comfort scores of each part of the body on the overall comfort can be analyzed, highlighting the body parts that mainly affect the overall perceived comfort. Then, an optimization problem has been performed in order to create a general law to formulate the Overall comfort function as the weighted sum of the comfort perceived in each body part.

2.1 Purpose

The aim of this study was to investigate the (dis)comfort perceived by participants during class-hours. The participants were observed during thirty minutes of lesson while sitting on a combo-desk. Each student performed a combination of three different tasks (listening, reading on a tablet and writing) and at the end of each task the perceived comfort, related to the upper limbs, was investigated by a questionnaire.

2.2 Participants

Twenty healthy volunteer MD students (8 females and 12 males), took part in the experiment. All participants signed the Informed Consent about the nature of the test, in accordance with the ethical standards of the University of Salerno. Demographic data of participants are gathered in Table 1.

Table 1
Demographic data of the participants. In Chapter 2.2

Age (years) Mass (kg) Height (m)

Mean 25,6 67,9 1,70

Std. deviation 2,1 11,7 0,10

Minimum 23 50,0 1,55

Maximum 31 86,0 1,90

	Age (years)	Mass (kg)	Height (m)
Mean	25,6	67,9	1,70
Std. deviation	2,1	11,7	0,10
Minimum	23	50,0	1,55
Maximum	31	86,0	1,90

2.3 Testing devices

The equipment used in this study for data acquisition and set-up was composed by a common combo-desk, a photographic acquisition system and a comfort questionnaire. The combo-desk was a side-mounted desktop chair (Fig. 1). It was characterized by a rigid seat-pan, a rigid seat-back, a right armrest and a side desk.

Fig. 1

Combo-desk. In section 2.3.

In the adopted configuration, the photographic acquisition system was equipped with five commercial cameras. This allowed to acquire photos from five points of view: front, behind, left side, right side, and above.

A body comfort questionnaire was used to acquire the subjective perceived comfort in which students had to rate:

The expected comfort before starting the experiment on a 10-point scale [15];

The perceived comfort for each part of the upper body, left and right, (Fig. 2), at the end of each task on a 5-point scale from 1 (Not comfortable) to 5 (Extremely comfortable) [28, 36];

The overall perceived comfort, at the end of each task on a 10-point scale [37].

Fig. 2

Comfort questionnaire. In section 2.3.

2.4 Simulation software

CATIA® V5R16 was used for the virtual-modelling of the combo-desk. DELMIA® Digital Human Modelling (DHM) software was used for modelling a ‘dummy’ based on the real participants’ anthropometric measurements [38 –43] Kinovea® software rel. 0.8.7 was used for the angular detection of users’ joints (while performing the required activities). Few small modifications were carried out to guarantee the accuracy of the manikin’s postures, according to the photogrammetric acquisition previously verified in [44] and [45].

2.5 Procedure

Testing was conducted in a class of the Faculty of Engineering at the University of Salerno. Participants were asked to sit on a combo-desk and to perform three main tasks: writing, listening and reading on a tablet. The overall duration of each test was 30 minutes, divided into two sessions of 15 minutes. In each session, each task was performed in 4 minutes, with a 1-minute pause between tasks to fill the questionnaire. The sequence of the tasks in each session was randomized. Each participant performed two sessions of test in order to consider the influence of effects overtime on comfort. Photos, from all views, were taken simultaneously just before the end of each task, making sure to have the same participant’s posture in all views. During the tasks, students were able to move freely. Photos were processed by the software Kinovea® to gather postural angles of human joints, as shown in Fig. 3. Postural angles were then used into Delmia® to simulate each posture (Fig. 4). In this step, some assumptions were made to ensure the correspondence between the angles evaluated by the two different software (Kinovea® and Delmia®). Delmia® was used to evaluate angles that were not available through the photographic acquisition, such as the medial arm rotation, the forearm pronation/supination and the hand flexion/extension, as well as the radio-ulnar deviation.

Fig. 3

Example of postural angles gathered from the software Kinovea®. The picture shows the lateral side, the most representative posture. In section 2.5.

Fig. 4

(a) Writing 1; (b) Reading 1; (c) Listening 1; (d) Writing 2; (e) Reading 2; (f) Listening 2. In section 2.5.

2.6 Data analysis

Data were gathered to evaluate the impact of the subjective comfort scores of each part of the body has on the overall perceived comfort. Table 2 shows results from questionnaires about the subjective comfort scores.

Table 2
Results from questionnaires: subjective body part comfort scores and overall perceived comfort. In Chapter 2.6

Fig. 5

A redesign example. In section 5.

The analyses were conducted for each task performed during the two sessions of the test. The statistical analysis software SPSS® rel.13 was used to perform these analyses. Spearman’s correlation coefficients were calculated to identify potential correlations among the acquired variables. Spearman’s correlation appears to be the best among other techniques like Pearson’ s correlation, as it did not require the assumption of linearity among the variables [46].

Table 3 shows the most significant correlations between the overall subjective comfort and the subjective comfort scores for the body parts during the reading and writing tasks. A strong correlation emerged between the overall comfort and the subjective comfort scores of the neck, upper back, lumbar part, right arm, right forearm and right wrist, perceived in the two sessions of reading and writing tasks. Meanwhile, the subjective comfort scores of the left arm, left forearm and left wrist were not correlated with the overall perceived comfort.

Table 3

Correlation between the overall comfort and the subjective comfort scores about reading and writing tasks. In Chapters 2.6 and 2.7

Subjective comfort	Neck	Upper back	Lumbar part	Right arm	Right forearm	Right wrist	Left arm	Left forearm	Left wrist
Writing1 (W1)	0.709^**	0.667^**	0.492^*	0.760^**	0.686^**	0.591^**
Writing2 (W2)	0.568^**	0.645^**	0.578^*	0.612^**	0.578^**	0.551^*
Reading1 (R1)	0.465^*	0.575^**	0.472^*	0.645^**	0.630^**	0.545^*
Reading2 (R2)	0.485^*	0.560^*	0.460^*	0.588^**	0.488^*	0.478^*

^**The correlation is significant at level 0.01 (2-queues) ^* The correlation is significant at level 0.05 (2-queues).

In Table 4 the significant correlations between the subjective overall perceived comfort and the subjective comfort perception for the body parts during the listening session are reported. The table shows a strong correlation between the overall perceived comfort and the subjective comfort scores of the right arm, right forearm, right wrist and upper back. Even in this case, the subjective comfort scores of the upper left limb were not related to the overall perceived comfort. Unlike other tasks, there is a lack of correlation between the overall perceived comfort and the subjective comfort scores of the neck and the lumbar part.

Table 4

Correlation between the overall comfort and the subjective comfort scores about listening. In Chapters 2.6 and 2.7

Subjective Comfort	Neck	Upper back	Lumbar part	Right arm	Right forearm	Right wrist	Left arm	Left forearm	Left wrist
Listening1 (L1)	0. 520^*	0. 574^**	0. 550^*	0. 519^*
Listening2 (L2)	0. 487^*	0. 692^**	0. 699^**	0. 730^**

^**The correlation is significant at level 0.01 (2-queues) ^* The correlation is significant at level 0.05 (2-queues).

2.7 New global comfort indexes

A new global comfort index for each task was created to understand the influence of the perceived comfort of the different parts of the body on overall comfort. These new global comfort indexes were calculated as weighted averages, considering, for each of them, only the body parts where the correlations were found: neck, upper back, lumbar part, arm and forearm for the reading and writing tasks (see Table 3); upper back, arm and forearm for the listening task (see Table 4).

The new global comfort indexes for the writing task (subscript W), performed two times, are defined by the following formulas (1) (2):

$\begin{matrix} Global Comfort Index (Writing 1) = \\ a_{1} * A_{W 1} + a_{2} * N_{W 1} + a_{3} * B_{W 1} + a_{4} * L_{W 1} \end{matrix}$ (1)

$\begin{matrix} Global Comfort Index (Writing 2) = \\ a_{1} * A_{W 2} + a_{2} * N_{W 2} + a_{3} * B_{W 2} + a_{4} * L_{W 2} \end{matrix}$ (2)

The new global comfort indexes for the reading task (subscript R), performed two times, are defined by the following formulas (3) (4):

$\begin{matrix} Global Comfort Index (Reading 1) = \\ b_{1} * A_{R 1} + b_{2} * N_{R 1} + b_{3} * B_{R 1} + b_{4} * L_{R 1} \end{matrix}$ (3)

$\begin{matrix} Global Comfort Index (Reading 2) = \\ b_{1} * A_{R 2} + b_{2} * N_{R 2} + b_{3} * B_{R 2} + b_{4} * L_{R 2} \end{matrix}$ (4)

The new global comfort indexes for the listening task (subscript L), performed two times, are defined by the following formulas (5) (6):

$\begin{matrix} Global Comfort Index (Listening 1) = \\ c_{1} * A_{L 1} + c_{3} * B_{L 1} \end{matrix}$ (5)

$\begin{matrix} Global Comfort Index (Listening 2) = \\ c_{1} * A_{L 2} + c_{3} * B_{L 2} \end{matrix}$ (6)

In which:

A is the subjective comfort score of the upper right limb, given as the arithmetic mean of the subjective comfort index of arm, forearm and wrist;

N is the subjective comfort score of the neck;

B is the subjective comfort score of the upper back;

L is the subjective comfort score of the lumbar part.

The weights must be determined considering that:

The sum of the weights must be equal to 1;

The individual weights must be strictly included in the range [0, 1].

2.8 Optimization problems

The aim, in this phase, was to maximize the sum of:

The correlation between the overall comfort perceived and the new global comfort index relative to the same task performed in the first session;

The correlation between the overall comfort perceived and the new global comfort index relative to the same task performed in the second session.

O.F. max (correlation (overall_comfort_i; new_global_comfort_index_i)_{1st _ session}+correlation (overall_comfort_i ; new_global_comfort_index_i)_{2nd _ session})

where i = reading, writing or listening tasks

Constraints

0 < weight_i<1 for i = 1,2,3,4 (for reading and writing tasks); for i = 1,3 (for listening tasks)

∑ weight_i = 1 for i = 1,2,3,4 (for reading and writing tasks); for i = 1,3 (for listening tasks)

To determine the weights of the three new global comfort indexes ((1), (2), (3), (4), (5), (6)), two optimization problems were settled.

In a first step, the Excel Solver was used. Table 5 shows the Excel Solver output: the optimal values of the weights, for the three tasks (listening, writing and reading). The weights were used to identify new global comfort indexes.

Table 5
Weights (Excel Solver). In Chapter 2.8

Weights (Excel Solver) Listening Writing Reading

a₁ 0.4605

a₂ 0.0978

a₃ 0.1274

a₄ 0.3142

b₁ 0.5101

b₂ 0.0000

b₃ 0.4214

b₄ 0.0684

c₁ 0.6715

c₃ 0.3284

Sum 1 1 1

Weights (Excel Solver)	Listening	Writing	Reading
a₁		0.4605
a₂		0.0978
a₃		0.1274
a₄		0.3142
b₁			0.5101
b₂			0.0000
b₃			0.4214
b₄			0.0684
c₁	0.6715
c₃	0.3284
Sum	1	1	1

The weights, as defined, ensured the optimal values of the correlations between the overall comfort perceived and the new global comfort index relative to the same task. In Table 6, the values of the correlation, for each task and for the first and second session, are reported. As a further check of the excellence of the results, a Macro was created in Excel to generate 10000 random weight values subject to the already-discussed constraints.

Table 6

Correlations (Excel Solver). In Chapter 2.8

Correlations (Excel Solver)	Listening	Reading	Writing
First session	0.7322	0.8228	0.8621
Second session	0.7706	0.6870	0.7345

The results, reported in Table 6 and Table 7, are very similar to those obtained through Excel Solver. To verify the goodness of the method, in Table 8 are shown the values of the O.F. (Objective Function) for both methods. The maximum values found by the Random Method were very close to the ones of the Excel Solver. The Excel Solver was a good method to identify the optimum global point.

Table 7

Weights (Random Method). In Chapter 2.8

Weights (Random Method)	Listening	Writing	Reading
a₁		0.4617
a₂		0.110
a₃		0.1110
a₄		0.3183
b₁			0.5135
b₂			0.0000
b₃			0.410
b₄			0.076
c₁	0.6715
c₃	0.3284
Sum	1	1	1

Table 8

Correlations (Random Method). In Chapter 2.8

Correlations (Random Method)	Listening	Reading	Writing
First session	0.7322	0.8222	0.8636
Second session	0.7706	0.6874	0.7328

3 Results and discussion

The new global comfort indexes were used to evaluate the comfort perception and to compare the single tasks, in order to determinate in which tasks students perceived less comfort values. Table 9 shows the obtained results.

Table 9
Objective Function (Random Method vs Excel Solver). In Chapter 2.8

O.F. Listening Writing Reading

Random Method 1.502 1.5096 1.5964

Excel Solver 1.5028 1.5097 1.5966

O.F.	Listening	Writing	Reading
Random Method	1.502	1.5096	1.5964
Excel Solver	1.5028	1.5097	1.5966

Table 10

New global comfort for each task. In Chapter 3

Task	1° session	2° session
Listening	6,249	5,420
Writing	5,3596	4,5570
Reading	5,1456	4,5434

It is clear that in both sessions, the worst comfort index is related to reading, even though the value is very similar to writing. This result is mainly due to the position taken by students during these work activities that force the student-body to be located too far from the reading and writing surface. Besides, there is a worsening of comfort indexes between the first and second session, caused by student tiredness, that was accentuated during the test. This result is in accordance with the results of Vink et al. [47] in which the influence of effects overtime on comfort and discomfort were studied. Instead, listening has the best value. This result was expected because: during the listening task, the subjects were less constrained. They could place themselves in the most comfortable way to carry out the task. Definitely, analysis was reasonably satisfactory and consistent:

In both sessions, listening is the better task and reading is the worst one;

Listening in the first session is the most comfortable activity;

Reading in the second session is the most uncomfortable activity.

It is evident that, during learning activities, the student-body was always located too far from the working surface, and while taking notes or reading something, there were negative effects on his/her back, neck and arms. The Fig. 3 shows an example of the participants’ posture. The virtual simulation aimed to verify the criticality of the postures assumed (asymmetry of postures, excessive rotation and flexion of the neck) and to support the redesign of the combo-desk in order to be more suitable for the use that is required.

In order to solve this problem, it was necessary to make changes to the folding chair desk. The proposed changes consisted of a system that allows students to set the distance between the chair and writing surface and also to tilt the desk during reading, as shown in Fig. 5. In this way, the physical characteristics of the users would be considered and so used to set the system according to own needs. In this new configuration, we expect better results in terms of global comfort indexes.

As general results, we can state that:

Comfort decrease over time, independently of the kind of activity the subject performs as reported in [22 , 48];

The activity in which the human body has the possibility to move freely is the most comfortable, as described in [28 , 49–51];

The body part that mainly affect the overall comfort in performing a task while seated, are the upper limbs that are involved in the task and the upper back.

The task involving neck flexion affects mainly the lower back part of the human body; this part affects a lot the overall comfort, as stated in [35].

4 Limitations

Moreover, some limitations of this experiment have to be acknowledged.

During the tests, no information on the lower limbs were acquired. The reason was that the investigated tasks involved, primarily, the upper part of the body, while, the lower limbs could move freely. However, it is recommended to repeat the experiments implementing the evaluation of lower limbs and, if possible, the acquisition of the pressure at the interface between the body and the chair;

The combo-desk utilized during the experiments had a very limited area of backrest surface. This aspect, probably, influenced the comfort perception, related to the upper and lumbar area, especially for the tallest subjects;

In this experiment, the participants were all right-handed. The reason was that the combo-desk had its desk on the right side, making it difficult to use for left-handed people. It could be appropriate to extend the work also considering the left-handed people so to evaluate, if and how the comfort changes between the right-handed e left-handed people. Moreover, the fixed elbow height could influence the perceived comfort because it could be not suitable or not support comfortably a wide range of population, according to the anthropometric variability;

The sample was very homogeneous and limited; therefore, the study should be extended to a wider population in order to give more validity to the results.

Even if the study has some limitation due to limited setup, limitation in acquired and analysed data (no lower limbs) and limited (in age and in type) sample of subjects, obtained results can be a useful support during the problem solving and directly suggest, to designers, easy solution to re-design the combo-desk. The proposed solution takes into account the characteristics of the tasks that the subjects have to carry out during the lessons and the subject’s anthropometric characteristics.

5 Conclusions

In this work, the authors investigated the problems related to the adoption of a common combo-desk used by university students during a combination of three different tasks (writing, listening and reading).

The method used in this work was based on photo/video recording and photogrammetry, image processing using Kinovea® software, coupled with the use of DHM commercial software (CATIA® for modelling, DELMIA® for simulation).

All the acquisition methods used are very cheap and easy to use. The most important result achieved in this paper is the precision of the acquisition method, as well as the fact that by not using complicated, expensive acquisition methods we were still able to reach an outstanding level of numerical/experimental. Indeed, the DHM analysis was able to compare the acquired postures with the modelled ones and to show the perfect match among them; furthermore, the method can be easily reproduced for other applications. Via a correlation analysis, through the Spearman index, it was possible to understand the influence of subjective comfort scores of each part of the body on the overall comfort.

The statistical analysis was able to highlight the body parts that mainly affect the overall perceived comfort.

The results showed how the upper back and the task-related upper limb are the most influencing factors in the overall comfort perception. This result was used to perform an optimization problem (with different approaches that gave the same results) in order to create a general law to formulate the Overall comfort function as the weighted sum of the comfort perceived in each body part. The result was a new global comfort index that works very well and is independent on the time-effect (decreasing of comfort in time). This work shows an easy method for doing that.

Conflict of interest

None to report.

Footnotes

Acknowledgments

The research work reported here was made possible by the support of students that kindly participated.

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School combo-desk comfort assessment: A method for weighing postural factors that affect the overall perceived comfort

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1 Introduction

2 Materials and methods

2.1 Purpose

2.2 Participants

Table 1 Demographic data of the participants. In Chapter 2.2 Age (years) Mass (kg) Height (m) Mean 25,6 67,9 1,70 Std. deviation 2,1 11,7 0,10 Minimum 23 50,0 1,55 Maximum 31 86,0 1,90

2.5 Procedure

Table 2 Results from questionnaires: subjective body part comfort scores and overall perceived comfort. In Chapter 2.6

Table 5 Weights (Excel Solver). In Chapter 2.8 Weights (Excel Solver) Listening Writing Reading a1 0.4605 a2 0.0978 a3 0.1274 a4 0.3142 b1 0.5101 b2 0.0000 b3 0.4214 b4 0.0684 c1 0.6715 c3 0.3284 Sum 1 1 1

Table 9 Objective Function (Random Method vs Excel Solver). In Chapter 2.8 O.F. Listening Writing Reading Random Method 1.502 1.5096 1.5964 Excel Solver 1.5028 1.5097 1.5966

5 Conclusions

Conflict of interest

Footnotes

Acknowledgments

References

Table 1
Demographic data of the participants. In Chapter 2.2

Age (years) Mass (kg) Height (m)

Mean 25,6 67,9 1,70

Std. deviation 2,1 11,7 0,10

Minimum 23 50,0 1,55

Maximum 31 86,0 1,90

Table 2
Results from questionnaires: subjective body part comfort scores and overall perceived comfort. In Chapter 2.6

Table 5
Weights (Excel Solver). In Chapter 2.8

Weights (Excel Solver) Listening Writing Reading

a₁ 0.4605

a₂ 0.0978

a₃ 0.1274

a₄ 0.3142

b₁ 0.5101

b₂ 0.0000

b₃ 0.4214

b₄ 0.0684

c₁ 0.6715

c₃ 0.3284

Sum 1 1 1

Table 9
Objective Function (Random Method vs Excel Solver). In Chapter 2.8

O.F. Listening Writing Reading

Random Method 1.502 1.5096 1.5964

Excel Solver 1.5028 1.5097 1.5966