Indian Institute of Technology Guwahati

ICT Intervention for
Improvisation of Maternal
Healthcare in Assam
Design Project...
Acronyms Used –
NRHM – National Rural Health Mission
ANM – Auxiliary Nurse Mid-wife
ASHA – Accredited Social Health Activi...
Figures and Images Used
Fig.1. - ANMs at Moriyapati sub-center
Fig.2. - A set of static gestures (Paper 4)
Fig.3. - An ana...
Index
Brief History (Design Project II)

5

Studying Research Papers on Gesture
Based Systems
6
Critical Appraisal

10

Me...
Project History
(Design Project I)
According to the Sample Registration
Services (SRS) 2004-2006, the MMR* for
Assam was 4...
Studying Research Papers on
Gesture Based Systems

are like slapping the phone to mute the
ring tone.

Following papers we...
This paper uses Wiimote to uncover the
user’s cultural background by analyzing
his patterns of gestural expressivity in a
...
The classification results are used along
with the feature vector to generate a
combination of sounds and images that
chan...
8. Kinect in the Kitchen: Testing
Depth Camera Interactions in
Practical Home Environment
Research takes the Kinect into r...
Critical Appraisal:
Our research showed that no work had
been done in the area of gesture based
systems for people in rura...
Methodology for Designing
Gestural User Interfaces

Identify right functions (e.g. stop,
play, pause etc.) – 1.0

Based on...
It is important to design the
experiments in a way that users use the
gestures in a natural way.
User’s social acceptance:...
Gestures are hidden but feedback is
revealed
• Deictic, propositional etc. more new
forms can be identified and classified...
•

•

Q1- What would you think if you
saw someone else performing
this gesture (for example, when
walking down the street)...
Building the gesture vocabulary
3D Gesture Documentation was an
attempt to provide an overview of
possible 3D gestures, wh...
Experiment
Ms Sumitha Sharma visited the EI Lab
from Speech Based and Pervasive
Interaction Group, Tampere Unit for
Comput...
output. Participants were asked to step
in between two lines marked on the
floor, 1 meter apart and three meters
away from...
a. Of the two selection methods,
which one did they prefer and
why?
b. Give one positive feedback and
one negative feedbac...
Traditionally, female users in India wear
sari and salwar-kamiz for their daily
routine. Low literate users (old & young)
...
Following tables and graphs show the analysis of the study. Fig 12 shows the table which has all the data collected from t...
Fig.13. Complete data obtained from the user initially

21
In the following table, the cognitive load and Raw TLX for each user’s touching and pointing has been calculated.

Fig.14....
The above shows the mean and standard deviation for all the parameters. It is observed that the values of mean and standar...
Fig.16. Bar graph showing the preferences of users of different
categories. It shows that touching is majorly preferred ov...
Following were a few user statements 




Pointing could be controlled
better (ML).
Preferred touching body part to
poi...
Fig.18. Graph showing the distribution of cognitive load with age.

26
Fig.19. Graph showing the distribution of cognitive load with education.

27
Conclusion
Based on the study conducted above, the
methodology proposed was refined. It
will now be used in the subsequent...
Brashear,Thad Starner, Harley Hamilton,
Peter Presti

29
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Project Report, Design Project 2 - ICT Intervention for Improvisation of Maternal Healthcare in Assam

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Project Report, Design Project 2 - ICT Intervention for Improvisation of Maternal Healthcare in Assam

  1. 1. Indian Institute of Technology Guwahati ICT Intervention for Improvisation of Maternal Healthcare in Assam Design Project II Minal Jain (10020526) Mannu Amrit (10020523) Guide- Prof. Keyur Sorathia Course Co-ordinator – Asst. Prof Abinash Kumar Swain In collaboration with IBM Research & EI Lab, IIT Guwahati 1
  2. 2. Acronyms Used – NRHM – National Rural Health Mission ANM – Auxiliary Nurse Mid-wife ASHA – Accredited Social Health Activist PHC – Primary Health Center MO – Medical Officer ICT – Information and Communication Technology CHC – Community Health Center PW – Pregnant Woman ANC – Antenatal Care TLX – Task Load Index ML – Male Literate MLL – Male Low Literate MOL – Male Old Literate MOLL – Male Old Low Literate FL – Female Literate FLL – Female Low Literate FOL – Female Old Literate FOLL – Female Old Low Literate 2
  3. 3. Figures and Images Used Fig.1. - ANMs at Moriyapati sub-center Fig.2. - A set of static gestures (Paper 4) Fig.3. - An analysis of the gestures (paper 4) Fig.4. - Head sideways (right) to scroll to the right Fig.5. - Right leg upwards to select or results Fig.13. - Complete data obtained from the user initially Fig.14. - Cognitive Load and Raw TLX values for all the users have been calculated Fig.15. - Comparatives analysis of the rating of mental demand, temporal demand, physical demand, confirm a choice on right side; performance, effort and frustration horizontal scrolling from right side. for pointing and touching. Fig.6. - Fly right to select or confirm a Fig.16. - Bar graph showing the preferences choice on right side; horizontal of users of different categories. It scroll on right shows that touching is majorly Fig.7. - A screenshot of the system screen preferred over pointing Fig.8. - Observations being made from the Fig.17. - Bar graph showing the weight usability room Fig.9. - Distribution of user groups Fig.10. - User performing the tasks Fig.11. - User performing the task Fig.12. - Complete data obtained from the user initially while compiling these means of different parameters for both touching and pointing. Fig.18. - Graph showing the distribution of cognitive load with age Fig.19. - Graph showing the distribution of cognitive load with education 3
  4. 4. Index Brief History (Design Project II) 5 Studying Research Papers on Gesture Based Systems 6 Critical Appraisal 10 Methodology for Designing Gestural User Interfaces 11 Building the gesture vocabulary Experiment 16 Conclusion 28 References 4 15 28
  5. 5. Project History (Design Project I) According to the Sample Registration Services (SRS) 2004-2006, the MMR* for Assam was 480 per 100,000 live births the highest in the country. India's MMR was 254. The study aimed at investigating existing problems faced by ASHA members and ANMs, their work environment, their role in safe and healthy motherhood, relationship with pregnant women (PW) and family members, technology literacy and opportunities for Information Communication Technology (ICT) interventions to empower maternal health scenario. Two sub-centres (SC), one Anganwadi centre, one primary health centre (PHC), one civil hospital and one community health centre (CHC) were visited and observed. 12 one-to-one on-field interviews were conducted with ASHA members, ANMs, PW and Doctors at the Primary Health Centre. Publications from Design Project I Research analysis was done using affinity and six use cases were prepared based on that.  Keyur Sorathia, Minal Jain, Mannu Amrit, Denny George, Jagriti Kumar and Amit Ranjan, Research Findings, Analysis and Design Opportunities for Empowerment of Maternal Health in Assam, India, In workshop on Intelligent User interfaces for Developing Regions (IUIDR), International Conference on Intelligent User Interfaces, CA, USA (19-22nd March) Fig.1. ANMs at Moriyapati sub-center  Keyur Sorathia, Mannu Amrit and Minal Jain, Research Findings, Analysis and ICT Interventions for Empowerment of Maternal Health in Assam, India, In International Conference on Global Research Association for Development and Excellence (International Journal of Research in Engineering & Applied Science (ISSN:2294-3905)) 5
  6. 6. Studying Research Papers on Gesture Based Systems are like slapping the phone to mute the ring tone. Following papers were studied and presentations were held every Wednesday discussing these– Suspenseful - Suspenseful gestures have manipulations revealed but the effects hidden. For example drawing an exaggerated “X” mark in air to turn silent profile ON 1. Would you do that? Understanding Social Acceptance of Gestural Interfaces Calkin S. Montero, Jason Alexander, Mark T. Marshall, Sriram Subramanian This paper presents main factors that influence gestures’ social acceptance including culture, time, interaction type and the user’s position on the innovation adoption curve. They claim that user performance or manipulation of a device along with the visible results of that performance or its effects is a vital element that influences social acceptance. Gestures have been classified into four categories based on manipulation vs effect plane Expressive - Expressive gestures with both manipulations and effects visible 6 Secretive - It has both the manipulation and effects hidden such as tapping on the phone to change its volume when talking. Magical - Magical gestures have their manipulations hidden but the effects revealed or amplified. Conclusion - Both secretive gestures and expressive gestures have a greater chance of being socially acceptable, whereas suspenseful gestures are more often seen socially unacceptable. 2. Wave Like an Egyptian — Accelerometer Based Gesture Recognition for Culture Specific Interactions Matthias Rehm, Nikolaus Bee, Elisabeth André
  7. 7. This paper uses Wiimote to uncover the user’s cultural background by analyzing his patterns of gestural expressivity in a model based on cultural dimensions. With this information at hand, the behavior of an interactive system can be adapted to culture-dependent patterns of interaction. This paper uses embodied conversational agents as interface metaphor. According to authors, it has great potential to provide: (i) Information presentation (ii) Entertainment (iii) Serious games 3. Evaluating Performance and Acceptance of Older Adults Using Freehand Gestures for TV Menu Control Jan Bobeth, Susanne Schmehl, Ernst Kruijff, Stephanie Deutsch, Manfred Tscheligi and acceptance of freehand gestures by implementing several techniques and conducting a user study with 24 older adults. 4. Free-Hand Gestures for Music Playback: Deriving Gestures with a User-Centred Process Niels Henze, Andreas Löcken, Susanne Boll In the user study, four different kinds of freehand gesture interaction were compared to control a corresponding TV menu, investigating specifically on abilities of older adults. Each of the interaction types was analysed regarding task completion time, error rate, usability and acceptance. A refined process for deriving gestures from constant user feedback is proposed. Along this process a set of free-hand gestures for controlling music playback is developed. Two gesture sets containing static and dynamic gestures are derived and analyzed in a comparative evaluation. Participatory design method was used. Results showed that directly transferring tracked hand movements to control a cursor on a TV achieved the best performance and was preferred by the users. In this paper, the authors tried to explore alternative TV menu control methods, focusing specifically on older users. They investigated performance A set of static gestures (Paper 4) Fig.2. A set of static gestures (Paper 4) 7
  8. 8. The classification results are used along with the feature vector to generate a combination of sounds and images that change in real time depending on the person’s facial expressions. Fig.3. An analysis of the gestures (paper 4) 5. Facial Expression Recognition as a Creative Interface Roberto Valenti, Alejandro Jaimes, Nicu Sebe An audio-visual creativity tool that automatically recognizes facial expressions in real time, producing sounds in combination with images was developed. The facial expression recognition component detects and tracks a face and outputs a feature vector of motions of specific locations in the face. The feature vector is used as input to a Bayesian network which classifies facial expressions into several categories (e.g., angry, disgusted, happy, etc.). 8 responded to the gestures of the game 3. Provide a guideline to design gestural user interfaces for institutionalized older adults 6. Full body motion based game interaction for older adults Kathrin M. Gerling, Ian J. Livingston, Lennart E. Nacke, Regan L. Mandryk 7. Teaching Natural User Interaction Using Open NI & Microsoft Kinect Sensor This paper describes how full-body motion-control games can accommodate a variety of user abilities, have a positive effect on mood and, by extension, the emotional well-being of older adults Kinect offers opportunities for novel approaches to classroom instruction on natural user interaction. Current state of this technology evaluated and overview of some of the development frameworks presented. This paper presents 3 main studies 1. Identification of appropriate gestures to support video games for institutionalized older adults, evaluate it and design a video games based on identified gestures 2. Design of a video game and evaluate how participants Examples were presented to show how Kinect assisted instruction can be used to achieve some learning outcomes in HCI courses. The paper concluded and verified that OpenNI, with accompanying libraries, can be used for these activities in multi-platform learning environments.
  9. 9. 8. Kinect in the Kitchen: Testing Depth Camera Interactions in Practical Home Environment Research takes the Kinect into real-life kitchens, where touchless gestural control could be a boon for messy hands, but where commands are interspersed with the movements of cooking. A recipe navigator, timer and music player are implemented Users were allowed to change the control scheme at runtime and navigate with other limbs when their hands are full. 9. Wiimote and Kinect: Gestural User Interfaces add a Natural third dimension to HCI Paper presents two systems specifically designed for 3D gestural interaction on 3D geographical maps. The proposed applications rely on two consumer technologies both capable of motion tracking: the Nintendo Wii and the Microsoft Kinect devices. 10. Using the Kinect to Encourage Older Adults to Exercise: A Prototype and verification for educational games for deaf children. The study aims to find the factors that play an important role in motivating older adults to maintain a physical exercise routine. System was tested with 5 users in the age group of 20 to 30 overall positive response was obtained 11. Super Mirror: A Kinect Interface for Ballet Dancers Super Mirror, a Kinect-based system combines the functionality of studio mirrors and prescriptive images to provide the user with instructional feedback in real-time. The research is focused on questions about user control of the system, system recognition of position data, and user feedback. 12. American Sign Language Recognition with the Kinect The paper aimed at investigating the potential of the Kinect depth-mapping camera for sign language recognition 9
  10. 10. Critical Appraisal: Our research showed that no work had been done in the area of gesture based systems for people in rural areas in developing countries, especially in the field of health. We took this as an exploration combining gesture based interaction with spoken web technology of IBM Research to solve the gripping problem of high maternal mortality in rural Assam. Literature research on gesture based systems designed previously helped us in understanding the methodology followed in different project and evolve our own methodology. A gesture vocabulary was created for references. An experiment was conducted with people of both genders from all age groups in both low literate and literate categories. Comprehensive analysis of the system produced results. In the end a methodology for designing gesture based systems was proposed. 10
  11. 11. Methodology for Designing Gestural User Interfaces Identify right functions (e.g. stop, play, pause etc.) – 1.0 Based on the literature research a methodology for designing gestural user interfaces was proposed. Identify right set of functionalities your system will require. Explain each functionality in detail to have a clear understanding of the functions. The human based principles should make the gestures: e.g. skip: it will be used to skip contents on sub modules • Easy to perform and remember User testing – 2.0 • Intuitive • Metaphorically and iconically logical towards functionality Find the gestures that represent functions found in step 1. • Ergonomic; not physically stressing when used often In order to achieve these principles it is necessary to take usability theory, and biomechanics/ergonomics. Following are the stages of gesture system design as proposed by us for the project on maternal health in rural assam – • Preparation: • Categorize the study into prestudy, during study and post study sections • Prestudy: prepare introduction document and all required functions • During study: prepare space, video camera, projector & a scenario video (to be presented to users, e.g. a small video & stop function is tested) • Post study: remuneration, signature, verification of function-gesture question • Study • 20 PW will be recruited* • Users must be introduced to task. A demo of the task is required* • Voice and gesture both should be encouraged. • Complete task must be recorded - voice recording of researcher explaining the tasks, users performing tasks and postperformance questions. • Explain user a scenario and ask them to perform gestures for a specific function. • Use video recording and written notes for documentation of performed gestures. 11
  12. 12. It is important to design the experiments in a way that users use the gestures in a natural way. User’s social acceptance: - Did they feel comfortable or uncomfortable, awkward or natural, relaxed or embarrassed? This will lead to an overall positive or negative impression of the task or technology. Spectator’s social acceptance: User actions are performed in a range of public and private situations, i.e. contexts. - User performance or manipulation of a device along with the visible results of that performance or its effects is a vital element that influences social acceptance - It has stronger impact on spectator’s social acceptance. If an interaction is too loud or obtrusive and there is no real meaning to it from the spectator’s view, a negative impression will form - Technology must perform good to increase social acceptance from users Analysis of user testing – 3.0 - Does the audience understand what the user is doing? • Extract commonly used gestures and note how consistently users use them. - Do they think the action is ‘weird’ or ‘normal’? • Understand whether those are static or dynamic gestures. - The spectator quickly builds a positive or negative impression of the user’s actions. • On dynamic gestures, capture a video or frames to document it. Manipulation vs. effect 12 Selection of gesture should take into account: • Evaluate internal force caused by posture • Deviation from neutral position • Outer limits • Forces from inter-joint relations • Evaluate frequency and duration of that gesture Analysis of user testing – 3.1 Classification of gestures • Expressive e.g. slapping the phone to mute ring tone • Suspenseful e.g. drawing “x” in air to delete contents • Secretive e.g. tapping the phone to change its volume • Magical
  13. 13. Gestures are hidden but feedback is revealed • Deictic, propositional etc. more new forms can be identified and classified Analysis of user testing – 3.2* Evaluate possible and potential gestures with team of doctors. This study will help us identify gestures those can be potentially performed by PW from different trimester Analysis of user testing – 4 Test the gesture vocabulary • Translate all gestures in “Assamese” language* • Guess the function • Give users a list of functions. • Prepare a set of videos explaining each function through a gesture • Present the gestures and ask the person to guess the functions • Score = errors divided by number of gestures • Memory • Give them a demo of all gestures & associated functions* • Give them a 10 minutes break* • Present a slideshow of names of functions in a swift pace, 6 seconds per function. Users are asked to perform gestures when function is presented on slideshow • Score = number of restarts • Stress • Identify right sequence of gestures* • Identify how many times this sequence has to be performed* • The user must perform the sequence X times, where X times the size of gesture vocabulary equals 200. Between each gesture go back to neutral hand position. • Note down other observations during the study. E.g. User was stressed due to a specific gesture • Stress Use the following score list for each gesture and overall for the sequence • No problem • Mildly Tiring/Stressing • Tiring/Stressing • Very annoying • Impossible Likert scale can be used to evaluate above parameters* • Social acceptance • 10 sec. video of each gesture will be showcased to participants. 13
  14. 14. • • Q1- What would you think if you saw someone else performing this gesture (for example, when walking down the street)? Participants will be asked to give 2-3 keyword answer for every gesture and then fill Q2 • Q2- How would you feel performing this gesture in public space? The Likert scale ranged from 1 (Embarrassed) to 6 (Comfortable) will be given to them. This scale will give us insights on the social acceptance of the gestures • 14 They will be asked two questions: an open question (Q1) and a six-point scale data question (Q2) Social acceptance needs to be understood more in-detail
  15. 15. Building the gesture vocabulary 3D Gesture Documentation was an attempt to provide an overview of possible 3D gestures, which can be implemented in gestural user interfaces for variety of purposes. Possible functionality of the gesture was also highlighted. • Lower body gestures Gestures that involve lower body (below waist) movements The gestures are divided into three major sections: • Fig.6. Fly right to select or confirm a choice on right side; horizontal scroll on right Upper body gestures Gestures that involve upper body (above waist) movements Fig.5. Right leg upwards to select or confirm a choice on right side; horizontal scrolling from right side • It is gathered from existing literature and on-going research on identification of appropriate gestures for social acceptance. Full body gestures Gestures that involve full body movement Fig.4. Head sideways (right) to scroll to the right 15
  16. 16. Experiment Ms Sumitha Sharma visited the EI Lab from Speech Based and Pervasive Interaction Group, Tampere Unit for Computer Human Interaction University of Tampere, Finland. A study was conducted with her to analyse the comfort levels of literate males and female below and above the age of 35 and low literate males and females below and abve 35 years of age. Also, a comparitive analysis was conducted to understand their preference between pointing at the option and touching the corresponding body parts while using gesture based system. The system contained information about the head, neck, shoulder and stomach: basic functionality of each part and what ailments it is most prone to. Microsoft Kinect was used to track the user’s upper body movements and the 3D model and video content was rendered using the Panda 3D graphics engine. The core logic application that controlled the output based on the user’s Kinect data was coded in Python. An added feature was the wave gesture to return to the menu screen from the currently playing video content. System We developed a health information system that used free form gestures as input and provided audio-visual content in Assamese as output. When the system detects a user, a 3D Assamese lady avatar introduces the system to the user, explaining how to interact using two selection methods: pointing and touching. User can point to icons on the menu screen or touch that particular body part with their right hand to trigger a selection, as shown in figure 7. 16 Fig.7. A screenshot of the system screen User Study A user study was conducted with native Assamese users in March 2013. Initially, we asked the users to try either pointing or touching depending on what they preferred but it was observed that participants would only try touching since it was explained last in the introductory video. This made it difficult to ask them what they preferred if they only tried one method. To be able to compare between the two selections methods, we changed the task to include both pointing and touching for each user. Thus the user study is divided in two parts: one with 9 users who tried the system once and the other with 25 users who were explicitly asked to try both pointing and touching as two separate tasks. After each tasks, users were asked to answer the NASA TLX rating and weights comparison. At the end of the study, users were also interviewed. Each user was given Rs 200 as remuneration. 1. Setup The setup consisted of a laptop running the system and connected to a Kinect for user tracking, speakers for audio and a 51” LCD TV displaying the graphical
  17. 17. output. Participants were asked to step in between two lines marked on the floor, 1 meter apart and three meters away from the TV, as shown in the figure 8. A camera was kept inside the room that recorded the actions of the user and this was further connected to a usability room where observations were made by us. Fig.8. Observations being made from the usability room 1. Participants There were 37 participants in total, consisting of both male and female low literate and educated users. Users were classified based on gender, age and education level where user above 35 years of age were considered old and users with more than 10+ years of schooling were considered educated. This gave us 8 users groups following the convention: FL, FLL, FOL , FLL (female literate, female low literate, female old literate, female old low literate) and similarly for the male users (ML, MLL, MOL, MOLL). Out of these 37 participants, 9 were asked to do only one task so their data is incomplete for a direct TLX comparison, and 3 didn’t answer the NASA TLX completely either because they were too tired or in a hurry to leave. Thus the remaining 25 users preformed two separate tasks (one for pointing and one for touching) where they were asked to select any two of the four body parts for information which they did not have to remember. The user profile and distribution of those 25 users is shown in the figure below: 2. Procedure Of those 25 participants, each participant was first asked to try an exercise session where they were shown a human shadow on TV that imitated their upper body movements. This was done for two reasons: first to get a fair idea of how well the Kinect was able to track the users and second to allow the user to familiarize herself / himself with the on-screen shadow. Users were asked to just wave their hands in the air or perform any gesture of their liking for as long they felt comfortable. Then users were asked to find information about any two body parts by first pointing and then later touching (or vice versa). There was no time limit for any of the tasks and users could select more than two options if they so wished. After each task, users were asked to fill in the NASA TLX system evaluation form. Since a lot of the users were not comfortable with this type of questionnaire or were not familiar with the NASA TLX terminology, moderators translated the six sub-scales ratings and comparisons. After completing both the tasks and their NASA TLX evaluations, users were asked to answer three interview questions: Fig.9. Distribution of user groups 17
  18. 18. a. Of the two selection methods, which one did they prefer and why? b. Give one positive feedback and one negative feedback about their interaction with the system. c. If they would be open to using such a system in the future. 3. Observations Introduction of ASHA health worker as a 3D character was found effective among low literate users. Users performed “namaste” (hello) and “dhanyavad” (thank you) in front of the system. After understanding the system, low literate users started expecting more from the system. They were found touching their knee, back and other body parts. One user started verbally explaining her back pain to the system, indicating a strong relationship built with system. As compared to low literate users, literate users were found emotionally less connected to the system. They did not perform “namaste” or “dhanyavad” to the system, instead asked about advanced features such as more language options and increase/decrease 18 in volume etc. They also found the introduction video too. One user mentioned, “This kind of a system can be used at home, but not in public space”, showcasing literate users’ less willingness to use gesture based interfaces in public space. System’s technical performance was found critical for results. Inaccurate system performance confused users as they continuously looked back towards the moderator for help. One user got tensed as head touching did not work. She asked whether there is any problem in her head which had caused this error. Between touching and pointing, users whose touching was found inaccurate preferred pointing as a gesture modality, while users whose touching was found accurate preferred touching. Similarly, visual feedback played important role in selecting pointing as a preference gesture modality. Fig.10. User performing the tasks One user said that they liked pointing because it showcased their hand on screen while selecting”. Touching also had a visual feedback (icon was changing its colour when touching a specific body part), however users could not relate with visual changes. A few low literate users mistook the content / system as being able to take their x-rays or see inside of them. It showed that users are easily misled into believing the system is more capable than it is and trust it blindly. A lot of the users would keep touching the relevant body part even after the video started playing. It shows the need to find a way to define the exact gesture a system takes as input. Currently, users felt that anything they did would ‘do something’ to the system.
  19. 19. Traditionally, female users in India wear sari and salwar-kamiz for their daily routine. Low literate users (old & young) and literate users (old) performed all tasks wearing traditional cloths. While performing tasks, one user’s pallu moved from her salwar-kamiz, which was detected as input from the system, due to which user got confused. Low literate users relate the system with touch based interfaces. Two users tried reaching to television to touch preferred contents. One user mentioned, “I have seen this system in a local museum” which actually was a touch enabled interface. It is important for a system to inform users that this is not a touch based system. NASA TLX method, parameters and ratings are provided in English. Post study questionnaire in local language using NASA TLX method was found very difficult. Researchers had difficulty in explaining various “demands” in local language Assamese/Hindi, due to which it was difficult for users to compare between demands, especially for low literate users. For touching, users were not able to relate to the Human shadow on the screen (even after the exercise session), and thus it seems that users' felt there was immediate feedback only for pointing (hand cursor on the screen). This is interesting because for the exercise session they were able to relate to the shadow. Also, with users so new to such a gesture based system, it seems that users found this mapping difficult to recall. Fig.11. User performing the task 19
  20. 20. Following tables and graphs show the analysis of the study. Fig 12 shows the table which has all the data collected from the user organised in a tabular form. Cognitive Load, RAW TLX, Mean and standard deviation have been calculated additionally. Fig.12. Complete data obtained from the user initially while compiling these results. 20
  21. 21. Fig.13. Complete data obtained from the user initially 21
  22. 22. In the following table, the cognitive load and Raw TLX for each user’s touching and pointing has been calculated. Fig.14. Cognitive Load and Raw TLX values for all the users have been calculated. 22
  23. 23. The above shows the mean and standard deviation for all the parameters. It is observed that the values of mean and standard deviation are found quite close. Using T-test, p-value for comparison between pointing and touching for all the users is - 0.27194 Hence not much of significant results are obtained here. The following graphs give a few insights. . Fig.15. Comparatives analysis of the rating of mental demand, temporal demand, physical demand, performance, effort and frustration for pointing and touching. 23
  24. 24. Fig.16. Bar graph showing the preferences of users of different categories. It shows that touching is majorly preferred over pointing. Fig.17. Bar graph showing the weight means of different parameters for both touching and pointing. 24
  25. 25. Following were a few user statements    Pointing could be controlled better (ML). Preferred touching body part to pointing as it required less physical movement (ML) Preferred touching the body part as pointing wasn’t natural (MOL) In the above tables, we observe that the p value obtained by the T-test is <0.05 which shows that the difference between the quantities is not very significant. However, we feel such is result is due to factors like system errors and inaccuracies and the difficulty faced while conducting NASA TLX due to language problem as well as improper translation of the meanings of them terms. 25
  26. 26. Fig.18. Graph showing the distribution of cognitive load with age. 26
  27. 27. Fig.19. Graph showing the distribution of cognitive load with education. 27
  28. 28. Conclusion Based on the study conducted above, the methodology proposed was refined. It will now be used in the subsequent months for designing the gesture based system. In the experiment, although quantitative results show no clear distinction between pointing and touching, qualitative analysis throws light upon factors like system errors and inaccuracies and the difficulty faced while conducting NASA TLX due to language problem as well as improper translation of the meanings of them terms that might have influenced the study. A research paper is being written which will be submitted for the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2014). References 1. Would you do that? Understanding Social Acceptance of Gestural Interfaces Calkin S. Montero, Jason Alexander, Mark T. Marshall, Sriram Subramanian 2. Wave Like an Egyptian — Accelerometer Based Gesture Recognition for Culture Specific Interactions Matthias Rehm, Nikolaus Bee, Elisabeth André 3. Evaluating Performance and Acceptance of Older Adults Using Freehand Gestures for TV Menu Control Jan Bobeth, Susanne Schmehl, Ernst Kruijff, Stephanie Deutsch, Manfred Tscheligi 4. Facial Expression Recognition as a Creative Interface Roberto Valenti, Alejandro Jaimes, Nicu Sebe 5. Free-Hand Gestures for Music Playback: Deriving Gestures with a UserCentred Process Niels Henze, Andreas Löcken, Susanne Boll 28 Full body motion based game interaction for older adults Kathrin M. Gerling, Ian J. Livingston, Lennart E. Nacke, Regan L. Mandryk Teaching Natural User Interaction Using Open NI & Microsoft Kinect SensorNorman Villaroman, Dale Rowe, Bret Swan Kinect in the Kitchen: Testing Depth Camera Interactions in Practical Home Environment Galen Panger Wiimote and Kinect: Gestural User Interfaces add a Natural third dimension to HCI Rita Francese, Ignazio Passero, Genoveffa Tortora Using the Kinect to Encourage Older Adults to Exercise: A Prototype Samyukta Ganesan Lisa Anthony Super Mirror: A Kinect Interface for Ballet Dancers Zoe Marquardt, João Beira, Isabel Paiva, Natalia Em, Sebastian Kox American Sign Language Recognition with the Kinect Zahoor Zafrulla, Helene
  29. 29. Brashear,Thad Starner, Harley Hamilton, Peter Presti 29

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