Analysis of Available Solutions for the Improvement of Body Posture in Chairs

Abstract

Due to the nature of current lifestyles, many people find themselves sitting for prolonged periods of time. Combined with an improper body posture, this leads to a rise in health issues. The most common ones consist of headaches and pain in the back and neck area. Other issues that may occur are changes to the spine and digestive problems, as well as anxiety and depression, which could result in declined productivity. The purpose of this study is to determine which of the available solutions is the most effective in improving the body posture while in a seated position by considering multiple aspects, such as the discrete characteristics of the solutions analyzed and the characteristics related to the manufacturability of products including the analyzed detection solution. This study considers specific criteria related to the manufacturing and behavior of systems to detect body posture in a seated position, such as invasiveness, accuracy, portability, reliability, manufacturability, privacy, and scalability. The main analysis methods involved in this study are AHP to determine the individual weights of the previously mentioned criteria, and PUGH to determine the optimal solution, taking into consideration the resulting weight of each criterion. Using the AHP method and comparing the criteria, we were able to set a priority order for the criteria. The next step consisted of constructing a PUGH matrix. This matrix is used to find out which of the available solutions is optimal based on the imposed criteria, while taking into consideration the weights resulting from the AHP method.

1. Introduction

Nowadays, most people work in an office type of environment, which involves sitting for prolonged periods of time [1]. Sitting for prolonged periods of time can lead to the development of different health issues, and by also having an incorrect sitting position those issues will only become more severe. There are some symptoms, such as headaches and pain in the back and neck area, as well as some health issues that have been linked with incorrect body posture. For instance, changes to the spine, digestive problems, anxiety, and depression are some examples of the health issues found [2,3]. Back pain, one of the previously mentioned symptoms, is considered a relevant issue in modern society since previous studies have shown that least one episode of back pain affects 50% to 80% of people at some point in their lives [4]. Taking these facts into consideration, the development of an ergonomic chair that can alert the user to incorrect body posture is highly desirable.

In this paper, various solutions to the presented problem have been analyzed to find which one is preferable. This subject is important, considering the number of people that work while sitting down. The development of a posture-correcting chair could lead to a healthier way of working, reducing the chances of developing health issues, which may also result in higher productivity. Of course, for a completely healthy working environment, other factors must be taken into consideration and more components (desk, mouse, monitor, etc.) must be designed by putting emphasis on the ergonomics. In other words, by applying ergonomics to the working environment, it is possible to decrease the level of work-related illnesses, which can also result in lower absence rates [5].

Marcu, V. and Dan, M. [6] spoke about kinetoprophylaxis as an important branch of kinesitherapy, which includes the means through which we can maintain the balance of the body but also strengthen the health of the human body. In the event that inappropriate behavior leads to drastic structural changes from wearing a corset or other orthopedic equipment that helps support the muscles or, in advanced stages, to orthopedic surgery, a recovery program through exercise and sports is mandatory and should be performed regularly.

Smith et al. [7] considered the concept of quality of life as more of a state of mental well-being, while the state of health was considered a physical component of each person. Representing a holistic self-assessment of satisfaction, referring to the individual, quality of life is a highly debated, multidimensional, difficult to define concept [8]. The state of musculoskeletal balance is good posture that protects the supporting structures of the human body against progressive damage and deformation, structures that are in a state of work or rest [9]. An important indicator of musculoskeletal health is posture, which is the alignment and maintenance of body segments in certain positions, including orthostatism, clinostatism, or sitting. This needs to be consistent with a specific position of the body in space, minimizing gravitational stresses on human body tissues [10].

2. State of the Art

There are a few ways to determine whether a person has a good or poor posture while sitting, as well as ways to correct it. Most solutions require the usage of sensors (force-sensitive sensors, flex sensors, etc.) but others favor a visual inspection system.

The solutions found can be invasive or non-invasive. For example, harnesses and the Upright Go [11] are an invasive solution for the correction of poor posture. While these are quite a direct approach and can provide good results, most users might find them undesirable as they could feel unpleasant or constricting. In order to eliminate such problems, non-invasive solutions have been created. Some of these include the addition of sensors in chairs to determine the body’s posture based on the center of gravity. Others use a camera, which visually identifies the posture adopted.

Posture-sensing chairs use sensors placed strategically in the seat and backrest of the chair. The sensors can be force-sensitive resistors, pressure cells, flex sensors, temperature sensors, acoustic sensors, and ultrasonic range sensors.

As mentioned previously, most solutions are based on the usage of sensors, more specifically, force-sensitive resistors (FSR). In Bilge et al. [12], the solution presented consists of 19 FSRs that are in a near-optimal placement in the seat and backrest of an office chair. Their prototype is able to recognize 10 different body postures with an accuracy of 78%. The 10 postures were: left leg crossed; right leg crossed, leaning left; leaning back; leaning forward; leaning left; leaning right; left leg crossed, leaning right; seated upright; right leg crossed; slouching.

In Luna-Perejón et al. [13], six FSRs are placed on the seat of the chair. The seven recorded postures were: upright, reclined, torso bent forward, torso inclined laterally to the right, torso inclined laterally to the left, upright with the right leg crossed over the left and upright with the left leg crossed over the right. The system’s accuracy in classifying the seven different body postures reached 81%.

Another example that uses FSRs can be seen in Teng et al. [14]. With a total of eight FSRs, four on the seat and four on the backrest, it was possible to detect eight states: body leaning right, leaning left, body leaning back, sitting upright, right leg crossed over left, left leg crossed over right, sitting forward, and no contact.

Others have decided to use pressure cells. For example, in Lucena et al. [2], four pressure cells were placed on the seat of a chair. The placement of the cells was based on an anatomical approach. Using these sensors, it was possible to detect 10 positions: upright; slouching; leaning forward; leaning back; leaning left; leaning right; left leg crossed over right; right leg crossed over left; left leg crossed over right and leaning right; right leg crossed over left; leaning left.

Another example, found in Geonil et al. [15], uses 12 pressure sensors in the seat and 8 in the backrest. It also has two ultrasonic sensors, placed in the shoulder blades area, with the purpose of detecting/measuring the distance between the chair and the head and neck. The goal of this device was to identify whether the posture of the user is good or not, with an accuracy of at least 70%. Body posture can also be determined using flex sensors. For example, in Qisong et al. [16], by using six flex sensors, it was possible to determine seven sitting postures. Those were: straight; left recline; right recline; lounge; lean backwards; left leg crossed; right leg crossed. Their estimated accuracy for the device is around 97%.

Another way to determine the body posture of a sitting person is to use temperature sensors and acoustic sensors. This type of solution works by detecting the body’s temperature in order to classify the body posture. The acoustic sensors should be able to detect movement when the user is changing positions. These two types of sensors are used together to achieve a higher accuracy, since it is quite possible to miss certain changes in the posture when only one of these sensors is used. An example of this solution can be seen in Russell et al. [17]. The posture changes detected were sitting leaning forward, leaning back, leaning right, stop leaning right, simultaneously leaning left and standing up.

3. Analysis

The methodology used in this paper consists of reviewing the scientific literature and researching the commercial market for solutions to detect the upper-body posture. After researching commercially available solutions and solutions proposed in the scientific literature, the next step of this study is the determination of the most significant criteria affecting the decision-making process and determining the grade of importance, or weight, of each individual criterion. The process of determining the hierarchy of criteria is conducted through AHP analysis.

AHP stands for Analytic Hierarchy Process, an analytic method involved in complex decision-making processes to objectively determine the importance of each criterion involved in the process of decision making. The reason for this comparison is to determine the order of importance of the criteria and the individual weight of each criterion. These results are used in a PUGH matrix to determine which of the solutions is a better fit based on the requirements.

The criteria taken into consideration in this study were chosen based on the characteristics of the interaction between the subject and the detection system, the grade of manufacturability of the detection system into an office chair, the effects regarding the privacy of the user, and the grade of further scalability of the resulting smart furniture piece in the case of series production.

The evaluation process for establishing the AHP analysis requires the side-by-side comparison of all criteria used. The analytic comparison process was established by the climatotherapist involved in this study and a group of research engineers with expertise in mechatronics and industrial product design with previous experience in the development and use of posture-detection devices.

Thus, the chosen criteria are:

• Non-invasive: posture detection and correction without physically affecting or hindering the user;
• Accuracy: the effectiveness of the device in classifying/ detecting body postures;
• Portability: whether the device can easily be moved;
• Reliability: whether the device can perform accordingly in a repeatable manner;
• Manufacturability: the ability to be efficiently manufactured;
• Privacy: the ability to not spread user-sensitive data;
• Scalability: the ability to extend the process of detection to a wider range of physiognomies without modifying the parameters of the detection and correction system.

Based on the results from

Table 1. AHP analysis for the criteria used in the selection detection type.

AHP Priorities: 7	Non-Invasive	Accuracy	Portability	Reliability	Manufacturability	Privacy	Scalability
Non-invasive	1	0.333	2	0.2	0.333	0.333	3
Accuracy	3	1	4	0.5	1	2	6
Portability	0.5	0.25	1	0.2	0.25	0.333	4
Reliability	5	2	5	1	3	4	7
Manufacturability	3	1	4	0.333	1	2	5
Privacy	3	0.5	3	0.25	0.5	1	4
Scalability	0.333	0.167	0.25	0.143	0.2	0.25	1
Priority	0.069	0.191	0.056	0.354	0.179	0.121	0.029

, we can reach the conclusion that reliability, having the highest score, is the most important criterion. Furthermore, given the values of the principal eigen value of 7.317 and the result of the consistency ratio of 0.039, it is concluded that the AHP analysis is successful and conclusive. This means that reliability will have the highest influence on the further analysis, more specifically on the PUGH matrix. The second highest score is for accuracy, followed by manufacturability.

By computing the weight of each criterion of interest, the technical solutions for determining the posture based on the mass center and upper-body tilt can be analyzed by using a PUGH matrix analysis. The PUGH matrix is a decision-making tool that considers the weight of each individual criterion, and, by assigning a discrete value for each analyzed solution, the final score of the optimal solution is achieved by the highest weighted average of each solution with respect to each criterion and its weight. Due to the wide range of discrete solutions available on the market, in the literature or at request, developed specifically for the user, we decided to group the solutions based on types of detection and analyze the groups through the PUGH matrix based on the average values for the previously mentioned criteria.

The means of posture detection analyzed are:

• S1—Posture correction based on ergonomics, consisting of furniture pieces, such as chairs, that through their design or construction emphasize the importance of ergonomics. Considering the design involved in constructing chairs or furniture pieces with shapes and geometries to accommodate a wide range of physiognomies and aid posture while sitting, the grade of invasiveness is significant, the accuracy is average, the reliability is high, the manufacturability and scalability are below average compared to usual furniture pieces, and the portability and privacy are maximal;
• S2—Posture detection and correction based on the mass center and upper-body tilt, consisting of devices that can mathematically determine the position of the center of gravity of the body and the tilt angle of the upper body. Considering the nature of this group, it consists of devices that can determine the sitting posture without physically interacting with the user in a way that bothers the user or makes the user aware of the devices’ presence, thus resulting in a minimal grade of invasiveness, a high accuracy grade, high reliability, high manufacturability and scalability, and maximal value for portability privacy;
• S3—Body detection and correction based on a visual inspection system, consisting of devices that use visual inspection techniques to determine the overall posture of the user. The nature of this class usually imposes the use of expensive equipment such as high-resolution, low-latency and high-refresh-rate cameras and expensive hardware for image processing. Due to these aspects, this class of solutions has a maximal value for noninvasiveness, very high accuracy, below average portability, very high reliability, and low manufacturability;
• S4—Posture detection and correction based on sound and temperature sensors, consisting of devices that use sound-based or temperature-based techniques for determining the overall posture of the user. The overall nature of this group is highly dependent on the environment, thus resulting in average values for the accuracy and reliability, maximal values for noninvasiveness, portability and privacy, and low levels of scalability and manufacturability;
• S5—Posture correction using a posture-corrector corset, consisting of wearable equipment that mechanically imposes the right posture upon the user. The nature of this group is defined by uniqueness with respect to the user or a narrow range of users, thus resulting in maximal values for accuracy and portability, with excellent results for correcting the posture, high values for reliability and privacy (being able to be worn under clothes), low values for manufacturing and scalability due to the fact that in most cases corsets are made custom for the users, and minimal values for noninvasiveness because of the permanent physical and mechanical interaction with the user;
• S6—Posture correction using wearable devices, consisting of wearable devices that can mathematically determine the position of the center of gravity of the body and the tilt angle of the upper body. The nature of this group is characterized by worn detection devices, thus resulting in an average-to-high value of noninvasiveness and scalability, with maximal values for portability and high values for accuracy, reliability, scalability and manufacturing.

The values assigned to each group for each criterion are based on the quantitative analysis of the previously mentioned characteristics on a scale from 1 (representing minimum) to 10 (representing maximum), and the values represent the average values, taking into consideration the overall design and features of the individual solutions.

Based on the results from

Table 2. PUGH matrix for the available solutions.

Solution/Criteria		C1	C2	C3	C4	C5	C6	C7
		Non-Invasive	Accuracy	Portability	Reliability	Manufacturability	Privacy	Scalability	Score
		0.069	0.191	0.056	0.354	0.179	0.122	0.029	1
S1	Ergonomics	3	5	10	8	5	10	3	6.756
S2	Mass center and upper-body tilt	10	7	10	8	7	10	7	8.095
S3	Visual inspection system	10	9	4	9	3	2	8	6.832
S4	Sound and temperature	10	5	10	4	7	10	6	6.268
S5	Posture-corrector corset	1	10	10	7	3	8	3	6.617
S6	Wearable devices	6	8	10	8	8	6	8	7.73

, the ideal solution for posture detection and correction is to use a system based on the mass center and upper-body tilt (sensor based) to precisely determine the key parameters of the sitting posture without altering the privacy of the user, achieved by being non-invasive and portable. Such an example of this type of solution was presented by Qisong et al. [15].

Even though the ideal solution is a non-invasive device, based on the results from the PUGH matrix presented above, the second-best solution is to correct the body posture through wearable devices (invasive device) such as passive harnesses, smart harnesses equipped with sensors and actuators, or body-attached devices such as the Upright Go [11]. The foundation of the previous statement results from key elements such as high values for portability and increased values for accuracy, reliability, and manufacturability with an extended scalability horizon. To determine the optimal technical solution, which is considered a detection system based on the mass center and upper-body tilt, for addressing and correcting the posture while sitting, it is necessary to analyze the discrete technical solutions that take part in the previously mentioned group.

Given the fact that all technical solutions from this group are designed for the detection of the posture while sitting, it is necessary to determine the main characteristics of each technical solution and analyze each one accordingly. The main criteria analyzed are:

• Price of the overall solution, including estimated costs for manufacturing;
• Lifespan of the solution considering the cycle-time of each system and subsystem;
• Overall dimensions of the detection system without considering the dimensions of the furniture piece in which the system is integrated;
• Measurement range of the detection system.

The results from

Table 3. AHP analysis for the criteria used in the selection of sensors.

AHP Priorities: 4	Price	Lifespan	Overall Dimensions	Measurement Range
Price	1	0.17	0.25	0.14
Lifespan	6	1	2	0.33
Overall dimensions	4	0.5	1	0.25
Measurement range	7	3	0.149	0.548
Priority	0.051	0.252	0.149	0.548

show that the most important criterion for choosing a sensor type is measurement range, followed by lifespan. Furthermore, given the values of the principal eigen value of 4116 and the result of the consistency ratio of 0.042, it is concluded that the AHP analysis for the criteria used in the selection of sensors is successful and conclusive. The least relevant criterion for choosing a sensor, in this situation, is the price. Given the previous results of the AHP analysis, we can foresee that sensors developed with technologies that can measure wide ranges of values and have increased repeatability and lifespan will be the optimal solution, even considering the high prices of acquisition and implementation. The key element regarding the previous statement is that in the scenario of choosing multiple or individual cheaper counterparts, the process of design and implementation in a product would face great challenges, thus resulting in a longer implementation and development time. The following list represents the most-used sensors in this group and their characteristics:

• S1—Piezoelectric gauge pressure sensor (Honeywell 24PC series rated to 15PSI) [18]

  ◦

  Price: ~USD 100

  ◦

  Lifespan: 1 million cycles

  ◦

  Overall dimensions: 10.7 mm × 12.7 mm × 7.7 mm

  ◦

  Measurement range: 1 Psi to 15 Psi

• S2—Force sensors (FSR402/406; SEN-09376/09375) [19]

  ◦

  Price: ~USD 25

  ◦

  Lifespan: Essentially unlimited cycle times

  ◦

  Overall dimensions: 17.47 mm × 18.3 mm

  ◦

  Measurement range: 100 g–50 kg

• S3—Flex sensors (FS-L-0055–253-ST from Spectra Symbol) [20]

  ◦

  Price: ~USD 30

  ◦

  Lifespan: more than 2 million

  ◦

  Overall dimensions: 73.66 mm × 6.35 mm

  ◦

  Measurement range: 25 K Ohms (Flat Resistance); 45 K to 125 K Ohms (Bend Resistance Range)

• S4—FX19 Compression-Load Cell [21]

  ◦

  Price: ~USD 75

  ◦

  Lifespan: Essentially unlimited cycle times

  ◦

  Overall dimensions: 19.2 mm diameter

  ◦

  Measurement range: 10–100 kg

Given the variety of phenomena and principles used by each sensor for detection, the measurement range is represented as the range in which the previously presented sensors can quantify the principle used for the detection. In this way, most sensors that rely on principles regarding the measurement of the weight of an object require at least three identical sensors to properly determine the mass center. Due to the behavior of the previously presented sensors, the weight is computed proportional to the measurement range. Considering the hardware requirements of these sensors, all the previously presented sensors require means to acquire and interpret the weight data. Usually, these components are represented as microcontrollers, microprocessors, and passive electrical components.

Based on the results from

Table 4. PUGH matrix for the sensors.

Solution/Criteria		C1	C2	C3	C4
		Price	Lifespan	Overall Dimensions	Measurement Range	Score
		0.051	0.252	0.149	0.548	1
S1	Piezoelectric	1	1	1	1	1
S2	Force sensors	10	10	10	8	8.904
S3	Flex sensors	9	2	1	3	2.756
S4	Load Cell	3	10	3	10	8.6

, it is concluded that the recommended sensors for detecting the posture of a user are force sensors, followed closely by compression-load cells.

The resulting conclusion from Table 4 suggests that force sensors are the optimal technical solution in terms of price, lifespan, overall dimensions, and measurement range to be implemented in furniture, such as chairs, to precisely determine the posture of a wide range of people. This resulting solution will be used to detect and further signal and correct their posture, aid them to maintain better health, and prevent health issues related to sitting for prolonged periods of time with inappropriate posture.

4. Conclusions

Based on the overall results, we have concluded that the ideal solution for the development of a posture-detection and -correction chair is to find the mass center and upper-body tilt. Compared to the other solutions available, this solution can be considered better due to its higher accuracy when it comes to posture classification. Another characteristic that ranks it higher than the rest is the ability to avoid discomfort for the user, as well as avoiding privacy breaches.

In order to find the mass center and upper-body tilt, we suggest using force sensors and ultrasonic sensors. The reason why these types of sensors are better suited for a posture-detection and -correction chair can be seen in the tables above.

When it comes to the device itself, it could be imbedded into the chair, or it could be attachable to it. An attachable device would be ideal since it could be attached to any office chair. Thus, it would improve the overall efficiency and health of office workers, without needing to replace every single chair.

In addition, an alert mechanism should be implemented for the user to know that their body posture is incorrect. This could be achieved through a vibration system or a visual system, with an LED or pop-up messages on a device.

It should also be mentioned that there are a lot of improvements that can be made to a posture-detection and -correction device, especially in the programming area. For example, it would be good to keep track of the user’s sitting habits. This could make the device more personal and offer specific solutions to the user to improve their posture based on their bad sitting habits. This would result in a long-term positive impact on the user’s posture and health.