Smart monitoring of worker posture in an office environment

Steven Haveman Gijs Kant

ABSTRACT Doing computer work with an incorrect posture can lead to musculoskeletal disorders. Training workers with ergonomics advice decreases these risks. In this paper a posture recognition system, CAPRIO, is proposed, using pressure sensors and context information to generate tailored ergonomics advice. Using pressure sensors alone leads to too many false positives in detecting wrong postures. Using computer usage as an additional input improves the accuracy of the posture detection. The system is compared by simulation to other possible methods on accuracy, implementation complexity, usability and privacy sensitivity. CAPRIO scores well on those criteria. It has lower accuracy than some video based methods, but is less privacy infringing.

Keywords Posture detection, Ergonomics, Pressure sensors, Context-awareness

1. INTRODUCTION Research shows that computer use, in particular with inappropriate postures, increases the risk of musculoskeletal disorders [OTMR03, YKTE+07]. From this research it is clear that preventing or improving incorrect postures will lead to less musculoskeletal disorder and better ergonomics.

A lot of research has been done in the field of ergonomic interventions. This research does not only show that there is less musculoskeletal disorder [ARDB+03] but also that workers also complete their tasks more effectively [Rob07], thus leading to an increase of performance. [Rob07] and [ARDB+03] used training sessions to instruct workers on ergonomic aspects. Throughout the process, however, the workers were not monitored anymore.

The training could probably be improved if monitoring is used to analyze the postures of the workers. Based on the posture analysis a system could then provide advice when it is needed, i.e. when a worker is not sitting correctly, and that is tailored to the specific posture of the worker.

In this paper we propose a system, Context Aware Posture Recognition In Offices (CAPRIO), that continuously monitors the posture of an office worker, so that such dynamic interventions can be done. The monitoring of the posture should be done in a smart way, which means that the monitoring does not need any effort from the worker. For this smart monitoring the worker posture must be measured accurately.

The feedback to the worker must also be presented in a smart way. This could be done in two different ways, explicitly or implicitly. In the explicit way, the office worker can be notified (for example with a pop-up on his computer screen) of an incorrect posture, urging him to adjust his position accordingly. In the implicit way, the system somehow stimulates the user in a correct posture. This could be done by automatically adjusting the chair on which the user is sitting. A problem that is not analyzed in this paper is where to display the feedback, although some options are given. Privacy is an issue in ubiquitous environments. The monitoring should be done in a way that respects the privacy of the

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workers. The CAPRIO system will thus provide training for an office worker in a smart way.

The goal of this paper is to describe and evaluate the proposed method and compare it with other existing technologies that can monitor worker posture in a smart way. For this evaluation and comparison several questions need to be answered:

1. What is a good worker posture in an office environment?

2. What are criteria for comparison of smart posture monitoring methods?

3. Which technologies exists that can be used for posture monitoring?

4. Which of these technologies fit the criteria best?

The first three questions are answered by a state of the art on ergonomics and posture monitoring and by describing the CAPRIO system. Because of time constraints no experiments have been done using an implementation of CAPRIO. Instead, simulations are used to evaluate how the CAPRIO solution performs. The last research question is answered by a comparison of existing studies and the evaluation of the CAPRIO solution.

This paper is organized as follows. An overview of worker ergonomics is in Section 2. The state of the art on posture monitoring is covered in Sections 3. Our solution is presented and evaluated in Section 4. The comparison of methods is done in Section 5. Conclusions and recommendations for future work are in Section 6.

2. WORKER POSTURE AND ERGONOMICS In this section a state of the art on the relation between ergonomics and posture is presented. Many worker postures occur, even in an office environment. However, the most occurring worker posture is when an employee works with a visual display unit (VDU). This mainly includes working on a computer workstation, but can also be working with another kind of computer terminal. When worker postures are mentioned in this paper, the posture of a worker controlling a VDU is meant.

In Section 2.1 it is discussed what postures are correct and what postures are incorrect. In Section 2.2 it is discussed how advice on postures can be generated.

2.1 Distinguishing wrong and right in worker postures There are many references to what a correct posture is and what an incorrect posture is. It is clear that there is no single correct posture. Furthermore, maintaining a correct posture also incorporates a timely switching of posture, to prevent too much strain and stress gained by constantly maintaining one position. For this paper, we will use the definition as stated by the U.S. Department of Labor, Occupational Safety & Health Administration [OSHA08]. They use the concept of neutral body positioning. Neutral body positioning incorporates a comfortable working posture in which the joints of a worker are naturally aligned. There are roughly four positions that satisfy the concept of neutral body positioning. These are depicted in Figure 1.

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The OSHA also states a few rules of thumb that will result in neutral body positioning. They also emphasize the importance of frequently changing body posture. In this paper, however, we focus on the detection of right and wrong postures.

2.2 Generating posture advice In [ARDB+03] and [Rob07] ergonomics advice is used to improve the sitting postures of office workers. Similar advice can be presented by a system that can detect the posture if it can classify the posture as a correct or incorrect sitting posture. The advice could be tailored for the specific posture of the worker.

If a person is sitting correct for a long period there is no need for providing feedback. If someone is sitting in an incorrect posture, then the advice should address the specific incorrectness of the posture, but also explain what right working postures are.

The advice need not be generated immediately, but only in certain intervals, for the purpose is to train the worker. For the same reason not only a warning should be provided, but also a description of what good postures are. For the training goal the advice should be complete and therefore extensive, but for providing specific feedback the advice should be also specific and therefore concise. We do not want to overload the user with advice, even if the user ignores the advice, but we do want to provide feedback regularly. So, there is a tradeoff between extensive (generic) and concise (specific) feedback and between small and large intervals between subsequent advices.

3. UBIQUITOUS WORKER POSTURE MONITORING METHODS In order to be able to determine whether a worker is in an ergonomic posture or not, first the posture of the worker has to be detected. Technologies already exist that can be used for that. In this section several methods are presented which are being used for detection of postures. It is also discussed if these methods can be used for the detection of the posture of someone sitting in a chair and if the method is suitable for use in an office environment.

In Section 3.1 video monitoring for posture recognition is discussed. In Section 3.2 the use of sensors on the body or on objects is discussed.

a) Upright sitting b) Standing c) Declined sitting d) Reclined sitting

Figure 1: Sitting positions with neutral body positioning

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3.1 Video monitoring For detecting postures using video, it should be considered that it is not preferable to use multiple cameras, because of privacy concerns and implementation complexity. It should also be considered that a person sitting behind his desk does not move a lot, so a video of a person sitting behind his desk does not show a lot of motion. The legs of the person could be under the surface of the desk, so there might not be a direct line of sight. Therefore the camera position must be chosen very carefully. The system should detect where the person and the chair are, in order to detect whether there is a person sitting on the chair.

In [MHK06] a survey is given of research done in the field of human motion detection and pose estimation based on video. Several pose estimation types are described, monocular or multiple view, using video or static images and with or without the use of a 3D model of the human body. Detecting of humans in an environment that does not change frequently (such as an office environment) can be done. The survey shows recent advances in pose estimation, also with constructing a 3D pose from a monocular view. So a single camera could be used for detecting the pose.

In [LC06] human pose detection from static images is presented based on a model of the human body. If the system can also detect the human body in a sitting position, this could also be used for the sitting pose detection. The pose could then also be recognized without much motion.

In [LY07] a 3D model is constructed from multiple video cameras. Also facial features, hands and fingers are recognized.

In [ZL07] a 3D human pose is derived from a sequence of images from one camera. An example of a sequence of images with derived 3D poses is in Figure 2.

Human body recognition is used in monitoring of elderly [JTD07]. It is also used in car safety applications, e.g. in [YJW07], where the driver is monitored using a camera for detection of the alertness of the driver, by recognizing eye and mouth contours. Little is written, however, on the use of human body recognition in office environments. In [AS01], however, video is used to detect activities

Figure 2: Recovering the poses of a subject performing a turning walking motion (from [ZL07])

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in an office, such as a person entering a room, picking up a phone, opening a cabinet etc.

It can be concluded that

1) Video can be used to detect the posture of person; and

2) Pose estimation from static images (or low motion video) is advancing.

It is, however, unclear if the pose detection can also be done reliably for sitting persons. Obstacles such as the desk, which prevents the camera(s) from having a direct line of sight, possibly block visual detection of postures.

From the constructed 3D pose model it could be derived if the pose is correct according to the ergonomics rules that are explained in section 2. For the analysis of the video data some processing resources are required.

3.2 Sensors on objects and body Measurements on objects can also be used to detect a person’s posture. When someone is working at a VDU, sitting on a chair and using a keyboard and a mouse, these objects can be used to detect someone’s pose. Also sensors on the body or in the clothing of the person could be used. If the position of the objects is known and the relation between the object and the person, that information could be used to derive the position and possibly the posture of the person. Pressure sensors on a chair can be used to detect whether someone is sitting on the chair and on which parts of the chair and may be able to provide information on the posture of the person. If there are pressure sensors in the back of the chair it can for instance be measured if someone leans on the back of the chair. The keyboard, the mouse and other devices can sense whether the person is using the computer, if it is known that these devices are close enough to the chair.

Body sensors might be used to detect someone’s position or posture. In [FPBA06] the posture of moving persons is determined based on accelerometer data, which is distributed by a wireless Body

Figure 3: Deriving posture from EMG measurements on the body of the subject (from [EHKK+07])

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Area Network (BAN). It might be possible to replace the accelerometer sensors by receivers that measure distance to some radio transmitters with known locations, so that the locations of the receivers can be determined by triangulation. An example of indoor localization using triangulation is in [EMLA+07], with high accuracy (30 cm), and in [RK08], using WLAN (providing an accuracy of 1.5 m). These positions can then be distributed by the BAN and a central node can then derive the posture.

In [MT06] clothing is used that is equipped with sensors, which is used to detect back postures. The same technology might be used to detect the complete posture of a person.

In [EHKK+07] electromyography (EMG) is used for measuring muscle activity in persons performing tasks while sitting in a chair. From the EMG measurements also the posture of the persons can be recognized. A picture of the tool that is used is in Figure 3.

Pressure mappings are used a lot for several applications, such as the evaluation of seating comfort, e.g. in cars and in medical applications [DKV03, SPE03, NLCC05]. In [Ash02] an overview is given of available interface pressure sensors, which includes arrays of sensors that provide pressure distribution measurements.

In [TSP01] pressure sensors are used for classifying sitting postures. A set of static posture classes has been defined and a system was trained by principal component analysis (PCA) of the pressure distributions. The system could recognize 10 different sitting postures with 79% accuracy for new subjects.

It can be concluded that several kinds of sensors and technologies can be used to detect the posture of a sitting person. Pressure measurements have been shown to be quite reliable for determining sitting postures.

Figure 4: Placing of the sensors on the chair. Original image from [BON08]

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4. CAPRIO: COMBINING PRESSURE SENSORS WITH CONTEXT-AWARENESS In order to establish a smart monitoring of a worker posture in an office environment, several options are available. Pose estimation from video-based analysis, body sensors or sensors on objects can be used. We propose a system that makes use of pressure sensors in the chairs of workers, called Context Aware Posture Recognition In Offices (CAPRIO). Because the pressure sensors alone provide limited information, the system also uses context information. In Section 4.1 the posture detecting based on the pressure sensors is described. In Section 4.2 the context-aware extension is discussed.

4.1 Pressure sensors The pressure sensor system resembles the system proposed by [MT03]. That system uses switches that indicate if the seat is in use, if the lumbar support is used, if the armrests are used and if the back rest is reclined. For that an adjusted chair is needed that can incorporate these switches. CAPRIO uses pressure sensors that are applied on the surface of the sitting, the back panel and the arm rests, which enables that almost any chair could be used. In [MT03] the sensitivity is determined at the switches. By using pressure sensors the sensitivity can be set in the processing unit, where force thresholds can be specified for each sensor. CAPRIO uses binary values, activated (1) and not activated (0). If a sensor reading exceeds the force threshold, the reading is interpreted as 1, else the reading is interpreted as 0.

Using these sensors, it can be estimated if the worker has a correct posture. In order to estimate if the worker has a right posture, pressure sensors have to be implemented in to the chair in several places.

The placing of the sensors can be seen in Figure 4. The sensors are placed in the middle of the sitting and back panel, but they can be activated at the complete width of the sitting and back panel. We assume that the chairs are adjusted correctly to fit the user’s body, i.e. the correct height and back support setting, etc.

From the posture advice in [OSHA08] it can be derived where the sensors should be on the chair. In

Table 1: Truth table for pressure sensors

Back panel Sitting panel Armrests Interpretation

Upper Lumbar Back Middle Front Left Right

1) 0 0 0 0 0 0 0 Seat not in use

2) 0 0 0 1 0 0 0 Seat in use, user is in front of chair with legs raised

3) 0 0 0 1 1 0 0 Seat in use, user is not in back of sitting panel of chair, no contact with back panel

4) 0 0 1 1 1 0 0 Seat in use, torso is tilted forward, no contact with back panel

5) 0 1 1 1 1 0 0 Seat in use, torso is lightly tilted forward

6) 1 1 1 1 1 0 0 Seat in use, torso and legs are correctly aligned

7) 1 1 1 1 1 1 1 Seat in use, torso/legs correctly aligned, shoulders are relaxed

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order to sit correctly, a person has to make contact with the complete length of the sitting panel. The sensors in the sitting panel ensure that this is the case. The back sitting panel sensor checks if a person is sitting far enough to the back, and that someone is having his legs supported by the sitting panel (front sitting panel) and that somebody is sitting on the chair (middle sitting panel), because it could be possible not to activate the front sensor and back sensor, but still be sitting in the chair.

Furthermore, a person sitting in a chair should have appropriate back support, both in the upper and lumbar region. The sensors in the back panel perceive whether this is happening. The upper sensor for the upper part of the back and the lower, lumbar sensor detects if there is appropriate lumbar support. The back sitting sensor and the lumbar support sensor might be correlated. If that is the case the system generates redundant measurements. Experiments are needed to determine whether one of the two could be discarded.

The sensors at the armrest ensure that the elbows or forearms have support, and thus that the shoulders are relaxed.

These sensors are currently only used to detect a binary value (1 or 0). Because the sensors can be activated at the whole width of sitting panel, the sensors cannot sense whether someone is sitting on the right, the left or evenly distributed on the sitting. The same holds for the sensors in the back panel: they cannot sense if someone leans on the left or right part of the panel or on the whole panel.

Table 1 shows different values of the sensors and the corresponding interpretations. In Table 2

Table 2: Pictures of the situations in the truth table

(2)

(3)

(4)

(5)

(6)

(7)

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situations are shown in which these sensor readings can occur. Table 1 can be divided in different situations, either the chair is not in use (1), or it is in use with a correct position (7) or it is in use with a wrong position (2-6). It is clear from the tables that some interpretations regarding a worker posture can be made. For example, the non-use of the lumbar support or upper back panel is always very clear and the worker could be notified to adjust his position accordingly. It is also clear that not all ergonomics guidelines can be measured with the use of these seven pressure sensors, e.g. a strange hand position cannot be detected. Also, a person needs to activate all sensors in order to obtain a correct sitting position. The table shows only a limited number of cases. The other combinations of measurements indicate a wrong posture or that the seat is not in use, but somehow still some sensors are activated. Distinguishing between these two cases can be very difficult.

Furthermore, there is a risk that the interpretations of the sensor values are not correct, because multiple situations can lead to the same combination of sensor values. This can lead to conflict situations. For example, a correct position is detected, but it is in fact a wrong position or no position (seat not in use). Or a wrong position is detected, but it is in fact a correct position or no position. Or no position is detected, but there is a wrong position or a good position. This implies that the system has to act carefully and somehow have a certain confidence that it senses the actual posture before it concludes that someone sits correctly or not. Also, if the system is not quite sure about a position, it is difficult to give tailored advice. The advice could then be inappropriate and as a result the system will annoy the user and not perform as it is supposed to. An overview of possible conflict situations is in Table 3. Table 4 shows situations in which these conflicts occur.

As we can see, a conflict between a correct posture and no posture is highly unlikely, however, a conflict between correct and incorrect and between incorrect and no posture is likely to happen sometimes.

In general the sensors cannot provide information on what the chair is used for. It cannot discriminate a package on a chair from a person and it cannot distinguish between a person working on the computer and a person discussing a subject with someone else, without using the computer at all.

Table 3: Conflict situations

Detected situation Actual situation Conflict scenarios

1) Seat not in use Seat in use, correct posture

-

2) Seat not in use Seat in use, incorrect posture

A person is sitting on only one side of the chair, not leaning in the middle, where the pressure sensors are placed

3) Seat in use, incorrect posture

Seat not in use A heavy package is placed on the chair, activating the middle sensor in the sitting panel

4) Seat in use, incorrect posture

Seat in use, correct posture

Shoulders are relaxed, but arms are not resting on armrests

5) Seat in use, correct posture

Seat not in use A pile of packages is placed in the chair while someone is leaning on the armrests

6) Seat in use, correct posture

Seat in use, incorrect posture

Chair is not set upped right, head is tilted too far, hands are in an awkward position, person is sitting with legs crossed

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Someone could for instance be turned in his chair to talk for two minutes with a colleague behind him (see for instance Table 4, 6b). This will result in a strange working posture and the system will detect that this is an incorrect posture. According to the system, this incorrect posture should be changed and the system falsely notifies the user. These false notifications can be partially corrected by using a time threshold value. This threshold value should determine the time the system has to wait before acting on an incorrect posture. This is however only a partial solution, because there are still a lot of scenarios thinkable in which this time threshold is not sufficient.

Table 4: Pictures of conflict situations

(2)

(3)

(4)

(5)

(6a)

(6b)

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4.2 Context-aware extension This is why we propose to extend this system (the chair with pressure sensors) to a more context-aware system. The extension will encompass the measuring of keyboard and mouse activity. There will also be wireless communication between the chair and the PC. For the communication Bluetooth or WLAN could be used. An overview of this system is depicted in Figure 5.

The figure shows how the system works. The user gives the system input via the chair and via the PC input devices. The pressure sensor data is collected and processed in a processing unit that is integrated in the chair. The processing unit in the chair computes the posture of the person in the chair and uses the wireless connection to send the posture information to the PC. Keyboard and mouse activity (or other activity of input devices) is also gathered to indicate whether the PC is being used. The PC runs a program that analyzes the posture information and the activity data and determines when a user should be notified and with what advice. This extension of the system deals also with the problem of determining where to display the advice to the user, because it can be shown on the screen of the active PC.

Furthermore, the system will be more accurate when combining the sensor data with keyboard or mouse activity. For example the situation in which a package is placed on the chair is now assessed correctly. Because there is no mouse or keyboard activity, the system will not think that there is a working person on the chair. Of course someone could be typing while a package is on the chair, which will still lead to a conflict situation. This is however more unlikely. Thus, the system will still not be completely accurate, but with a good setup of the force and time threshold values this can lead to an acceptable performance.

The difficulty of this kind of system setup is that is unsure if the chair that is communicating, actually communicates with the correct PC. There are two options: a one-to-one relation between the chair and the PC or no direct association.

Figure 5: An overview of the CAPRIO system

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4.2.1 A direct association between a chair and a PC

It is assumed that a certain chair is used by the same person most of the time. This person, and thus the chair, will make use of the same PC most of the time. Also, with a temporary working spot (multiple persons making use of one work terminal), it is assumed that the same chair is used most of the time for that particularly working terminal. Thus it makes sense to associate a chair to a PC, by using an identifier that is established upfront. In this manner, the system can be quite sure that if the chair is nearby and transmitting data (within data transfer range) and a PC is being used, the person controlling the PC is on that specific chair. This is depicted in Figure 6a. In the figure the circle is the range in which the PC can detect chairs. If the center of a chair is in the circle, it can be detected. If multiple chairs are around there could be conflicts. An example of a conflict is in Figure 6b. The associated chair is close enough to be detected, but it is not the chair of the person that is operating the computer terminal. In this case, the person in chair 652B could for instance demonstrate a certain program to the person in chair 651A.

4.2.2 No direct association between a chair and a PC

In order to keep the system flexible, which means that every chair should be able to be used with every computer in an office, the chairs and PCs should not be associated. The PC should select the chair that is nearest by. The Bluetooth or WLAN link between the chair and the PC could be used for that. It is expected that the chair that is nearest by always is the chair from which the PC is operated. If the PC can successfully detect the chair that is closest by, it can then start to analyze the data coming from this chair. Two possible scenarios are depicted in Figure 7. In the first scenario, only one chair is in range of the PC, thus the selection is easy. In the second scenario, two chairs are in range of the PC, it has to select the chair that is closest by. Proximity detection, however, is hard and the range of wireless communication devices is difficult to predict.

5. COMPARISON OF THE METHODS For the comparison of the methods several criteria are used. These are described in section 5.1 . The comparison is done in section 5.2 .

a) Normal situation b) Association conflict

Figure 6: Chair and keyboard are associated

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5.1 Criteria In this section the criteria for comparing posture monitoring methods in an office environment are given.

5.1.1 Accuracy

For a posture detection method to be useful for generating ergonomics advice, the method needs to be accurate and complete in detecting if the person is in a correct posture or not. The accuracy is not only determined by the number of true negatives and true positives (when the system correctly detects right and wrong postures), but also by the number of false positives and false negatives (when the system erroneously classifies a wrong posture as a right posture or a right posture as a wrong posture). It becomes especially awkward when the system mistakes a correct posture for an incorrect posture and starts giving inappropriate advice.

5.1.2 Usability and acceptability

For a monitoring method to be deployed in a regular office environment it should have high usability and acceptability for the office workers. Usability in this context is better if the system is ubiquitous in respect to the user. The user should be able to use the system right away and not undertake any extra actions to operate the system. Also, the user should not be hindered by the system during his actions. Energy management is also an issue here, because a user should not be bothered with the energy requests of the system. Acceptability means the acceptance of the system by the user, for instance that the user does not feel uncomfortable being monitored.

5.1.3 Complexity

For the system to be implemented in an office environment, where there are lots of chairs, the complexity of implementing and deploying the system should be low. Complexity also encompasses the expenses that have to be made for the system.

a) Only one chair within range b) Multiple chairs within range

Figure 7: No direct association between chair and keyboard

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5.1.4 Privacy

The information about the posture of the user should be distributed outside the system itself to be able to provide the user with feedback. It is, however, not always known who the user is. Therefore the feedback has to be sent to nearby devices. This information, that has no direct relation to a person for the system, might be related to a person for observers outside the system. Therefore the information can be privacy sensitive. For that reason only information that is necessary for providing correct feedback should be distributed, not all measured data. Other information about the user that is not relevant to the system should be collected as little as possible and should not be further stored, processed or distributed. No information has to be distributed if the user sits correctly. The shared information could perhaps be sent over a secure connection if it is known on beforehand with which devices the feedback may have to be shared.

5.2 Comparison In this section existing methods based on video, body sensors and object sensors are compared to the CAPRIO method. From the video methods both single camera and multiple camera methods are compared, because they perform different on accuracy and privacy. As body sensor based method EMG is chosen, since it is known as an accurate posture detection method. Pressure mappings are used as an example of object sensor methods, since it also known to be able to detect postures. Each posture detection solution is evaluated based on the criteria, of which an overview is given in Table 5.

5.2.1 Accuracy

Because the methods are not operating yet, it is hard to say whether the systems will be accurate or not. We expect systems that are more context-aware to perform better in the accuracy, because more data is available. We expect the number of false positives to decrease when more data is available. When we use this as base, the pressure mappings and pressure mappings and CAPRIO deliver us the least data, and will thus be less accurate. The single camera and EMG offer some more information about a worker posture. Multiple cameras will offer the most data and thus we expect this method to be most accurate.

5.2.2 Usability and acceptability

We expect all systems to work fine, without hindering the user, except the EMG system. The placement of sensors on the body of a user is a major drawback for this system. The camera methods perform

Table 5: Overview of the comparison

Single camera

Multiple cameras

EMG Pressure mappings

CAPRIO

Accuracy + ++ + +/- +/-

Usability / Acceptability + + -- + +

Complexity - -- +/- +/- +

Privacy - -- - +/- +

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slightly worse, because they can intimidate the user by giving the user a “big brother is watching you” feeling. For EMG and the pressure sensing systems, the systems that are integrated in the chair, batteries have to be used. That requires changing the batteries when they are depleted, which negatively affects the usability.

5.2.3 Complexity

We expect the camera systems to be quite complex. The camera systems need sophisticated image processing and can be quite expensive to set-up. The EMG method and the pressure mapping method are less complex. Though the system architecture can be complex also, there is less need for sophisticated processing, though there is still a lot of data to be analyzed. Because the complexity of the data is low, CAPRIO scores the highest. It is assumed that the EMG method and both pressure sensor methods need wireless transmission of data.

5.2.4 Privacy

The main privacy issue was trying to avoid redundant data, which could lead to a privacy or security breach. The multiple camera method delivers a lot of redundant information and is thus awarded with a low score. The single camera method has the same issues, but generates less data. The EMG method, and to lesser extend also the pressure mappings method, generates a lot of data about the physical conditions of a person, which is not necessary for posture detection and from which possibly health information could be derived.

When all of this data is kept local and only abstracted posture information is distributed this might not be a privacy issue, but that requires security measures that prevent other systems from accessing the local measurement data.

The CAPRIO method gathers minimal data about the person and is therefore less privacy sensitive.

6. CONCLUSIONS & FUTURE WORK Literature shows that computer use increases musculoskeletal disorders. This is widely known and much research has been done in recuperation and prevention. An example of prevention is training workers with regular training sessions. Research revealed that this training leads to less disorders and even helps performance, because workers are more organized. It can be concluded that training workers decreases disorders.

Postures that fit into the concept of neutral body positioning, which is explained in Section 2.1 , are considered to be good worker postures. This information can be used to provide a user specific training, which should at least be as effective as training workers using regular training sessions. It is assumed that this kind of training is more effective. Because workers are continuously monitored, they get advice on specific mistakes in their posture. It is also assumed that the training can be improved by providing instructions that are specifically aimed at correcting specific mistakes. From this we can conclude that systems that use posture detection can improve the training and will thus decrease disorders and help to increase performance in at least the same manner as regular training sessions.

From Section 3 it is clear that there are several methods to detect worker postures. Five methods were compared on the criteria accuracy, usability and acceptability, complexity and privacy. These methods are single camera, multiple cameras, EMG, pressure mappings and CAPRIO, our method. An overview of the comparison is in Table 5.

The single camera method and multiple cameras method are quite similar in their results. The multiple

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cameras method only has more extreme scores, because it is a multiplication of the method of the single camera. Both score high in accuracy and usability, but low on acceptability, complexity and privacy. The EMG system scores fairly low, mainly due to the low score on usability. This is because of the fact that sensors have to be attached to the skin of the subject. The pressure mappings method does a little better, but due to low accuracy and moderate scores on the other criteria, it scores lower compared to the previous three methods. Our proposed CAPRIO system scores reasonably well on all criteria.

Which method is the best depends on the weight of the different criteria. If all criteria are equally important, CAPRIO is the best option. If accuracy and performance are most important and privacy and complexity are less important, the multiple cameras method is preferable.

From section 4.2 it is clear that for a worker posture monitoring system to perform reasonably well, more than only the posture should be known. It is not about detecting any posture, but specifically about detecting worker postures. This means that someone actually has to be working. If the system does not know this, it will perform a lot worse then when the system does know this. From this, we can conclude that more context-aware systems perform better then systems that are not context-aware. But there is a downside to context-aware systems. The more a system knows, the more a system can say about other things then worker postures. This is discussed in Section 5.2.4 . From this we can conclude that a more context-aware system will result in more privacy issues.

The CAPRIO system was evaluated by using simulations. Future work will extend to actually building a system and experimenting with this system. The effect of using more accurate sensor values (not only binary values) can also be tested. Perhaps more information can be derived of using these actual sensor values. The system could be extended with pressure sensors in the feet rest or on the ground, to establish a foot position. The transmission of data and the association between PC and chair will be an important step in the implementation of the system. Also, force thresholds and time thresholds should be established from experimental data. These are all issues that should be solved when the system is up and running, because these should be system parameters that can be changed and experimented with.

Furthermore, the evaluation should be extended to also consider a frequent change of posture, as sitting in one (correct) position for a longer period is unfortunately incorrect.

REFERENCES [ARDB+03] B.C. Amick, M.M. Robertson, K. DeRango, L. Bazzani, A. Moore, T. Rooney and R.

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