Fatigue problems in remote pointing and the use of an upper-arm support

International Journal of Industrial Ergonomics 42 (2012) 293e303

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Fatigue problems in remote pointing and the use of an upper-arm support Kyung S. Park a, Gi Beom Hong a, *, Sangwon Lee b a b

Department of Industrial Engineering, Korea Advanced Institute for Science and Technology, Guseong-Dong 373-1, Yuseong-Gu, Daejeon 305-701, South Korea Department of Industrial and Management Engineering, Hanyang University, Sa3-dong 1271, Sangrok-gu, Ansan-si, Gyeonggi-do 426-791, South Korea

a r t i c l e i n f o

a b s t r a c t

Article history: Received 14 October 2011 Received in revised form 6 February 2012 Accepted 17 February 2012 Available online 22 March 2012

Remote Gesture Pointing (RGP) has been developed as a means for controllers to perform pointing operations when the controller of a device is required to move about during the task and is not normally stationed at a fixed location. Some devices in RGP environments have been developed for such purposes, but to date little research has been reported. To contribute to addressing this issue, our study examines the muscle fatigue problems in RGP environments and proposes an orthopedic appliance (which supports the upper arm) to resolve such problems. Twelve healthy subjects participated in the experiment, which had three conditions (General Mouse Pointing (GMP), RGP with an upper-arm support (‘RGP with support’), and RGP without an upper-arm support (‘RGP without support’)). Participants performed the multi-directional tapping tests for each condition. The experimental results indicated that the effects of time on the fatigue indexes (from each the upper trapezius and the anterior deltoid) was more prominent in the ‘RGP without support’ condition than in the other conditions. Especially in the ‘RGP without support’ condition, each of the fatigue indexes after 30 min became significantly different with the corresponding initial values. Similarly, the subjective fatigue scores showed a common pattern in the ‘RGP without support’ > ‘RGP with support’ > ‘GMP’ order in all parts (shoulder, upper arm, forearm, and wrist and hand). The overall performance of the RGP conditions was much lower than that of the GMP condition. Also, the throughput value was significantly higher in ‘RGP with support’ than in ‘RGP without support’. These findings show that RGP has potential fatigue and poor performance problems and an orthopedic appliance supporting the upper arm can be an alternative to alleviate those problems of RGP environments to some extent. Future work is necessary to further develop an orthopedic appliance in real situations. Relevance to industry: The method and the experimental findings in this study can be utilized as the bases for developing an orthopedic appliance to alleviate the fatigue and performance problems in real RGP environments. Ó 2012 Elsevier B.V. All rights reserved.

Keywords: Remote gesture pointing Orthopedic appliance Fatigue Performance Ubiquitous control

1. Introduction Pointing operations are generally made during the interactions between users and graphical user interface (GUI) environments on computers (Cockburn et al., 2011; MacKenzie and Jusoh, 2001). Meanwhile a mouse has been the most commonly used pointing device on desktops. However, as the use purposes of computers become highly diverse (from simple arithmetic purposes to presentation, education, entertainment, and other multimedia purposes) and computer-based applications become increasingly ubiquitous (e.g., interacting at a distance) (MacKenzie and Jusoh, 2001), the need to make pointing operations away from desktops

* Corresponding author. Tel.: þ82 42 869 3102; fax: þ82 42 869 3110. E-mail addresses: [email protected] (K.S. Park), [email protected], [email protected] (G.B. Hong), [email protected] (S. Lee). 0169-8141/$ e see front matter Ó 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.ergon.2012.02.005

become greatly emphasized. For pointing operations while users are standing or moving, they need to use other pointing apparatus (e.g., laser pointers, joy sticks, trackballs, and light guns) than a conventional mouse. Remote Gesture Pointing (RGP) is one of the pointing methods made in the air, which utilizes the motions or gestures of arms to point targets at a distance (Cockburn et al., 2011). RGP includes a broad range of methods (i.e., a conventional analog laser pointing method, and digitalized methods using cameras, gyro sensors, or light guns). The inherent properties of RGP following the conventional analog pointing operation method make it easy to handle and control. Also, due to its intuitive and quick responses (compared with other pointing methods made in the air), the RGP method has been widely adopted for various applications. In fact, there are already many commercialized products utilizing the RGP’s advantages. Those products include a light pen or gun, an air-mouse, and home video game systems such as ‘WIITM’ of NintendoÒ. In that

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most of these products do not require any complicated installation process, their applicability can be higher. Likewise, it is expected that the RGP method well fits the requirements for the nextgeneration ubiquitous environment (Ballagas et al., 2006, 2005; Cockburn et al., 2011; Eckert and Moore, 2000; Gallo et al., 2010; Horn, 1995; Kirstein and Muller, 1998; MacKenzie and Jusoh, 2001; Myers et al., 2002; Oh and Stuerzlinger, 2002; Olsen and Nielsen, 2001; Rohs, 2005; Winograd and Guimbretiere, 1999). However, RGP has a few obvious drawbacks. RGP tends to show relatively poorer performance than General Mouse Pointing (GMP). MacKenzie and Jusoh (2001) found that a mouse operated in the air performed more poorly than a standard mouse with respect to users’ speed and accuracy in completing tasks, and consequently was less favored than the latter one. Myers et al. (2002) described that using a laser pointer as an interacting device at a distance tended to be imprecise, error-prone, and slow in terms of task performance. Also, Ballagas et al. (2005) introduced a new remote interaction technique called ‘Sweep’ which operated by waving phones in the air and found that the method resulted in high task completion times and consistently low scores in a qualitative evaluation. Fatigue problems have been considered important as much as performance issues in RGP. Localized muscular fatigue can be characterized by users’ feeling for a tightened muscle, a sustained cramp with a deep and intermittent pain, or continuous pain with a desire to cease their work and activities. Actually, many researchers have demonstrated the potentially-high prevalence of musculoskeletal discomfort or fatigue in traditional video (or visual) display terminals (VDTs) (Aaras, 1994; Babski-Reeves et al., 2005; Balliet et al., 1996; Choobineh et al., 2011; Cook and Kothiyal, 1998; Horgen et al., 1995; Murata et al., 2005; Pan and Schleifer, 1996; Szeto and Sham, 2008). However, users’ responses to fatigue, discomfort, and work-related musculoskeletal disorders can be affected by many environmental factors (e.g., types of input devices, features of VDTs, users’ postures and locations, and measurement methods). By considering and controlling these factors, the studies have provided some answers to solve the problems in VDT environments. Considering the previous studies’ findings and RGP’s features of making arm movements in the air, it is basically expected that RGP gives more fatigue to users than does GMP on desktops. Also, the RGP methods that are being increasingly used in presentations or video games sometimes slow down users after several hours of use. With a variety of use purposes of RGP in ubiquitous control, the use frequency or time of RGP will be increased. However there is little reported research on RGP devices, especially muscle fatigue associated with their use. In this vein, this study tries to find the variation of muscle fatigue by time when users continuously adopts RGP, and further to provide some bases for developing an orthopedic appliance to alleviate the performance and the risk fatigue problems in RGP environments. As a physiological study, the paradigm of the research takes into account a neuroepsychophysiologic approach linked to a movement analysis. In sum, the objectives of this study are: (1) to identify whether muscle fatigue problems can occur in an RGP condition e by comparing RGP with GMP in terms of muscle fatigue and performance issues; (2) to examine the use of an orthopedic appliance on the fatigue and performance in RGP. 2. Materials and methods This section describes experimental apparatus and methods to examine the task performance and the effect of time on muscle fatigue in GMP and RGP and to find the effectiveness of an orthopedic appliance (in this study, supporting an upper arm) in an RGP environment.

2.1. Participants Twelve right-handed male graduate students volunteered to participate in the experiment. They are between 25 and 31 years old (with an average age of 28.08 years) and between 171.2 and 183.3 cm tall (with an average height of 176.2 cm). No participant reported any conscious shouldereneck or upper extremity fatigue. Before conducting the experiment, they were fully informed of the purposes of the experiment and the possibility of muscle fatigue according to the ethical guidelines of the university. 2.2. Apparatus 2.2.1. Development of an orthopedic appliance The results from a preliminary pilot test for prolonged RGP showed that there existed not only low performance problems in RGP but also fatigue problems, as expected in the previous studies’ findings. Though lighter-weight equipment and appropriate relaxation/filtering of hand unsteadiness could help solve these problems to some extent, these seemed to be insufficient in directly resolving users’ inherent limits in motions and gestures. Related to this issue, we designed an orthopedic appliance that could supplement RGP. The design axioms (i.e., the Independence Axiom and the Information Axiom) (Suh, 2001) were utilized in designing our appliance. The Independence Axiom is “Maintain the independence of the functional requirements” and the Information Axiom is “Minimize the information content of the design”. Previous studies found that the forearm support reduced the relevant muscle activity and discomfort in using a general mouse in the VDT environment (Aaras et al., 1998, 2001; Cook and BurgessLimerick, 2001, 2002; Delisle et al., 2006; Woods et al., 2002). In a similar sense, it is expected that supporting a part of the arm in the RGP environment would be effective in repeatedly performing Decomposition, Zigzagging, and Hierarchy tasks. In this study, an orthopedic appliance to support the upper arm was designed with the design axioms above e including all the functional requirements but minimizing the information content of the appliance’s design. Table 1 represents the final form and diagram of the functional requirements and the design parameters. As a stage of the appliance development, each design parameter was inserted in dummy parameter form. According to these basic guidelines (Table 1), an orthopedic appliance was designed so that it could fix and support the upper arm. Regarding the ‘RGP with support’ condition in our experiment, Fig. 1 represents how the appliance functions, which upper limb movements occur for all the degree of freedom, and how participants equip and use it. The center of mass of the arm is on the backside of the upper arm during the control. Thus it is necessary to support the backside of the upper arm. Basically, the part to support upper arm did not disturb or limit the forearm’s horizontal (left/ right) and vertical (up/down) movements. The orthopedic appliance had four adjustable parts (ⓐ, ⓑ, ⓒ, and ⓓ in Fig. 1). The part ⓐ was connected to a waist belt of a participant so that it could support the appliance’s weight. Considering the anthropometric characteristics of Korean people, this part was adjustable in the connecting point, providing ten levels by 1 cm (10 cm in total), and was rotatable in a horizontal axis. For fixation, the part ⓑ was linked to a belt of the chest part of a participant, being controllable according to his or her height. The part ⓒ had a function to control the supporting angle (by 10 ) for a participant’ arm. However, our experiment was performed only in the basic posture (as seen in Fig. 1) because the focus of this study was in the measurement of the static fatigue during a long time. Though the part ⓒ was fixed during the experiment, its angle to the horizontal plane (by actual measurement) could be different when a participant wore the

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Table 1 Functional requirements (FRs) and design parameters (DPs) for an orthopedic appliance. Functional domain

Physical domain

FR1: Reduce the fatigue of the shoulder and arm FR11: Distribute the load FR111: Distribute the load to the back, belly and waist FR112: Distribute the load to the legs FR12: Reduce the total load

DP1: Make sure to support the arm from the bottom. DP11: Use a belt and a support DP111: Use the ‘back & belly‘ and waist fixation belts DP112: Require a vertical axis support to distribute the load to the legs DP12: Prevent the oscillation of the arm during the task

FR2: Improve the throughput FR21: Increase the precision FR22: Increase the velocity

DP2: Fix a part of the arm to the body or some utensil DP21: Provide a place for the arm to rest DP22: Move the central axis of movement to the rear of the ankle

FR3: Retain the wear ability FR31: Do not disturb the movements FR32: Make available for all physiques FR33: Retain the mobility FR34: Make possible various application motions

DP3: Enhance the convenience for users DP31: Support the upper arm, not the lower DP32: Design so that the height and position of the support can be adjusted DP33: Design so that the support fits the body DP34: Design so that axial & radical angle variance is possible

C1: Hard to control the rest time

appliance. Thus, our experiment utilized the marks of body points e the actual angle between the acromion and the elbow was 0e10 in vertical. Finally, the part ⓓ was made to be controllable in length when supporting an upper arm. The important thing was that the experiment was conducted after the four parts were set in given conditions so that participants could feel comfortable. 2.2.2. Input device Among several devices that implement RGP, an air-mouse captures motions using gyro sensors and allows users to make pointing operations in the air. Specifically, the MX Air Rechargeable Cordless Air Mouse (LogitechÔ) used in this study can function as a laser mouse on desktops. This feature could minimize the errors that could occur when other devices with different properties were used in our experiment. Due to the prolonged experimental time, only the air-mouse was used as the experimental device for the

conditions reflecting both the RGP and the general VDT environments. 2.2.3. EMG recording The experiment used Norxon MyoTrace 400 (Noraxon USA, Inc.) to collect surface electromyographic (EMG) data (20e500 Hz filtering). The EMG signals were amplified and conversed with the analogue-to-digital sampled at a rate of 1 kHz. The waveforms were collected and analyzed by a graphical programming analysis system (MyoResearch XP Master; Noraxon USA, Inc.). The trapezius muscle is related with the neck and the shoulder. This can be applied to remote pointing environments. Also, considering the characteristics of remote pointing, the movements are often made with the elbow as their axis without the use of the wrist, and thus the loads are mainly on the anterior deltoid muscles. In fact, the pilot test results indicated that the biceps brachii of the upper arm

Fig. 1. Orthopedic appliance for supporting the upper arm.

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was similar to the anterior deltoid, and the extensor carpi ulnaris and the extensor digitorum of the forearm were not different, in terms of the load and fatigue. For these reasons, disposable bipolar AGeAgCL surface electrodes were placed on the upper trapezius (UT; midway between the vertebra prominens and the acromion) and the belly of the anterior deltoid (AD) muscles with an interelectrode distance of 20 mm. The electrodes were located longitudinal to the direction of the muscle fiber. Before placing the surface electrodes, the experimenter gently abraded and cleaned the participants’ skin with sandpaper and alcohol. Inter-electrode impedances were below 10 kU (mostly below 5 kU). 2.2.4. The Borg’s scale estimation of fatigue The Borg’s CR-10 scale has been commonly used as a subjective assessment tool of fatigue in previous studies (e.g., Alricsson et al., 2001; Crenshaw et al., 2010; Dedering et al., 2000; Govindu and Babski-Reeves, 2012; Hummel et al., 2005). The scale was constructed to measure ratings of subjective fatigue with the numbers anchored by verbal expressions (from 0 ‘nothing at all’ to 10 ‘very strong’) (Borg, 1982, 1990; Strimpakos et al., 2005). In our experiment, participants were first instructed to take time to look at the written expressions of the Borg’s scale and then to choose a number corresponding to the basic fatigue level (V0 in Fig. 3). To reduce possible alternations which could influence EMG detection, the experimenter started measuring the scores using verbal expressions before each tapping test ended (overlapping with the test). To encourage more exact cognition, it was ensured that participants comprehended Borg’s scale fully before performing the experimental tasks and during rests. 2.3. Experimental tasks ISO 9241-9 (ISO, 2000) describes standard tests to evaluate the efficiency and effectiveness of non-keyboard input devices. Among the several test methods in ISO 9241-9, the ‘tapping test’ consisting of simple pointing and clicking actions has been commonly used as a standardized test in previous studies of pointing devices (e.g., Ballagas et al., 2005; Hertzum and Hornbæk, 2010; MacKenzie and Jusoh, 2001; Myers et al., 2002; Oh and Stuerzlinger, 2002). There are two types of tapping tests: the one-directional tapping test and the multi-directional tapping test. In our experiment, we employed the multi-directional tapping test, which has basically more degree of freedom than the one-directional test. The multi-directional tapping test is suitable for measuring the fatigue related to pointing tasks because the test includes multi-directional pointing and clicking actions in a standardized way (from ISO 9241-9). The test was programmed with Microsoft Visual Basic 6.0. In a test set (each ‘Pi’ in Fig. 3) for a condition (GMP, ‘RGP without support’, or ‘RGP with support’), six subtests (two subtests per task difficulty; see below) was continuously provided. The number of the provided subtests depended on a participant’s speed to complete tasks. Five sets were given to each participant (see Section 2.5). Regarding the test procedure for each set, participants were firstly asked to click on the highlighted topmost button. When clicked, a button on the other side of the topmost button got highlighted (① in Fig. 2). Once a cursor moved and that button clicked, the button next to the topmost button got highlighted (② in Fig. 2). Again, when this

Fig. 2. Multi-directional tapping subtest (medium precision).

button was clicked, another button on the opposite side got highlighted (③ in Fig. 2). This zigzagging process was repeated in the clockwise direction. In this way, each subtest was completed when the 25th click was performed. Following the instructions of ISO 9241-9, three precision levels determined by target distance/width were chosen to cover a range of task difficulties:

ID ¼ log2 ðD=W þ 1Þ ¼ log2 ð960=80 þ 1Þ ¼ 3:7 bit : low percision;

(1)

ID ¼ log2 ðD=W þ 1Þ ¼ log2 ð800=40 þ 1Þ ¼ 4:39 bit : medium percision;

(2)

ID ¼ log2 ðD=W þ 1Þ ¼ log2 ð640=8 þ 1Þ ¼ 6:34 bit : high percision;

(3)

where D is the target distance (pixel) and W is the target width (pixel). The distance between participants and the screen was 3 m and the actual size of the projected display was about 1 cm per 8 pixels. The test session for a condition began when participants pointed to the topmost button and ended when they completed all the sequences. All participants were asked to perform the tasks as quickly and accurately as possible and to leave errors uncorrected. They completed the three test sessions (i.e., the three conditions). 2.3.1. Mean movement time The mean movement time in each condition can be calculated by dividing the total task completion time by the total number of subtests participants complete:

  Kvp 6 5 S12 v¼1 Sp¼1 St¼1 Ss¼1 Task completion time of subsetvpts   Mean movement time ¼ 5 Total # of subsets ¼ S12 v¼1 Sp¼1 Kvp  6

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Subtestvpts ¼ the sth subtest of the tth test of the pth set of the vth participant. kvp ¼ the # of completely finished tests in each of p’s (p ¼ 1e5) of the vth participant.

with the air-mouse and the orthopedic appliance supporting the upper arm. The experiment was conducted in three different conditions (sessions), each of which had five sets. The conditions were:

2.3.2. Mean error rate The mean error rate in each condition can be obtained by calculating the average error rate of subtests participants complete:

(1) General Mouse Pointing (GMP) e Conduct mouse control under a general VDT environment consisting of an adjustable chair with an armrest and a desk;

  kvp 5 6 S12 v¼1 Sp¼1 St¼1 Ss¼1 Total # of errors in subsetvpts =Total # of clicks in subsetvpts   Mean error rate ¼ 5 Total # of subsets ¼ S12 v¼1 Sp¼1 Kvp  6

Subtestvpts ¼ the sth subtest of the tth test of the pth set of the vth participant. kvp ¼ the # of completely finished tests in each of p’s (p ¼ 1e5) of the vth participant. error ¼ missed click of targeted button. 2.3.3. Throughput A tapping test is essentially a point-selection task. According to the ISO guidelines (ISO, 2000), a primary performance measure is ‘throughput’ (bits per second (bps)), which is a composite measure of the performance speed and accuracy:

Throughput ¼ IDe =MT; IDe ¼ log2 ððD=We Þ þ 1Þ; We ¼ 4:133 SD; Here, MT is the mean movement time (in seconds) for all trials within the same condition, and IDe is the effective index of difficulty (in bits) which is calculated from D (the distance to the target) and We (the effective width of the target, which can be computed from the observed distribution of selection coordinates in participants’ trials). Also, SD is the standard deviation of the selection coordinates measured along the axis of approach to the target. Throughput is similar to Fitts’ Index of Performance (IP) (Fitts, 1954). The actual measured sized of a target (W) in IP is replaced with the effective width (We). In this sense, throughput reflects the overall efficiency with which users were able to accomplish the tasks with given constraints of the device or other interface aspects. 2.4. Experimental procedure and analyses As a preliminary stage, one day before participating in the experiment, participants had enough practice to become familiar

(2) Remote Gesture Pointing without support (‘RGP without support’) e Conduct RGP without wearing the orthopedic appliance supporting the upper arm; (3) Remote Gesture Pointing with an upper arm support (‘RGP with support’) e Conduct RGP while wearing the orthopedic appliance supporting the upper arm. Participants performed the experimental tasks for all the conditions (sessions). The subtests of each set were continuously provided to participants in a counter-balanced order (for 15 min). Between the conditions, a 3-h break was given. Participants finished the whole experiment in one day to reduce the influence of after-effects. The tasks in different conditions were performed using the same procedure (see Fig. 3). Before performing the main pointing tasks, participants made the 30-s subjective ratings of fatigue by verbal expressions for the Borg’s CR-10 scale (V0). Then, the experimenter collected participants’ EMG data (30 s) and gave them to s short rest (30 s) (‘EMG recording and short rest’; T0). Each experiment for a condition (session) was composed five sets, each of which included 15-min multi-directional tapping subtests, a subjective rating of fatigue made 30 s before a set ended (like in V0), and a 1-min EMG recording and short rest (like in T0). The three sessions were assigned in a counter-balanced order. For the analysis of physiological fatigue, Mean Power Frequency (MPF), Median Frequency (MDF), and Root Mean Square (RMS) were derived from the EMG data of the upper trapezius and the anterior deltoid and were then normalized by dividing the values by each of the corresponding initial values (Uetake et al., 2000). When recording the EMG signals (during T0eT5 in Fig. 3), the experimenter asked participants to maintain their right arms in the forward flexion posture in the horizontal plane while holding a 2-kg dumbbell, so that the recording could be made under the same isometric condition (Kimura et al., 2007; Murata and Ishihara, 2005; Uetake et al., 2000). With respect to the force level, Öberg

Fig. 3. Experimental protocol of each session.

152.0 (79.6) 166.1 (85.1) 156.8 (59.5)

T5 T4

162.5 (101.7) 160.4 (78.9) 157.1 (61.8) 153.4 (60.9) 155.6 (77.9) 161.9 (71.1)

T3 T2

162.5 (87.6) 149.4 (69.2) 160.0 (67.4) 146.5 (72.7) 152.5 (75.1) 158.8 (61.4)

T1 T0

160.7 (78.9) 139.5 (60.6) 165.8 (68.2) 189.3 (92.8) 181.1 (87.3) 173.2 (100.3) 183.8 (86.5) 178.8 (91.4) 175.4 (100.2)

RMS of AD

T5 T3

176.5 (86.2) 174.4 (96.5) 168.5 (91.5)

T2

177.7 (94.4) 173.9 (97.5) 171.2 (100.3) 167.4 (94.7) 161.3 (97.5) 166.1 (91.6) 63.2 (7.4) 61.7 (8.6) 66.6 (9.3)

T1 RMS of UT

T0 T5

63.0 (8.6) 63.0 (8.9) 66.2 (9.7) 63.7 (8.2) 64.6 (8.7) 66.4 (8.4)

T4 T3 T2

RGP without support RGP with support

64.2 (8.3) 66.6 (8.4) 66.8 (8.3) GMP

T1

MDF of AD

T0

Index

Block

RGP without support RGP with support

62.3 (8.3) 64.8 (8.3) 67.3 (9.0)

T3 T2

T4

73.5 (9.0) 72.7 (8.7) 76.7 (10.2)

T4 T3

73.1 (8.5) 73.7 (8.8) 76.2 (9.4) 72.0 (9.5) 74.7 (9.3) 77.1 (9.8)

T2 T1

73.6 (9.5) 74.8 (9.0) 76.3 (9.3) 73.6 (9.4) 75.3 (9.6) 76.7 (9.6)

T0 T5

66.9 (7.9) 64.8 (7.4) 69.8 (7.7) 67.1 (8.1) 66.0 (7.2) 70.2 (7.8)

T4 T3

67.5 (8.4) 66.9 (6.7) 70.4 (8.1) 67.3 (8.4) 66.8 (6.7) 70.1 (7.4)

T2

67.5 (8.3) 69.2 (6.2) 69.9 (8.4)

67.6 (8.5) 67.7 (5.9) 70.1 (8.2)

T0

T1

180.6 (98.6) 174.7 (93.4) 177.3 (105.6)

59.5 (7.3) 59.6 (5.6) 61.4 (7.2)

T1 T0

58.3 (8.3) 60.4 (4.9) 60.5 (7.3) 72.9 (8.4) 71.9 (8.7) 76.3 (9.5)

T5

MDF of UT MPF of AD

GMP

For the physiological fatigue, three fatigue indexes (normalized MPF, normalized MDF, and normalized RMS) from each of the two measurement parts (UT and AD) were analyzed e the means and the standard deviations of raw data were presented in Table 2. The ANOVA results for the effects of time on the physiological fatigue in each condition revealed that: for the GMP condition, the time effects were significant only in terms of normalized UT RMS (F(5, 55) ¼ 3.866; p-value ¼ 0.004); for the ‘RGP without support’ condition, all the fatigue indexes of the two parts were significantly affected by time (normalized UT RMS: (F(5, 55) ¼ 3.204; p-value ¼ 0.013), other p-values < 0.001); and for the ‘RGP with support’ condition, no significant difference by time was found in the fatigue indexes. The results of the paired t-tests between the values in T0 and in each time-block for a specific condition are displayed in Fig. 4 (*: p-value < 0.1, **: p-value < 0.05). In this test, the ‘RGP with support’ condition was excluded because it showed no significant difference by time with respect to the physiological fatigue (see the

Block

3.1. Physiological fatigue

MPF of UT

In this section, the analysis results for the experimental data are described in terms of the physiological fatigue indexes, the subjective fatigue ratings, and the performance measures.

Index

3. Results

Table 2 Means and standard deviations of raw data (before normalization) of physiological fatigue indexes.

The experimental data showed that the physiological fatigue values and the performance measures satisfied the homogeneity of variance assumption and the normality assumption of one-way analyses of variance (ANOVAs) but the subjective ratings of fatigue did not. Nonparametric tests were employed to analyze the subjective fatigue scores. To examine the effects of time on the fatigue values from EMG recording in each condition, a one-way randomized block design ANOVA was performed. The paired t-tests between the physiological values of the initial point (T0) and the values of each time-block (T1eTe5) were conducted to obtain more information about those differences by time in a condition. Also, to check the differences among the conditions at a specific time, the EMG-related values in each time-block (except T0) were analyzed by a one-way randomized block design ANOVA and were grouped by Tukey’s HSD post hoc tests. The Friedman tests were performed to examine the effects of time on subjective fatigue scores in each condition and the differences among the conditions in each time-block (V0eV5). The Wilcoxon signed-rank tests between the subjective values in the initial point (V0) and in each time-block (V1eV5) were conducted to obtain more information about the changes by time in a condition and the differences among the conditions in a time-block. Finally, the performance data depending on the conditions were analyzed by the one-way randomized block design ANOVA. For more information about the differences by the conditions, Scheffe’s post hoc tests were additionally performed.

59.0 (7.6) 58.1 (5.9) 61.2 (6.3)

2.5. Statistical analyses

63.4 (8.1) 65.8 (7.9) 66.1 (8.6)

T5 T4

59.1 (7.3) 57.7 (6.9) 61.6 (6.6)

et al. (1992, 1994) found that there was a significant relationship between psychological fatigue scores and EMG values when participants held a 1-kg or 2-kg dumbbell. In addition, the subjective assessments of fatigue for shoulder, upper arm, forearm, and wrist and hand were measured (during V0eV5 in Fig. 3), and the mean movement time, the mean error rate, and the mean throughput were recorded (during P1eP5 in Fig. 3) as performance measures.

58.7 (7.3) 56.3 (7.3) 61.2 (6.9)

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59.0 (7.4) 58.5 (5.9) 61.6 (6.7)

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Fig. 4. Physiological fatigue indexes e MPF (a, b), MDF (c, d), and RMS (e, f) For each of UT and AD.

ANOVA results above). In the GMP condition, the normalized UT RMS in T4 and T5 (after approximately 1 h) showed significant increases over that in T0. For the ‘RGP without support’ condition, the values of the T2eT5 blocks were significantly less than those of the T0 block in terms of the normalized UT MPF, the normalized UT MDF, and the normalized AD MDF (after approximately 30 min). The normalized AD MPF significantly decreased and the normalized AD RMS significantly increased since the T3 block (after approximately 45 min), and the normalized UT RMS showed significant increases since the T4 block (after approximately 1 h). In addition, we found that the normalized UT MPF and the normalized UT MDF (T1 through T5) were above 1. This means that the initial fatigue was recovered or the noise was relatively greater than the change of fatigue. It is necessary to interpret this phenomenon with the results of the other fatigue indexes, not focusing on the values themselves. The ANOVA results for the effects of the conditions on the physiological fatigue in each time-block indicated that all the three fatigue indexes from the two measurement parts were significantly affected by the conditions (p-values < 0.1). The conditions were grouped by Tukey’s HSD post hoc tests for all the UT and AD fatigue indexes in a specific time (p-values < 0.05; the dotted ellipses in Fig. 4). Especially, the division between ‘RGP without support’ and the other conditions tended to be more prominent as time went by. For all but the case of the normalized UT RMS, significant differences between ‘RGP without support’ and the others were found in

T4 and T5. The three conditions were not clearly grouped in terms of the normalized UT RMS. More specifically, the normalized UT MPF and the normalized UT MDF of ‘RGP with support’ were significantly greater than those of ‘RGP without support’, and the normalized AD RMS of ‘RGP with support’ was significantly less than that of ‘RGP without support’, from the T2 block (after approximately 30 min). Also, the normalized UT MPF and the normalized UT MDF of GMP became greater than those of ‘RGP without support’ in the T3 and the T1 blocks, respectively, and the normalized AD MDF of ‘RGP with support’ was greater than that of GMP only in the T2 block. 3.2. Subjective fatigue rating The Borg’s scale scores at shoulder, upper arm, forearm, and wrist and hand in V0eV5 are presented in Fig. 5. The Friedman test results showed that the effects of time on the subjective fatigue were significant for all the conditions (p-values < 0.01). Similar to the cases of the physiological fatigue, differences between the ‘RGP without support’ and the other conditions tended to increase as time went by. Based on the results of the Wilcoxon signed-rank test, the main effects of the conditions on the subjective fatigue for the four parts were marked as dotted ellipses in Fig. 5. For the subjective fatigue for shoulder, the values of ‘RGP without support’ were significantly higher than those of GMP and ‘RGP with support’ since V2 (after

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Fig. 5. Subjective fatigue ratings for shoulder, upper arm, forearm, and wrist and hand.

approximately 30 min) and V3 (after approximately 45 min), respectively. The subjective fatigue for shoulder of ‘RGP with support’ became greater than that of GMP from the V4 block (after approximately 1 h). For the case of upper arm, the fatigue ratings of ‘RGP without support’ were significantly higher than those of GMP and ‘RGP with support’ since V3 (after approximately 45 min) and V5 (approximately 75 min), respectively. The GMP and the ‘RGP with support’ conditions showed no significant difference in terms of the subjective fatigue for all measured parts. Additionally, the subjective fatigue ratings of ‘RGP without support’ were significantly greater than those of ‘RGP with support’ and GMP only in V5s (after approximately 75 min) of the forearm and the wrist and hand cases, respectively. 3.3. Performance measures 3.3.1. Mean movement time The mean movement time in each condition was presented in Fig. 6. The ANOVA results revealed that the conditions were significantly different in terms of the mean movement time (F(2, 717) ¼ 1303.899; p-value < 0.001). Also, the results of Scheffe’s post hoc tests showed that the mean movement times of the three

Fig. 6. Mean movement time of each condition.

conditions were all differently grouped (p-values < 0.05). Specifically, the mean movement time of RGPs was higher than that of GMP, and the mean movement time of ‘RGP with support’ was somewhat lower than that of ‘RGP without support’. 3.3.2. Mean error rate As described above, participants had sufficient practice with the air-mouse and the orthopedic appliance. The mean error rates of the conditions were shown in Fig. 7. Similar to the case of the movement time, the mean error rates were significantly different by the conditions (based on the ANOVA results; F(2,717) ¼ 318.063; p-value < 0.001). The results of Scheffe’s post hoc tests also revealed that the three conditions were all differently grouped in terms of the mean error rates. The GMP condition had a relatively lower mean error rate than RGP conditions did. Also, the mean error rate of ‘RGP with support’ was slightly higher than that of ‘RGP without support’, against our expectation. 3.3.3. Throughput The throughput of each condition was displayed in Fig. 8. According to the ANOVA results, the effects of the condition on the throughput were significant (F(2, 717) ¼ 1673.028;

Fig. 7. Mean error rate of each condition.

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results because participants might have some difficulties in evaluating separately their Borg ratings for each link of the upper limb.

Fig. 8. Throughput of each condition.

p-value < 0.001). The results of Scheffe’s post hoc tests showed that the three conditions were all differently grouped. The GMP conditions had a higher throughput level than that of RGP conditions. Also, the throughput of ‘RGP with support’ was slightly better than that of ‘RGP without support’. 4. Discussion Regarding the objectives of this study, our expectations were: (1) performing RGP would cause muscle fatigue in the shoulder and arm more than performing GMP; and (2) the upper-arm support would be effective in reducing the fatigue and performance problems. In this section, the experimental results for the expectations are discussed. 4.1. Fatigue According to the subjective fatigue ratings (Fig. 5), participants seemed to have some difficulties to differentiate the fatigue of specific parts of the upper limb. Moreover, it could be possible that the fatigue of the upper limb extremity has had an incidence on the fatigue of the whole upper limb. However, because physiological data of the upper limb extremity (especially the forearm) have not been measured, it is difficult to examine the association between objective fatigue and subjective fatigue of specific parts. Further studies need to address this issue more deeply. 4.1.1. GMP and RGP without support The experimental results about EMG data indicated that ‘RGP without support’ induced fatigue more than GMP in terms of most of the physiological fatigue indexes for UT and AD. In case of ‘RGP without support’, the normalized UT MPFs, the normalized UT MDFs, and the normalized AD MDFs after 30 min were significantly higher than the corresponding initial values. Moreover, the significant differences between ‘RGP without support’ and GMP were found after it passed about 15 min in terms of the normalized UT MDF and the normalized AD RMS. In participants’ responses to the Borg’s scale, the differences of the subjective fatigues between ‘RGP without support’ and GMP became greater with time. The subjective fatigue ratings in ‘RGP without support’ had higher increasing rates than those in GMP. This tendency was consistently found in all the measured parts (shoulder, upper arm, wrist and hand) except for the forearm. Especially, the differences between ‘RGP without support’ and GMP were observed earlier for shoulder (after about 30 min) and upper arm (after about 45 min) than for wrist and hand (after about 75 min). However, we need to be cautious in interpreting these

4.1.2. Need for RGP with support Murata et al. (2005) could not find an effect of time on fatigue in their 4-h experiment using GMP. However, the experimental results for the GMP condition in our study, the normalized UT RMS and all the subjective fatigue ratings were significantly affected by time. Considering Murata et al.’s (2005) and our results, the time effects on fatigue in GMP can be limited, depending on the experimental settings. In case of ‘RGP with support’, any significant differences among the time-blocks were not found in the fatigue indexes for UT and AD though the effects of time on the subjective fatigue ratings were significant. Similar to the GMP condition, ‘RGP with support’ can be relatively resistant to the fatigue by time. However, all the physiological and subjective fatigue values of ‘RGP without support’ were significantly affected by time. This finding indicates the need for a supporting device to reduce the fatigue occurring as time goes by. In this study, the orthopedic appliance was designed to support an upper arm in RGP. The appliance effectively reduced the fatigue in the RGP condition by supporting the upper arm. As mentioned above, the experimental results showed that all the UT and AD fatigue indexes in ‘RGP with support’ were not significantly affected by time (during the whole experiment process e about 1 h 15 min). The significant differences between ‘RGP with support’ and GMP were found only in the T2 block of the normalized AD MDF. However, all the fatigue indexes except the normalized UT RMS showed that ‘RGP with support’ caused less fatigue than ‘RGP without support’. This could be more easily seen in the normalized UT MPF, the normalized UT MDF, and the normalized AD RMS. The findings indicate that the subjective fatigue can be relieved by using the upper-arm support. The results of the subjective fatigue were similar to those of the physiological fatigue. The subjective fatigue ratings in all conditions had a common pattern in the order, ‘RGP without support’ > ‘RGP with support’ > GMP, for the measured parts. For the shoulder, the differences between ‘RGP without support’ and ‘RGP with support’ became significant at an earlier time (after about 45 min) than those between ‘RGP without support’ and GMP. For the upper arm, ‘RGP without support’ was higher than ‘RGP with support’ and GMP in terms of subjective fatigue ratings. Also, ‘RGP without support’ showed significant increases over ‘RGP with support’ and GMP in the forearm and the writs and hand parts, respectively, only in the last time-block (after about 75 min). The subjective ratings for ‘RGP with support’ could reflect the fatigues occurring when participants wore and adjusted the orthopedic appliance for the upper-arm support e i.e., in the initial values (measured in V0) for all parts. In addition, there are two possible reasons to explain how the upper-arm support effectively reduced the fatigue in shoulder and upper extremity in RGP. First, supporting the upper arm might prevent shaking of the arm e the upper limb movements and the dynamic forces might become smaller. Second, the upper-arm support might distribute the load of the upper extremity to other body parts such as the back, waist, or legs by using the fixed belts and the axial-direction support. 4.2. Performance Previous studies for remote pointing mainly focused on the performance perspectives. For example, MacKenzie and Jusoh (2001) found that an air-mouse had worse performance in the speed and accuracy in completing tasks than a standard mouse, and Myers et al. (2002) demonstrated that the control method using a laser pointer tended to be imprecise, error-prone, and slow. In

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addition, other studies (e.g., Ballagas et al., 2005; Eckert and Moore, 2000; Horn, 1995; Kirstein and Muller, 1998; Olsen and Nielsen, 2001; Winograd and Guimbretiere, 1999) pointed the poor performance of remote pointing techniques. According to the experimental results, the mean movement time and the mean error rate of ‘RGP without support’ increased by approximately 52.5% and 102.3%, respectively, and the throughput decreased by approximately 40.0%, compared with the corresponding values of GMP. Similarly, the mean movement time and the mean error rate of ‘RGP with support’ increased by about 38.2% and 125.4%, and the throughput decreased by about 36.1%. This shows that the overall performance of RGPs was much lower than that of GMP. Especially, ‘RGP without support’ had a lower throughput than GMP. This finding is consistent with those of previous studies mentioned above. Also, ‘RGP with support’ had a higher level of the mean error rate that ‘RGP without support’, but the former one had a higher level of throughput (resulting from the decrease of the mean movement time) than the latter one. This means that anchoring consequently might help improve the overall performance. It was expected that the mean error rate of ‘RGP with support’ would be less than that of ‘RGP without support’ due to the effect of the supporting appliance, but the experimental results were against our intuition. The mean error rate of ‘RGP with support’ was higher by approximately 11.44% than that of ‘RGP without support’. This finding can be explained by the differences in fundamental control mechanisms between the RGP conditions. As the radius from the central axis of control increases, the amplitude and the angular velocity increase. This property eventually can affect the mean movement time and even the accuracy level. In case of ‘RGP without support’, the center of mass was at a point between the elbow and the hand because the upper arm often tended to move backward during the control. However, in case of ‘RGP with support’, the elbow point was fixed and the center of mass was at the elbow e the radius from the central axis of control was greater in ‘RGP with support’ than in ‘RGP without support’. Thus the amplitude and the angular velocity could increase and the accuracy could decrease while performing the tasks. These phenomena could result in the decreased movement time and the increased error rate. 4.3. Orthopedic appliance for upper-arm support The orthopedic appliance to support an upper arm was designed to enable pitch (up and down) and yaw (left and right) movements. To limit the range (e.g., postures, input devices, and experiment time), only the basic posture and device were tested. Though this study fairly well covered RGP environments, further study needs to address other postures and RGP devices for more information. In the same vein, the limited experimental time (about 1 h 15 min for each condition) can restrict our interpretations for RGP because participants’ responses can be strongly affected by their wearing acceptability of the orthopedic appliance beyond 75 min and thus the prolonged results can be different from our experimental results. Just like in VDT environments, users in RGP environments need to have adequate resting times to prevent the fatigue accumulation. However, in many situations where addictive contents in entertainment or efficiency-oriented nature in business are included, users do not properly take rests. Supporting an upper arm can be an alternative for resolving this problem. Also, in RGP conditions, the upper-arm support slightly improved the throughput. This implies that an orthopedic appliance to support an upper arm can reduce fatigue level without a performance loss. Nevertheless, the supporting appliance in this study can be impractical because it was

designed and manufactured in a mechanical manner e the fixed angle could block users’ natural movements. More research needs to be done to develop a more practical and industrially-lucrative appliance to support an upper arm. A wearable intervention using air-pressure methods and lighter materials can be an example. 5. Conclusion As a physiological study, this research tried to link a neuroepsycho physiologic approach to a movement analysis in RGP environments. The findings of this study confirm that RGP has potential fatigue and performance problems. Considering the growth of a potential market for RGP, these problems can be obstacles toward the development of RGP-related industries. As a part of addressing this issue, the orthopedic appliance to support an upper arm was introduced in this study. In RGP environments, this appliance reduced the muscle fatigue in shoulder and upper extremity and slightly improved the performance (i.e., throughput). This basically shows the effectiveness of the orthopedic appliance. Also, our design can be utilized as a basis for developing a better appliance for RGP environments. However, the findings can be limited in understanding the physiological phenomena and generalizing them (e.g., the effectiveness of the orthopedic appliance) because only two muscles without arm and forearm muscles were studies in the experiment. Further study needs to address this issue for more exact interpretation. References Aaras, A., 1994. Relationship between trapezius load and the incidence of musculoskeletal illness in neck and shoulder. International Journal of Industrial Ergonomics 14 (4), 341e348. Aaras, A., Horgen, G., Bjorset, H.-H., Ro, O., Thoresen, M., 1998. Musculoskeletal, visual and psychosocial stress in VDU operators before and after multidisciplinary ergonomic interventions. Applied Ergonomics 29 (5), 335e354. Aaras, A., Horgen, G., Bjorset, H.-H., Ro, O., Walsoe, H., 2001. Musculoskeletal, visual and psychosocial stress in VDU operators before and after multidisciplinary ergonomic interventions. A 6 years prospective study e Part II. Applied Ergonomics 32 (6), 559e571. Alricsson, M., Harms-Ringdahl, K., Schuldt, K., Ekholm, J., Linder, J., 2001. Mobility, muscular strength and endurance in the cervical spine in Swedish Air Force pilots. Aviation Space Environmental Medicine 72 (4), 336e342. Babski-Reeves, K., Stanfield, J., Hughes, L., 2005. Assessment of video display workstation set up on risk factors associated with the development of low back and neck discomfort. International Journal of Industrial Ergonomics 35 (7), 593e604. Ballagas, R., Rohs, M., Sheridan, J., Borchers, J., 2006. The smart phones: a ubiquitous input device. IEEE Pervasive Computing 5 (1), 70e77. Ballagas, R., Rohs, M., Sheridan, J.G., 2005. Sweep and point shoot: phone cambased interactions for large public displays. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Extended Abstract). Balliet, J.A., Dainoff, M.J., Mark, L.S., 1996. The effects of degree of upper arm flexion on shoulder-neck discomfort at the VDT. International Journal of HumanComputer Interaction 8 (4), 385e399. Borg, G., 1982. Psychophysical bases of perceived exertion. Medicine of Science in Sports and Exercise 14 (5), 377e381. Borg, G., 1990. Psychophysical scaling with applications in physical work and the perception of exertion. Scandinavian Journal of Work and Environmental Health 16 (Suppl. 1), 55e58. Choobineh, A., Motamedzade, M., Kazemi, M., Moghimbeigi, A., Pahlavian, A.H., 2011. The impact of ergonomics intervention on psychosocial factors and musculoskeletal symptoms among office workers. International Journal of Industrial Ergonomics 41 (6), 671e676. Cockburn, A., Quinn, P., Gutwin, C., Ramos, G., Looser, J., 2011. Air pointing: design and evaluation of spatial target acquisition with and without visual feedback. International Journal of Human-Computer Studies 69 (6), 401e414. Cook, C., Burgess-Limerick, R., 2001. Forearm support and computer keyboard use. In: Proceedings of the 38th Annual Conference of the Ergonomics Society of Australia and the Safety Institute of Australia, Sydney, Australia. Cook, C., Burgess-Limerick, R., 2002. Forearm support for intensive computer users: a field study. In: Proceedings of the HF 2002 Human Factors Conference, Melbourne, Australia. Cook, C.J., Kothiyal, K., 1998. Influence of mouse position on muscular activity in the neck, shoulder and arm in computer users. Applied Ergonomics 29 (6), 439e443. Crenshaw, A.G., Komandur, S., Johnson, P.W., 2010. Finger flexor contractile properties and hemodynamics following a sustained submaximal contraction:

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