2012 University of Technology Eindhoven

HTI Research project Student : Ing. Nick Ebert Student number: 0662869 Supervisors : Dr. Ir. Raymond Cuijpers Elena Torta (MSc) Version : Final

Exercising more with a humanoid robot Alternating frequency amplitude ratio of a embodied agent as feedback cue during health exercises

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Table of contents Introduction........................................................................................................................................3

Methods .............................................................................................................................................6

Task ................................................................................................................................................6

Participants .....................................................................................................................................6

Apparatus .......................................................................................................................................6

Experiment design ..........................................................................................................................8

Experiment procedure ....................................................................................................................9

Data analysis ................................................................................................................................. 10

Results .......................................................................................................................................... 11

Discussion and conclusion ................................................................................................................. 27

References ........................................................................................................................................ 29

Appendix 1 Godspeed questionnaire................................................................................................. 31

Appendix 2 between exercises questionnaire ................................................................................... 33

Appendix 3 robot amplitude and speed multiplication ...................................................................... 34

Appendix 4 Periodicity extraction...................................................................................................... 35

appendix 5 Pythagoras proposition ................................................................................................... 36

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Introduction A significant amount of scientific research indicate that periodical engagement in physical activity results in various kinds of mental, social and physical benefits for people of all ages and conditions. Furthermore, it also would lead to significant economical advantages, like reduced health care costs and elderly to live longer autonomously.

Engineers and scientists are increasingly focusing on sports and physical exercise activities. Current work can be divided in (Buttussi, Chittaro, & Nadalutti, 2006):

• Computer-supported physical games • Virtual (embodied) trainers • Mobile application and devices for physical activities

Mobile applications and devices (especially in combination with a virtual trainer) have great potential in accomplishing a more healthy lifestyle, as users can be assisted at every location and time. The benefit of a virtual trainer in comparison to for example pre-recorded human trainers performing exercises is the fact that animations can be interactively explored by the user (e.g. replaying of a specific exercise from a different angle). However a major limitation of these kind of products is that the interaction with the user is not quite the same as with a real human trainer.

This disadvantage could be reduced, or even removed by a real embodied trainer in form of a robot. Therefore, it is worthwhile to look at robotic training in more detail. This study is part of the European Union project KSERA (knowledgeable service robots for aging) which aims at introducing socially assistive robots in Ambient Assisted Living (AAL) applications in order to allow elderly people to live at home longer and more independently (KSERA, 2012). One of the aims of the project is to reach this goal with the ‘health through exercise’ scenario.

However, for the user to accept this kind of companionship, any agent should have a basic level of social intelligence. Indeed, this kind of intelligence is considered to be a essential part of human intelligence, and therefore a prerequisite of any artificially intelligent robot (Dautenhann, 2007). To accomplish this, the robot should follow a set of heuristics or guidelines when interacting with the user, which tells it how to behave and communicate. These heuristics or guidelines are also known as the robotiquette (Dautenhann, 2007). In a first attempt to find the most efficient embodied agent feedback in order to improve health exercise performance, one should consider the following matter.

When interacting with the user, the robot can enhance its persuasiveness significantly with common human-human communications like the use of gazing and gestures (Ham, Cuijpers, van der Pol, & Cabibihan, 2010). These kind of simple ‘tricks’ of a robot rely on inherently human tendency to attribute goals and intentions to even the simplest physical mobile entities (Feil-Seifer & Materic, 2005). A way to assess the attitude towards a robots is to use the Godspeed questionnaire. The Godspeed questionnaire covers four different themes: anthropomorphism, animacy, likeability and perceived intelligence of robots (Bartneck, Croft, Kulic, & Zoghbi, 2009).

Another possible exercise enhancer is giving feedback about exercise performance. As people respond differently to diverse type of motivation and encouragement, the user should be able to switch to his preferred coach profile (e.g. soft woman voice or more strict drill sergeant tone) (Asselin, Ortiz, Pui, & Smailagic, 2005). Verbal negative feedback of the robot should be given gently

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as inciting aggressively is not as positive evaluated as one would expect (IJsselsteijn, de Kort, Westerink, de Jager, & Bonants, 2004).

Furthermore, bodily measures about performance could be fed back to the user. However, heart rate feedback only is not likely to work, as users could get problems with their perception of control (IJsselsteijn, de Kort, Westerink, de Jager, & Bonants, 2004). Significantly more success is expected from heart rate feedback in combination with burned calories or other benefits of the conducted exercise (Buttussi et al., 2006). Also, documenting exercise data and tracking user progress increases exercise adherence (Castro & King, 2002). It is considered useful in combination with personal trainer (Morey et al., 2003).

Adding a game element in the exercise has the potentional to enhances exercise performance. For example, Hämäläinen, Ilmonen, Höysniemi, Lindholm, & Nykänen ( 2005) acquired user images and movements through a camera and embedded them into a 3D world shown on a beamer. Experts and novice preferred exaggeration of user jumps during aerobic exercises. The physical effort performed by the participants during this study is useful for fitness purposes, since the median of users’ heart rates was at 90% of their median maximum heart rate and values between 70% and 90% are recommended for fitness training (Buttussi et al., 2006).

When focusing on this exaggeration of movement, the following can be said about arm movement. Persons tend to do large amplitude arm movements at low frequency with shoulder and elbow rotation and small amplitude movements at high frequency with wrist and finger rotation (Bosga, Meulenbroek, & Rosenbaum, 2005). When persons are forced to depart from this pattern it results in more attention-demanding control regimes (Bosga, Meulenbroek, & Rosenbaum, 2005). In other words there is a inverse relationship between amplitude and frequency in this context (Bosga, Meulenbroek, & Rosenbaum, 2005).

Related work was done by Asselin et al (2005), which tested a real-time and partly mobile virtual trainer, named the Personal Wellness Coach. The main concept was to add the advantages of a personal fitness trainer to wearable exercise products. Participants were equipped with a wearable system to track movement and monitor heart rate. Feedback was given real time on a custom made dashboard displayed on a laptop. There was maximum 9 meters distance allowed between the participant and the laptop in order for the system to work correctly. Users responded positively to the system and consider it to be helpful for their exercise routine. The obvious limitation was the laptop, as this was considered not to be portable enough.

Also, Buttussi et al (2006) were the first to employ a mobile virtual embodied trainer for fitness activities. They showed the trainer on a PDA strapped to wrist of participants. Results encourages the use of mobile guides and embodied virtual trainers in (outdoor) fitness applications. The limitation was the strapped device to the wrist, as this may not be comfortable enough during exercise.

Furthermore, IJsselstein et al (2004) investigated the effects of coaching by a virtual agent on intrinsic motivation of participants cycling on a stationary home exercise bike. The virtual coach significantly lowered the perceived control and pressure/tension dimensions of intrinsic motivation, but did not affect the enjoyment dimension. The presence of the virtual coach also reduced negative effects associated with virtual environment. The participants had problems with the perceived

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control when only heart rate was fed back. Buttussi et al (2006) suggest to also notify participants about burned calories and other benefits of physical activity.

In this study we investigate how exaggeration of movements helps improve performance during health exercises. For this purpose we vary the amplitude and speed of the robot’s movements who acts as a health instructor. Is changing robot amplitude-speed ratio a suitable feedback cue during health exercises?

A suitable feedback cue, in terms of the KSERA project, would be one which improve user experience and interaction with a robot during the health exercise. We expect that exercising with a robot will improve attitudes towards robots.

A suitable feedback cue should also enhance (or at least maintain a sufficient level of) physical performance. Based on Bosga and collegues (2005), we expect that participants cope with high frequency movements by decreasing their movement amplitude. We hypothesize that the movement become exhaustive (and thus reaches a sufficient physical workload) when this phenomenon occurs.

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Methods

Task The humanoid robot Nao displayed several different health exercises. The participant’s goal was to copy the robot movement as accurate as possible. To give the participant time to adapt to the exercise, every speed-amplitude combination was repeated three times with a four second pause in between. After every speed-amplitude combination, participants were asked to fill in a short questionnaire (see appendix 2 for the between exercise questionnaire).

Performance of the participants was measured by amplitude of participant movement (vertical peak to peak distance) and speed of participant movement (horizontal peak to peak distance).

Participants The total of 16 participants were mainly residents of the municipality of Eindhoven. As the KSERA project aims on elderly people, one of the requirements for participation was a minimum age of 55 years old.

The gender distribution was as followed: there were 9 males and 7 females. Participant age varies between 57 years and 79 years (M=65,75 , SD= 5,927 ).

All participants had normal, or corrected to normal eye vision. Also none of the participants had a physical or mental disability.

Apparatus The robot used for performing the exercises was a 57 cm tall humanoid robot with 25 degrees of freedom (DOF) named Nao (see figure 1), developed by Aldebaran Robotics, France.

Figure 1: Robot Nao

The device to track and record participants movement was a Xbox Kinect (see figure 2) developed by Microsoft corporation, USA. It uses 3D depth sensors and a RGB camera and allows real-time, markless full body 3D motion capture in a room environment. The Kinect uses a single depth image

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with the 3D locations of each skeletral joint of interest. The 3D joint locations are converted to arm motion patterns by the ROS environment.

Figure 2: Xbox Kinect

The Kinect motion sensor provides directly preprocessed position data. For this study the position of the right hand in respect to the left hand of the participant was used to compute the distance between these two body parts. Pythagoras proposition was used to do so (see appendix 5).

The recordings of participants via the Kinect was process with a Matlab script. This script was able to detected upper and lower peaks in order to measure participant amplitude movement, see also figure 3.

Figure 3:processed Kinect data by Matlab. Red crosses indicate detected upper peaks and green crosses indicate detected lower peaks. Displayed are the exercise blocks of 9 different speed-amplitude conditions of one basic exercise of one participant. From upper left to lower right is m1 till m9 of one basic exercise.

The mean of the differences between these lower and upper peaks provided the amplitude in order to compare conditions with a repeated measures ANOVA and regression analysis.

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Participants arm movement periodicity was computed with a Fast Fourier Transform and horizontal peak to peak distance, see appendix 4 for details.

Experiment design This study used a within subjects design. There were three independent variables: amplitude (with 3

levels: small, medium and large), speed (with three levels: slow, medium and fast) and basic exercise (2 different exercises), see table 1.

Table 1: 9 different amplitude-speed combinations (M1-M9) for every basic exercise

Amplitude/Speed Slow Medium Fast Small m1 m2 m3 Medium m4 m5 m6 Large m7 m8 m9

The basic exercises consist of arm movements only.

A overview of the 2 different exercises can be found in figure 4. Every basic exercise is subject to all the combinations of the speed and amplitude values of table 1. Please note that participants were also exposed to 2 additional basic exercises (exercise 2 & 4) which are not part of this study and therefore not reported in the result section. To summarize, participants were exposed to 9 amplitude-speed combinations x (2+2 additional) arm movements = 36 different conditions. See appendix 3 for details about the values of speed and amplitude multiplication.

figure 4: overview of the two basic exercises - exercises shown are in the largest amplitude level

Both quantitative and qualitative dependent measures were used. The quantitative dependent variables are: (distance between hands as) amplitude of user movement and periodicity of user movement.

The qualitative dependent measures were measured using questionnaires which participants were asked to fill in after each block of exercises. Questions used a five-point Likert scale to measure subjective rating of tiredness of the exercise and participant perception of robot speed and amplitude after every block of exercises (see appendix 2). At the beginning and end of the

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experiment measurements were taken, also by means of questions using a five-point Likert scale, to access any possible change in the overall attitude towards robots.

Experiment procedure Participants were welcomed and asked to sign the consent inform and fill in the first questionnaire which contained questions on demographics and on overall perception of robots. Also, below these questions a short introduction of the experiment could be read. After the participant was done the experiment leader showed the experiment setup and the location were the participant would do his or her exercises. This location was marked with a cross on the floor, see also figure 5. The distance between the robot and the participant was 2 meters.

Figure 5: experiment set-up, the cross indicated the location of the participant during the exercise blocks.

The robot started with a training exercise which consisted of a exercise with 4 repetitions. After the training exercise, the experiment leader entered the room and explained or clarified certain points if necessary to assure that the participant understood the task.

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In table 2, the sequence of the experiment is summarized. As described earlier 2 additional basic exercises with their amplitude-speed combinations were also presented to the participants. These were not part of this study and therefore not included in the result section. However, for the completeness of this section, there are included here.

In table 2, note that j represents one amplitude-speed combination of one basic arm movement and thus runs till 36 (9 amplitude-speed combination x (2+2 additional) basic exercises). Each block had 3 sessions with 4 repetitions, the pause between a session was 4 seconds. Each block ended with a survey. After the last block (the 36th) a additional closure questionnaire was presented to the participant. The duration of the whole experiment including introduction and closure was about an hour.

Table 2: experiment logic

block task content

introduction Survey (demographics + Godspeed) 29 questions

Explanation of task

Training training exercise 4 repetitions

block 1 exercise n 4 repetitions

exercise n 4 repetitions

exercise n 4 repetitions

survey 9 questions

block 2 exercise i 4 repetitions

exercise i 4 repetitions

exercise i 4 repetitions

survey 9 questions

block j exercise k 4 repetitions

exercise k 4 repetitions

exercise k 4 repetitions

survey 9 questions

closure Godspeed survey 24 questions

Data analysis Statistical data analysis was conducted with Repeated Measures Anova with succeeding post-hoc and planned comparison tests. Also, for more detailed insight (multiple) regression analysis were performed.

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Results This section is split in two parts, first the quantitative data (participant amplitude scores as function of robot speed and amplitude and participant speed scores as function of robot speed and amplitude) is reported. The second part will describe the qualitative (survey) data.

Quantitative data - participant amplitude scores as function of robot speed and amplitude

Figure 6 shows the mean percentages of maximum hand distances of participants as function of the 9 speed-amplitude combinations of exercise 1. As can be seen in this figure, increase of robot movement amplitude results in significant arm movement increase of the participant. Also, none of the differences within a robot amplitude level differ much.

Figure 6: Error bars with 95 % CI of mean maximum distance between left and right hand of participants for exercise 1 per exercise amplitude-speed combination

Results of the repeated measures ANOVA for participants amplitude scores are given in table 3.

A significant main effect of robot amplitude on participants amplitude scores was found (p<0,001). Robot speed did not reach a significant statistical level, nor was there an interaction effect (p<0,654 and p<0,427 respectively).

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Table 3: Results of the RM ANOVA on the quantitative data - participants amplitude scores. The Huynh-Feldt correction was used for the cases where the sphericity assumption was violated.

amplitude change df df error F p

correction exercise 1 Speed 1,236 14,834 0,437 0,542

Huynh-Feldt

Amplitude 2 24 66,531 0,000 * none Speed*Amplitude 2,394 28,728 0,915 0,427

Huynh-Feldt

exercise 3 Speed 2 24 2,252 0,127

none

Amplitude 1,536 18,429 83,252 0,000 * Huynh-Feldt Speed*Amplitude 3,008 36,094 2,040 0,125

Huynh-Feldt

Bonferroni post hoc tests showed significant statistical differences for the speed level slow between all groups, indicated by blue columns in figure 6 (p< 0,001 for all tests, except between robot amplitude medium and large for this speed level, p< 0,009). The same results were found for the speed level fast, indicated by the green columns in figure 6 (p<0,001 for all tests, except between medium and large robot amplitude of this speed level, p<0,025). For the speed level medium, indicated by red columns in figure 6, differences between all groups were significant (p<0,001 for all tests) except between robot amplitude medium and large of this speed level. However, planned comparisons employing orthogonal contrasts also revealed a significant difference between these two robot amplitudes (p<0,05).

Bonferroni post hoc tests showed that none of the differences within a robot amplitude level were statistical significant.

To assess whether an inverse amplitude/frequency relation exists a multiple regression analysis was conducted. Regression results indicate the following:

Robot amplitude level significantly predicts participants mean maximum hand distance scores, B=0,218, t(133)=12,384, p < 0,001. Robot amplitude level also explains a significant proportion of variance in participant maximum hand distance scores, R2=54,1%.

Robot speed level does not significantly predict participant maximum hand distance score, this means that there is no effect of robot speed on a participant maximum hand distance score.

Equation 1 shows the multiple regression equation which represent percentage of participants maximum hand distance score of exercise 1. For robot amplitude possible x values are -1 for small amplitude, 0 for medium amplitude and 1 for large amplitude.

𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 𝑜𝑓 𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑛𝑡 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 ℎ𝑎𝑛𝑑 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = (0,686 ± 0,014) + (0,218 ± 0,018) ∙𝑋𝑟𝑜𝑏𝑜𝑡 𝑎𝑚𝑝 [1]

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Figure 7 shows the mean percentages of maximum hand distances of participants as function of the 9 speed-amplitude combinations of exercise 3. As can be seen in this figure, increase of robot movement amplitude results in arm movement increase of the participant. Also, the maximum distance between hands decreases within the large robot amplitude level.

Figure 7: Error bars with 95 % CI of mean maximum distance between left and right hand of participants for exercise 3 per robot amplitude-speed combination A significant main effect of robot amplitude on participants amplitude scores was found for exercise 3 (p<0,001). Robot speed and the interaction between robot speed and robot amplitude did not reach significant statistical levels (p<0,127 and p< 0,125 respectively, see also table 3).

Bonferroni post hoc tests showed significant statistical differences for the speed level slow between all groups, indicated by blue columns in figure 7 (p<0,001). The same results were found for the speed level medium (p<0,001) and fast (p<0,001) indicated by red and green columns in figure 7 respectively. We also found a significant difference between robot speed slow and fast in the large robot amplitude level (p<0,001). This indicates that participants, during the large amplitude conditions, moved their arms less far apart when the robot was moving fast in comparison to when it was moving slow.

The differences within the robot small and medium amplitude levels were not statistical significant.

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To assess again whether an inverse amplitude/frequency relation exists a multiple regression analysis was conducted. Regression results indicate the following:

Robot amplitude level significantly predicts participant maximum hand distance scores, Β =0,206, t(125)=15,885, p < 0,001. Robot amplitude level also explained a significant proportion of variance in participant maximum hand distance scores, R2=67,5%.

Robot speed level does not significantly predicts participant maximum hand distance scores, this means that there is no effect of robot speed on a participant maximum hand distance score.

Equation 2 shows the multiple regression equation which represent percentage of participants maximum hand distance score of exercise 3. For robot amplitude possible x values are -1 for small amplitude, 0 for medium amplitude and 1 for large amplitude.

𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 𝑜𝑓 𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑛𝑡 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 ℎ𝑎𝑛𝑑 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = (0,763 ± 0,011) + (0,206 ± 0,013) ∙𝑋𝑟𝑜𝑏𝑜𝑡 𝑎𝑚𝑝 + (−0,019 ± 0,016) ∙ 𝑋𝑟𝑜𝑏𝑜𝑡 𝑎𝑚𝑝 ∙ 𝑋𝑟𝑜𝑏𝑜𝑡 𝑠𝑝𝑒𝑒𝑑 [2]

Equation 2 shows a inverse amplitude/frequency relation via a interaction effect, indicating that with higher speeds, participant maximum hand distance decreases.

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Quantitative data - participant periodicity scores as function of robot speed and amplitude

Figure 8 shows the periodicity of participants per amplitude-speed combination of exercise 1. Decrease of robot movement periodicity results in movement periodicity decrease of the participant. Periodicity means within robot speed levels seems not to differ.

Figure 8: mean periodicity of participants for exercise 1 per robot amplitude-speed combination

The repeated measures ANOVA of table 4 reveals statistical main effect for robot speed for exercise 1 (p<0,001). Furthermore, the main effect of robot amplitude and the interaction effect were not statistical significant (p<0,763 and p<0,641 respectively).

Table 4: Results of the RM ANOVA on the quantitative data - participant periodicity. For measurements where the sphericity assumption was violated, the Huynh-Feldt correction was applied.

Participant periodicity df df error F p

correction exercise 1 Robot speed 1,102 8,816 515,114 0,000 * Huynh-Feldt Robot amplitude 2 16 0,275 0,763

none

Speed*Amplitude 2,384 19,068 0,509 0,641

Huynh-Feldt exercise 3 Robot speed 2 20 2886,382 0,000 * Huynh-Feldt Robot amplitude 2 20 1,584 0,23

Huynh-Feldt

Speed*Amplitude 3,805 38,047 2,437 0,066

Huynh-Feldt

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Bonferonni post hoc tests revealed for the small amplitude level (indicated by blue columns in figure 8) significant differences between all groups (p < 0.001 for all tests). The same results were found for the medium and large amplitude level (indicated by red columns and green columns in figure 8 respectively). Bonferonni post hoc tests showed that values within a robot speed level do not differ significantly.

Figure 10 shows the periodicity of participants per amplitude-speed combination of exercise 3. Decrease of robot movement periodicity results in movement periodicity decrease of the participant. Periodicity means within robot speed levels seems not to differ.

Figure 10: mean periodicity of participants for exercise 3 per exercise movement The repeated measures ANOVA of table 4 reveals statistical main effect for robot speed for exercise 3 (p<0,001). Furthermore, the main effect of robot amplitude and the interaction effect was not statistical significant (p<0,23 and p<0,07 respectively). For measurements where the sphericity assumption was violated, the Huynh-Feldt correction was applied.

Bonferonni post hoc tests revealed for the small amplitude level (indicated by blue columns in figure 10) significant differences between all groups (p < 0.001 for all tests). The same results were found for the medium and large amplitude level (indicated by red columns and green columns in figure 10 respectively). Also, Bonferonni post hoc tests revealed a significant difference between robot amplitude small and large in the fast robot speed level (p<0,05). This indicates that participants mean period time was slightly smaller (0,092 seconds) in the large amplitude condition in comparison to the small amplitude condition in the high robot speed level. Differences in the robot speed level slow and medium were not statistical significant.

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This result section will continue with the qualitative measurements results.

Qualitative data - speed perception

In figure 11 the perceived speed of exercise 1, which is expressed on a Likert scale from 1 to 5, is plotted for each robot amplitude and robot speed.

Figure 11: error bars with 95 % CI of perceived speed of exercise 1. Ordinal scores with -2 perceived as very slow, 0 as neutral and 2 as very fast.

Results of the repeated measures ANOVA of table 5 showed a significant main effect for robot speed and robot amplitude for exercise 1. There was no significant interaction effect.

Table 5: results of the repeated measures ANOVA of robot speed perception. The Huynh-Feldt correction was used for the cases where the sphericity assumption was violated.

Speed perception df df error F p

correction exercise 1 Robot speed 2 28 93,617 0,000 * none Robot amplitude 1,559 21,83 18,32 0,000 * Huynh-Feldt Speed * amplitude 2,95 41,306 0,56 0,641

Huynh-Feldt

exercise 3 Robot speed 2 26 56,95 0,000 * none Robot amplitude 1,38 18,03 10,51 0,002 * Huynh-Feldt Speed * amplitude 4 52 0,83 0,507

none

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Bonferroni post hoc tests revealed significant differences between all groups of the small robot amplitude level, indicated left on the horizontal axis of figure 11 (p < 0.001 for all tests, except between the medium and fast robot speed of this amplitude level, p<0.01 for this difference). These results were also found for the medium amplitude level, indicated in the middle of the horizontal axis of figure 11 (p < 0.001 for all tests) and the large amplitude level, indicated right on the horizontal axis of figure 11 (p < 0.001 for all tests). To quantify the effect size, we performed a linear regression analysis with robot speed and robot amplitude as dummy variables. For robot amplitude x values are -1 for small amplitude, 0 for medium amplitude and 1 for large amplitude. For robot speed x values are -1 for slow speed, 0 for medium speed and 1 for fast speed (see equation 3). Robot amplitude level significantly predicts participant perceived speed scores, Β =0,393, t(134)=6,166 , p < 0,001. Robot amplitude level also explained a significant proportion of variance in participant perceived speed scores. R2=9,4%. Robot speed level significantly predicts participant perceived speed scores, Β =0,0,966, t(143)=15,153, , p < 0,001. Robot Speed level also explained a significant proportion of variance in participant perceived speed scores. R2=56,1%.

𝑃𝑒𝑟𝑐𝑒𝑖𝑣𝑒𝑑 𝑠𝑝𝑒𝑒𝑑 = (−0,289 ± 0,052) + (0,393 ± 0,064) ∙ 𝑋𝑟𝑜𝑏𝑜𝑡 𝑎𝑚𝑝 + (0,966 ± 0,064) ∙𝑋𝑟𝑜𝑏𝑜𝑡 𝑠𝑝𝑒𝑒𝑑 [3]

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In figure 12, the perceived speed of exercise 3, which is expressed on a Likert scale from 1 to 5, is plotted for each robot amplitude and robot speed.

Figure 12: error bars with 95 % CI of perceived speed of exercise 3. Ordinal scores with -2 perceived as very slow, 0 as neutral and 2 as very fast. Results of the repeated measures ANOVA of table 5 showed a significant main effect for robot speed and robot amplitude for exercise 3 (p<0,002). There was no significant interaction effect (p<0,507).

Bonferroni post hoc tests revealed significant differences between all groups of the small robot amplitude level, indicated left on the horizontal axis of figure 12 (p < 0.001 for all tests, except between robot speed slow and medium of this amplitude level, p < 0.033). These results were also found for the large amplitude level, indicated right on the horizontal axis of figure 12 (p < 0.001 for all tests). For the medium robot amplitude level (in the middle of the horizontal axis of figure 12) Bonferroni post hoc tests revealed significant differences between all groups (p < 0.001 for all tests) except between robot speed slow and medium of this amplitude level (ns). However, planned comparisons employing orthogonal contrasts revealed a marginal significant difference between these two (p < 0.064). Again, to quantify the effect size, we performed a linear regression analysis with robot speed and robot amplitude as dummy variables. For robot amplitude x values are -1 for small amplitude, 0 for medium amplitude and 1 for large amplitude. For robot speed x values are -1 for slow speed, 0 for medium speed and 1 for fast speed (see equation 4).

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Robot amplitude level significantly predicts participant perceived speed scores, Β =0,281 , t(134)=3,897, p < 0,001. Robot amplitude level also explained a significant proportion of variance in participant perceived speed scores. R2=5%.

Robot speed level significantly predicts participant perceived score, Β =0,897, t(134)=14,428 , p < 0,001 .Robot Speed level also explained a significant proportion of variance in participant perceived speed scores. R2=51,6%.

𝑃𝑒𝑟𝑐𝑒𝑖𝑣𝑒𝑑 𝑠𝑝𝑒𝑒𝑑 = (−0,252 ± 0,059) + (0,281 ± 0,072) ∙ 𝑋𝑟𝑜𝑏𝑜𝑡 𝑎𝑚𝑝 + (0,987 ± 0,072) ∙𝑋𝑟𝑜𝑏𝑜𝑡 𝑠𝑝𝑒𝑒𝑑 [4]

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Qualitative data - amplitude perception

In figure 13 the perceived amplitude of exercise 1, which is expressed on a Likert scale from 1 to 5, is plotted for each robot amplitude and robot speed.

Figure 13: error bars with 95 % CI of perceived amplitude of exercise 1. Ordinal scores with -2 perceived as very small, 0 as neutral and 2 as very large. Results of the repeated measures ANOVA of table 6 show a significant main effect for robot amplitude for exercise 1 (p<0,001). There was no significant main effect for robot speed and also no interaction effect (p<0,16 and p<0,638 respectively).

Table 6: results of the repeated measures ANOVA of robot amplitude perception. The Huynh-Feldt correction was used for the cases where the sphericity assumption was violated.

Amplitude perception df df error F p

correction exercise 1 Robot speed 2 28 1,961 0,16

none

Robot amplitude 1,501 21,021 27,47 0,000 * Huynh-Feldt Speed * Amplitude 3,279 45,905 0,591 0,638

Huynh-Feldt

exercise 3 Robot speed 1,536 19,968 0,113 0,842

Huynh-Feldt

Robot amplitude 2 26 100,133 0,000 * none Speed * Amplitude 2,595 33,729 0,489 0,666

Huynh-Feldt

-2

-1.5

-1

-0.5

0

0.5

1

Mea

n pe

rcei

ved

ampl

itude

exe

rcise

1

Robot amplitude

Robot speed

Slow

Medium

Fast

Small Medium Large

22

Bonferroni post hoc tests revealed significant differences between all groups of the slow robot speed level, indicated by blue columns in figure 13 (p < 0.001 for all tests, except between robot amplitude medium and large for this speed level , p < 0.002 for this difference). The same results were found for the medium robot speed level, indicated by red columns in figure 13 (p < 0.001 for all tests, except between robot amplitude medium and large for this speed level, p < 0.005 for this difference) and for the fast robot speed level, indicated by green columns in figure 13 ( p < 0,008 between robot amplitude small and medium, p < 0,002 between robot amplitude medium and large and p < 0.001 between robot amplitude small and large). Also for amplitude perception, to quantify the effect size, we performed a linear regression analysis with robot speed and robot amplitude as dummy variables. For robot amplitude x values are -1 for small amplitude, 0 for medium amplitude and 1 for large amplitude. For robot speed x values are -1 for slow speed, 0 for medium speed and 1 for fast speed (see equation 5). Robot amplitude level significantly predicts participant perceived amplitude scores, Β =0,825 , t(143)=12,146, p < 0,001. Robot amplitude level also explained a significant proportion of variance in participant perceived amplitude scores. R2=51,2%. Robot speed level does not significantly predicts participant perceived score, Β =0,041, t(143)=0,596 p < 0,552.

𝑃𝑒𝑟𝑐𝑒𝑖𝑣𝑒𝑑 𝑎𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 = (−0,642 ± 0,055) + (0,825 ± 0,068) ∙ 𝑋𝑟𝑜𝑏𝑜𝑡 𝑎𝑚𝑝[5]

This means that there is no effect of robot speed on participants perceived robot arm amplitude.

23

In figure 14 the perceived amplitude of exercise 3, which is expressed on a Likert scale from 1 to 5, is plotted for each robot amplitude and robot speed.

Figure 14: error bars with 95 % CI of perceived amplitude of exercise 3. Ordinal scores with -2 perceived as very small, 0 as neutral and 2 as very large. Results of the repeated measures ANOVA of table 6 show a significant main effect for robot amplitude for exercise 3 (p<0,001). There was no significant main effect for robot speed and also no interaction effect (p<0,842 and p<0,666 respectively).

Bonferroni post hoc tests revealed significant differences between all groups of the slow robot speed level, indicated by blue columns in figure 14 (p < 0.001 for all tests, except between robot amplitude small and medium of this speed level, p < 0.027 for this difference). The same results were found for the medium robot speed level, indicated by red columns in figure 14 (p < 0.001 for all tests, except between robot amplitude small and medium of this speed level, p < 0.006) and for the fast robot speed level, indicated by green columns in figure 14 (p < 0.001 for all tests, except between robot amplitude small and medium for this speed level, p < 0.039 for this difference). Again, to quantify the effect size, we performed a linear regression analysis with robot speed and robot amplitude as dummy variables.

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Mea

n pe

rcei

ved

ampl

itude

exe

rcise

3

Robot amplitude

Robot speed

Slow

Medium

Fast

Small Medium Large

24

Robot amplitude level significantly predicts participant perceived amplitude scores, Β =1,062 , t(134)=15,794, p < 0,001. Robot amplitude level also explained a significant proportion of variance in participant perceived amplitude scores. R2=65,3%. Robot speed level does not significantly predicts participant perceived amplitude score, Β =0,019, t(134)=0,285, p < 0,776. This means that there is no effect of robot speed on participants perceived robot arm amplitude. Equation 6 shows the multiple regression of the robot regarding exercise 3. For robot amplitude x values are -1 for small amplitude, 0 for medium amplitude and 1 for large amplitude. 𝑃𝑒𝑟𝑐𝑒𝑖𝑣𝑒𝑑 𝑎𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 = (−0,234 ± 0,055) + (1,062 ± 0,067) ∙ 𝑋𝑟𝑜𝑏𝑜𝑡 𝑎𝑚𝑝[6]

Qualititive data - subjective tiredness

For the subjective measure of tiredness a repeated measures ANOVA was performed with robot speed and robot amplitude as independent variables.

Results of the repeated measures ANOVA are given in table 7. None of the main or interaction effects of both exercises reached statistical significant levels.

Table 7: Repeated measures ANOVA results of subjective tiredness.The Huynh-Feldt correction was used for the cases where the sphericity assumption was violated.

Subjective tiredness df df error F p correction exercise 1 Robot speed 2 28 0,054 0,947 none Robot amplitude 1,262 17,665 2,67 0,114 Huynh-Feldt Speed * Amplitude 3,118 43,655 1,011 0,399 Huynh-Feldt exercise 3 Robot speed 1,289 16,756 0,573 0,573 Huynh-Feldt Robot amplitude 1,527 19,845 0,934 0,386 Huynh-Feldt Speed * Amplitude 1,489 19,357 1,694 0,212 Huynh-Feldt

Participants rated the exercises as not tiring, regardless of speed and/or amplitude manipulation.

25

Qualitative data - Godspeed questionnaires

An additional subjective measurement was taken in form of the Godspeed questionnaire. This questionnaire was taken before and after the experiment to measure any possible change in the overall perception of participants regarding robots. The Godspeed questionnaire covers four different dimensions: anthropomorphism, animacy, likeability and perceived intelligence of robots (Bartneck, Croft, Kulic, & Zoghbi, 2009).

Figure 15: error bars with 95 % CI of overall attitude towards robots. 0 means that participants did not link robots with the corresponding theme at all. 5 means that participant completely link robots with the corresponding theme.

Figure 15 shows that exercising with the robot increased likeability towards it and that attitudes corresponding with the other three themes seemed to descrease. However, Repeated measures ANOVA (see table 8) showed that only the main effects of likeability and perceived intelligence are significant. The main effects of anthropomorphism and animacy do not reached statistical significant levels.

Table 8: Repeated measures ANOVA results of Godspeed before after questionnaire

Godspeed items df df error F p anthropomorphism 1 15 2,673 0,123

animacy 1 15 2,16 0,162 likeability 1 15 5,787 0,029 *

perceived intelligence 1 15 10,796 0,005 *

1

1.5

2

2.5

3

3.5

4

anthropmorphism animacy likeability perceivedintelligence

Like

rt sc

ale

scor

e

Before

After

26

The anthropomorphism dimension consisted of 6 items (α= 0,734), the animacy dimension consisted of 7 items (α=0,797), the likeability dimension of 5 items (α=0,745), and the perceived intelligence dimension of 5 items (α=0,837).

For the Godspeed item of likeablity, Bonferroni post hoc tests revealed a significant difference between perception before and after the experiment ( p < 0,029). The mean value between before and after the experiment increased with 0,175, which indicate that participants seem to like robots more after the experiment.

Also, for the Godspeed item of perceived intelligence, Bonferroni post hoc tests revealed a significant difference between perceived intelligence before and after the experiment ( p < 0,005). The mean value between before and after the experiment decreased with 0,287, which indicate that participants perceive robots as less intelligent after the experiment.

27

Discussion and conclusion In this study, we investigated in what way people respond to different arm movement amplitude and frequency of a embodied agent during a health exercise, to find an efficient way of proving feedback for the robot in this context. To do so we measured both user attitude towards robots as well as the participants movements.

We expected that participants would like robots more after the experiment indicating that exercising with a robot improves attitudes towards it. We found this to be the cause, however we did also found that participants perceived robots as less intelligent after the experiment in comparison to before. This reduced perception may be due to the fact that the robot went through fixed movements scripts with a minimum amount of social interaction with the participants.

We also found that an increased frequency of movement of the robot resulted in increased frequency movement of the participant. Statistical significant differences for participant periodicity were found between different robot speed values. This effect was found for all the amplitude levels (small, medium and large) for both exercises (1 and 3).

Similarly we found that an increased amplitude movement of the robot results in increased amplitude movement of the participant. Statistical significant differences between participant amplitude were found for the different robot amplitude conditions. This effect was found for all the speed levels (slow, medium and fast) for both exercises (1 and 3). With no exception, the smallest amplitude movement of the robot corresponded to the smallest amplitude movement of the participant. The largest amplitude movement of the robot corresponded to the largest amplitude movement of the participant. These results confirm that people followed the robot movement accurately.

Finally, we found that robot speed has a negative relation with participants amplitude scores for high speeds during Exercise 3. At higher speeds the maximal distance between hands decreased with speed, suggesting that people compensate for the mass inertia of their arms by bending their arms more towards each other (Bosga, Meulenbroek, & Rosenbaum, 2005). This effect was only found in exercise 3. This is because the mass inertia was higher in this exercise in comparison to exercise 1, as result of difference in the position of the arms during work out (see also figure 4). In exercise 3, the centre of gravity is further away from the rotation axis than in exercise 1. In other words, the inverse relationship between arm frequency and arm movement found by Bosga and colleagues (2005), was replicated in this study.

Interestingly, participant’ ratings of tiredness were the same for every exercise and robot speed-amplitude stimuli. All the presented exercise stimuli were, regardless of the difference in speed an amplitude manipulation, rated as not tiring. This could indicate that in a well designed embodied agent health exercise scenario, people can burn more calories without feeling more exhaustive. This would mean that people can reach their exercise goals with minimum amount of mental effort, if adequate feedback is delivered.

28

This leads to a interesting question, does training with a real embodied agent or robot make you forget your exhaustion? Or did we operate at speed and amplitude levels which were to slow/small to be exhaustive? The latter could be possible, however, we did find the by Bosga and colleagues (2005) described inverse relationship between frequency and speed for our most tiring exercise (exercise 3, high amplitude). At higher speeds, participants responded by bending their arms more towards each other in order to cope with this velocity. This could be an indication that the exercises (at least at higher speed) were indeed physical exhausting.

The aim of this study was to assess if increasing robot amplitude and speed are a good exercise performance enhancer. Physical performance in this context was defined as reaching an maintaining a, for the health exercise, desirable movement amplitude or speed and improving attitudes towards robots.

Participants responded to different robot amplitude and speed variations in the expected way and liked robots more after exercising. The mental workload was at a minimum as participants claimed that none of the exercises were tiring, even when we did found signals of physical exhaustion (the inverse frequency-amplitude relation at higher arm speeds).

With the first step taken in assessing if changing amplitude-speed ratio is a good embodied agent feedback cue in a health exercise context, we suggest to put this method to the test in a online fashion. In other words, assess what happens if indeed the robot responses to the user with changing its amplitude-speed ratio. The robot could decrease its speed when the inverse frequency-amplitude relation is detected and increase it when the user does not exhibit this phenomenon for an optimal workout.

To summarize, this study was a successful first attempt to find a suitable robot social feedback in a health exercise context.

29

References Asselin, R., Ortiz, G., Pui, J., & Smailagic, A. (2005). Implementation and Evaluation of the Personal Wellness Coach. IEEE Conference on Distrubed COmputing Systems Workshops.

Atkinson, G., Wilson, D., & Eubank, M. (2004). Effects on music on work-rate distribution during a cycle time trial. International Journal of Sports Medicine, 62 , 413 – 419.

Bartneck, C., Croft, E., Kulic, D., & Zoghbi, S. (2009). Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International Journal of Social Robotics 1 (1) , 71-81.

Bosga, J., Meulenbroek, R. G., & Rosenbaum, D. A. (2005). Deliberate control of continuous motor performance. Journal of motor behaviour, Vol. 37, No. 6 , 437-446.

Buttussi, F., Chittaro, L., & Nadalutti, D. (2006, September 12-15). Bringing Mobile Guides and Fitness Activities Together: A solution based on an embodied virtual trainer. MobileHCI’06 .

Castro, C. M., & King, A. C. (2002). Telephone-assisted counseling for physical activity. Excercise and Sport Sciences Reviews, 30(2) , 64-68.

Dautenhann, K. (2007). Socially intelligent robots: dimensions of human-robot interaction. Philosophical transactions of the royal society B , 679-704.

Feil-Seifer, D., & Materic, M. J. (2005). Defining socially assistive robotics. 9th International Conference on Rehabilitation Robotics. Chicago.

Ham, J., Cuijpers, R., van der Pol, D., & Cabibihan, J.-J. (2010). Making Robots Persuasive: The Influence of Gazing and Gestures by a Storytelling Robot on its Persuasive Power. Eindhoven: University of technology Eindhoven.

Hämäläinen, P., Ilmonen, T., Höysniemi, J., Lindholm, M., & Nykänen, A. (2005). Martial arts in artificial reality. CHI '05 (pp. 781-790). New York: ACM Press.

IJsselsteijn, W., de Kort, Y., Westerink, J., de Jager, M., & Bonants, R. (2004). Fun and Sports: Enhancing the Home Fitness Experience. ICEC 2004: Proceedings of the 3rd International Conference on Entertainment Computing (pp. 46-56). Berlin: Springer.

Karageorghis, C. I., Priest, D.-L., Terry, P. C., & Chatzisarantis, N. L. (2006). Redesign and initial validation of an instrument to assess the motivational qualities of music in exercise: The Brunel Music Rating Inventory-2. Journal of Sports Sciences, 24(8) , 899 – 909.

KSERA. (2012). KSERA Project. Retrieved from http://www.ksera-project.eu/

Morey, M. C., Dubbert, P. M., Doyle, M. E., MacAller, H., Crowley, G. M., Kuchibhatla, M., et al. (2003). From supervised to unsupervised exercise: Factors associated with exercise adherence. Journal of Aging and Physical Activity, 11 , 351-368.

Schneider, S., Askew, C. D., Abel, T., & Strüder, H. K. (2010). Exercise, music, and the brain: Is there a central pattern generator? Journal of Sports Sciences, 28(12) , 1337–1343.

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Szabo, A., Small, A., & Leigh, M. (1999). The effects of slow- and fast-rhythm classical music on progressive cycling to voluntary physical exhaustion. Journal of Sports Medicine and Physical Fitness, 39 , 220 – 225.

Wijnalda, G., Pauws, S., Vignoli, F., & Stuckenschmidt, H. (2005). A Personalized Music System for Motivation in Sport Performance. the IEEE CS and IEEE ComSoc (pp. 10-17). PERVASIVE computing.

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Appendix 1 Godspeed questionnaire Please rate your impression of robots on these scales:

Artificial 1 2 3 4 5

Lifelike 1 Kunstmatig Levensecht Ignorant

1 2 3 4 5 Knowledgeable

2 Onwetend Veel wetend 3 Mechanical

1 2 3 4 5 Organic

Mechanisch Organisch 4 Fake

1 2 3 4 5 Natural

Onecht Natuurlijk 5 Dislike

1 2 3 4 5 Like

Afkeer Geliefd Machinelike

1 2 3 4 5 Humanlike

6 Lijkend op een machine

Lijkend op een mens

7 Awful 1 2 3 4 5

Nice Afschuwelijk Mooi Anxious

1 2 3 4 5 Relaxed

8 Angstig Ontspannen Inert

1 2 3 4 5 Interactive

9 Passief Interactief Unintelligent

1 2 3 4 5 Intelligent

10 Onintelligent Intelligent Quiescent

1 2 3 4 5 Surprised

11 Rustig Verrast Unkind

1 2 3 4 5 Kind

12 Niet lief Lief Unpleasant

1 2 3 4 5 Pleasant

13 Onplezierig Plezierig Unconscious

1 2 3 4 5 Conscious

14 Onbewust

Heeft een bewustzijn

Moving rigidly 1 2 3 4 5

Moving elegantly 15 Houterige

bewegingen Vloeiende bewegingen

Incompetent 1 2 3 4 5

Competent 16 Onbekwaam Bekwaam Stagnant

1 2 3 4 5 Lively

17 Stilstaand Levendig Dead

1 2 3 4 5 Alive

18 Dood Levend

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Please rate your impression of robots on these scales:

Foolish 1 2 3 4 5

Sensible 19 Dwaas Gevoelig Artificial

1 2 3 4 5 Lifelike

20 Kunstmatig Levensecht 21 Irresponsible 1 2 3 4 5 Responsible Onverantwoordelijk Verantwoordelijk 22 Agitated

1 2 3 4 5 Calm

Opgewonden Kalm 23 Unfriendly

1 2 3 4 5 Friendly

Onvriendelijk Vriendelijk 24 Apathetic

1 2 3 4 5 Responsive

Apatisch Responsief

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Appendix 2 between exercises questionnaire

During the last trial, the robot moved fast.

Strongly disagree Disagree Neutral Agree Strongly Agree

I feel rested after this trial.

Strongly disagree Disagree Neutral Agree Strongly Agree

During the last trial, the arm movements of the robot were small.

Strongly disagree Disagree Neutral Agree Strongly Agree

I feel tired after this trial.

Strongly disagree Disagree Neutral Agree Strongly Agree

The robot was quick during the last trial.

Strongly disagree Disagree Neutral Agree Strongly Agree

During the last trial, the arm movements of the robot were big.

Strongly disagree Disagree Neutral Agree Strongly Agree

During the last trial, the robot moved slowly.

Strongly disagree Disagree Neutral Agree Strongly Agree

This last trial was fatiguing (vermoeiend).

Strongly disagree Disagree Neutral Agree Strongly Agree

During the last trial, the arms movement of the robot were substantial (aanzienlijk).

Strongly disagree Disagree Neutral Agree Strongly Agree

34

Appendix 3 robot amplitude and speed multiplication exercise 1

Amplitude/Speed Slow Medium Fast Small 1,2 / 4 1,2 / 2 1,2 / 1 Medium 2 / 4 2 / 2 2 / 1 Large 4 / 4 4 / 2 4 / 1

exercise 3 Amplitude/Speed Slow Medium Fast

Small 1 / 4 1 / 2 1 / 1 Medium 2 / 4 2 / 2 2 / 1 Large 4 / 4 4 / 2 4 / 1

Note: A higher speed multiplication value means a lower movement speed of the robot.

For exercise 1 and 3 indicates a higher amplitude multiplication value a higher amplitude of the robot.

35

Appendix 4 Periodicity extraction For exercise 3, the Fourier transformation was used, see description below. However, for exercise 1, the script did not function properly for unknown reasons. Therefore, for this exercise the horizontal peak to peak distance was used and averaged (see also figure 3, for an indication of this distance).

The Fourier transform coverts a sinewave of a periodic signal (in time domain) to frequency components in the frequency domain (see figure 16 below).

Figure 16: left the original signal (time domain) right the Fourier transformation (frequency domain). Two peaks can be distinguished at 50 Hz and 120 Hz, which indicates that the signal on the left is generated at 50 Hz and 120 Hz.

In case of our participant arm distance data time and frequency variables are discrete (in other words finitive). Therefore the Discrete Fourier Transform was used. This is a numerical approximation to the Fourier transform.

The Matlab script used the Fast Fourier Transform (FFT) which is a effective Discrete Fourier Transform (DFT) and developed by Cooley and Tukey. This algorithm reduces computation time.

The function Y = fftshift(fft(fftshift(x))) was used in Matlab, were x represent the signal of the participants distance between hands in the time domain. Y represents the intensity frequency domain graph (in other words, where this generates a peak a frequency of the signal is detected). fftshift(x) is used because it moves the zero-frequency component to the center of the array. It is useful for visualizing a Fourier transform with the zero-frequency component in the middle of the spectrum.

36

appendix 5 Pythagoras proposition In the Python script designed by Elena Torta (MSc, University of Technology Eindhoven) the Pythagoras proposition for a 3 dimensional grid was used to calculate the distance between hands. This distance is indicated with BH in figure 17.

Figure 17: 3 dimensional grid

BH is calculated with the following equation:

𝑑(𝐵,𝐻) = �𝑎2 + 𝑏2 + 𝑐2