GazeDrone: Mobile Eye-Based Interaction in Public Space Without Augmenting the User

Gaze interaction holds a lot of promise for seamless human-computer interaction. At the same time, current wearable mobile eye trackers require user augmentation that negatively impacts natural user behavior while remote trackers require users to position themselves within a confined tracking range. We present GazeDrone, the first system that combines a camera-equipped aerial drone with a computational method to detect sidelong glances for spontaneous (calibration-free) gaze-based interaction with surrounding pervasive systems (e.g., public displays). GazeDrone does not require augmenting each user with on-body sensors and allows interaction from arbitrary positions, even while moving. We demonstrate that drone-supported gaze interaction is feasible and accurate for certain movement types. It is well-perceived by users, in particular while interacting from a fixed position as well as while moving orthogonally or diagonally to a display. We present design implications and discuss opportunities and challenges for drone-supported gaze interaction in public

Human-Robot Interaction Based on Gaze Gestures for the Drone Teleoperation

Teleoperation has been widely used to perform tasks in dangerous and unreachable environments by replacing humans with controlled agents. The idea of human-robot interaction (HRI) is very important in teleoperation. Conventional HRI input devices include keyboard, mouse and joystick, etc. However, they are not suitable for handicapped users or people with disabilities. These devices also increase the mental workload of normal users due to simultaneous operation of multiple HRI input devices by hand. Hence, HRI based on gaze tracking with an eye tracker is presented in this study. The selection of objects is of great importance and occurs at a high frequency during HRI control. This paper introduces gaze gestures as an object selection strategy into HRI for drone teleoperation. In order to test and validate the performance of gaze gestures selection strategy, we evaluate objective and subjective measurements, respectively. Drone control performance, including mean task completion time and mean error rate, are the objective measurements. The subjective measurement is the analysis of participant perception. The results showed gaze gestures selection strategy has a great potential as an additional HRI for use in agent teleoperation

MIDAS: Deep learning human action intention prediction from natural eye movement patterns

Eye movements have long been studied as a window into the attentional mechanisms of the human brain and made accessible as novelty style human-machine interfaces. However, not everything that we gaze upon, is something we want to interact with; this is known as the Midas Touch problem for gaze interfaces. To overcome the Midas Touch problem, present interfaces tend not to rely on natural gaze cues, but rather use dwell time or gaze gestures. Here we present an entirely data-driven approach to decode human intention for object manipulation tasks based solely on natural gaze cues. We run data collection experiments where 16 participants are given manipulation and inspection tasks to be performed on various objects on a table in front of them. The subjects' eye movements are recorded using wearable eye-trackers allowing the participants to freely move their head and gaze upon the scene. We use our Semantic Fovea, a convolutional neural network model to obtain the objects in the scene and their relation to gaze traces at every frame. We then evaluate the data and examine several ways to model the classification task for intention prediction. Our evaluation shows that intention prediction is not a naive result of the data, but rather relies on non-linear temporal processing of gaze cues. We model the task as a time series classification problem and design a bidirectional Long-Short-Term-Memory (LSTM) network architecture to decode intentions. Our results show that we can decode human intention of motion purely from natural gaze cues and object relative position, with $91.9\%$ accuracy. Our work demonstrates the feasibility of natural gaze as a Zero-UI interface for human-machine interaction, i.e., users will only need to act naturally, and do not need to interact with the interface itself or deviate from their natural eye movement patterns

Evaluation of a Remote-Controlled Drone System for Bedridden Patients Using Their Eyes Based on Clinical Experiment

With the aging of the population in Japan, the number of bedridden patients who need long-term care is increasing. The Japanese government has been promoting the creation of an environment that enables everyone, including bedridden patients, to enjoy travel, based on the principle of normalization. However, it is difficult for bedridden patients to enjoy the scenery of distant places and to talk with the local people because they need support from helpers to travel to distant places using travel agencies. Therefore, to enhance their quality of life (QOL), we developed a remote-controlled drone system, which involves using only the eyes. We believe that bedridden patients are able to operate the system’s drone in a distant place, to easily view the scenery of the distant place with a camera installed on the drone, and to talk with the local people. However, we have never evaluated whether actual bedridden patients can operate the drone in a distant place, to see the scenery, and to talk with the local people. In this paper, we presented clinical experimental results to verify the effectiveness of this drone system. Findings showed that, not only subjects with relatively high levels of independence in activities of daily living, but also bedridden subjects, could operate the drone at a distant place with only their eyes and communicate with others

Interactive spaces for children: gesture elicitation for controlling ground mini-robots

[EN] Interactive spaces for education are emerging as a mechanism for fostering children's natural ways of learning by means of play and exploration in physical spaces. The advanced interactive modalities and devices for such environments need to be both motivating and intuitive for children. Among the wide variety of interactive mechanisms, robots have been a popular research topic in the context of educational tools due to their attractiveness for children. However, few studies have focused on how children would naturally interact and explore interactive environments with robots. While there is abundant research on full-body interaction and intuitive manipulation of robots by adults, no similar research has been done with children. This paper therefore describes a gesture elicitation study that identified the preferred gestures and body language communication used by children to control ground robots. The results of the elicitation study were used to define a gestural language that covers the different preferences of the gestures by age group and gender, with a good acceptance rate in the 6-12 age range. The study also revealed interactive spaces with robots using body gestures as motivating and promising scenarios for collaborative or remote learning activities.This work is funded by the European Development Regional Fund (EDRF-FEDER) and supported by the Spanish MINECO (TIN2014-60077-R). The work of Patricia Pons is supported by a national grant from the Spanish MECD (FPU13/03831). Special thanks are due to the children and teachers of the Col-legi Public Vicente Gaos for their valuable collaboration and dedication.Pons Tomás, P.; Jaén Martínez, FJ. (2020). Interactive spaces for children: gesture elicitation for controlling ground mini-robots. Journal of Ambient Intelligence and Humanized Computing. 11(6):2467-2488. https://doi.org/10.1007/s12652-019-01290-6S24672488116Alborzi H, Hammer J, Kruskal A et al (2000) Designing StoryRooms: interactive storytelling spaces for children. In: Proceedings of the conference on designing interactive systems processes, practices, methods, and techniques—DIS’00. ACM Press, New York, pp 95–104Antle AN, Corness G, Droumeva M (2009) What the body knows: exploring the benefits of embodied metaphors in hybrid physical digital environments. Interact Comput 21:66–75. https://doi.org/10.1016/j.intcom.2008.10.005Belpaeme T, Baxter PE, Read R et al (2013) Multimodal child–robot interaction: building social bonds. J Human-Robot Interact 1:33–53. https://doi.org/10.5898/JHRI.1.2.BelpaemeBenko H, Wilson AD, Zannier F, Benko H (2014) Dyadic projected spatial augmented reality. In: Proceedings of the 27th annual ACM symposium on user interface software and technology—UIST’14, pp 645–655Bobick AF, Intille SS, Davis JW et al (1999) The KidsRoom: a perceptually-based interactive and immersive story environment. Presence Teleoper Virtual Environ 8:367–391. https://doi.org/10.1162/105474699566297Bonarini A, Clasadonte F, Garzotto F, Gelsomini M (2015) Blending robots and full-body interaction with large screens for children with intellectual disability. In: Proceedings of the 14th international conference on interaction design and children—IDC’15. ACM Press, New York, pp 351–354Cauchard JR, E JL, Zhai KY, Landay JA (2015) Drone & me: an exploration into natural human–drone interaction. In: Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing—UbiComp’15. ACM Press, New York, pp 361–365Connell S, Kuo P-Y, Liu L, Piper AM (2013) A Wizard-of-Oz elicitation study examining child-defined gestures with a whole-body interface. In: Proceedings of the 12th international conference on interaction design and children—IDC’13. ACM Press, New York, pp 277–280Derboven J, Van Mechelen M, Slegers K (2015) Multimodal analysis in participatory design with children. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems—CHI’15. ACM Press, New York, pp 2825–2828Dong H, Danesh A, Figueroa N, El Saddik A (2015) An elicitation study on gesture preferences and memorability toward a practical hand-gesture vocabulary for smart televisions. IEEE Access 3:543–555. https://doi.org/10.1109/ACCESS.2015.2432679Druin A (1999) Cooperative inquiry: developing new technologies for children with children. In: Proceedings of the SIGCHI conference on human factors computer system CHI is limit—CHI’99, vol 14, pp 592–599. https://doi.org/10.1145/302979.303166Druin A (2002) The role of children in the design of new technology. Behav Inf Technol 21:1–25. https://doi.org/10.1080/01449290110108659Druin A, Bederson B, Boltman A et al (1999) Children as our technology design partners. In: Druin A (ed) The design of children’s technology. Morgan Kaufman, San Francisco, pp 51–72Epps J, Lichman S, Wu M (2006) A study of hand shape use in tabletop gesture interaction. CHI’06 extended abstracts on human factors in computing systems—CHI EA’06. ACM Press, New York, pp 748–753Fender AR, Benko H, Wilson A (2017) MeetAlive : room-scale omni-directional display system for multi-user content and control sharing. In: Proceedings of the 2017 ACM international conference on interactive surfaces and spaces, pp 106–115Fernandez RAS, Sanchez-Lopez JL, Sampedro C et al (2016) Natural user interfaces for human–drone multi-modal interaction. In: 2016 international conference on unmanned aircraft systems (ICUAS). IEEE, New York, pp 1013–1022Garcia-Sanjuan F, Jaen J, Nacher V, Catala A (2015) Design and evaluation of a tangible-mediated robot for kindergarten instruction. In: Proceedings of the 12th international conference on advances in computer entertainment technology—ACE’15. ACM Press, New York, pp 1–11Garcia-Sanjuan F, Jaen J, Jurdi S (2016) Towards encouraging communication in hospitalized children through multi-tablet activities. In: Proceedings of the XVII international conference on human computer interaction, pp 29.1–29.4Gindling J, Ioannidou A, Loh J et al (1995) LEGOsheets: a rule-based programming, simulation and manipulation environment for the LEGO programmable brick. In: Proceedings of symposium on visual languages. IEEE Computer Society Press, New York, pp 172–179Gonzalez B, Borland J, Geraghty K (2009) Whole body interaction for child-centered multimodal language learning. In: Proceedings of the 2nd workshop on child, computer and interaction—WOCCI’09. ACM Press, New York, pp 1–5Grønbæk K, Iversen OS, Kortbek KJ et al (2007) Interactive floor support for kinesthetic interaction in children learning environments. In: Human–computer interaction—INTERACT 2007. Lecture notes in computer science, pp 361–375Guha ML, Druin A, Chipman G et al (2005) Working with young children as technology design partners. Commun ACM 48:39–42. https://doi.org/10.1145/1039539.1039567Hansen JP, Alapetite A, MacKenzie IS, Møllenbach E (2014) The use of gaze to control drones. In: Proceedings of the symposium on eye tracking research and applications—ETRA’14. ACM Press, New York, pp 27–34Henkemans OAB, Bierman BPB, Janssen J et al (2017) Design and evaluation of a personal robot playing a self-management education game with children with diabetes type 1. Int J Hum Comput Stud 106:63–76. https://doi.org/10.1016/j.ijhcs.2017.06.001Horn MS, Crouser RJ, Bers MU (2011) Tangible interaction and learning: the case for a hybrid approach. Pers Ubiquitous Comput 16:379–389. https://doi.org/10.1007/s00779-011-0404-2Hourcade JP (2015) Child computer interaction. CreateSpace Independent Publishing Platform, North CharlestonHöysniemi J, Hämäläinen P, Turkki L (2004) Wizard of Oz prototyping of computer vision based action games for children. Proceeding of the 2004 conference on interaction design and children building a community—IDC’04. ACM Press, New York, pp 27–34Höysniemi J, Hämäläinen P, Turkki L, Rouvi T (2005) Children’s intuitive gestures in vision-based action games. Commun ACM 48:44–50. https://doi.org/10.1145/1039539.1039568Hsiao H-S, Chen J-C (2016) Using a gesture interactive game-based learning approach to improve preschool children’s learning performance and motor skills. Comput Educ 95:151–162. https://doi.org/10.1016/j.compedu.2016.01.005Jokela T, Rezaei PP, Väänänen K (2016) Using elicitation studies to generate collocated interaction methods. In: Proceedings of the 18th international conference on human–computer interaction with mobile devices and services adjunct, pp 1129–1133. https://doi.org/10.1145/2957265.2962654Jones B, Benko H, Ofek E, Wilson AD (2013) IllumiRoom: peripheral projected illusions for interactive experiences. In: Proceedings of the SIGCHI conference on human factors in computing systems—CHI’13, pp 869–878Jones B, Shapira L, Sodhi R et al (2014) RoomAlive: magical experiences enabled by scalable, adaptive projector-camera units. In: Proceedings of the 27th annual ACM symposium on user interface software and technology—UIST’14, pp 637–644Kaminski M, Pellino T, Wish J (2002) Play and pets: the physical and emotional impact of child-life and pet therapy on hospitalized children. Child Heal Care 31:321–335. https://doi.org/10.1207/S15326888CHC3104_5Karam M, Schraefel MC (2005) A taxonomy of gestures in human computer interactions. In: Technical report in electronics and computer science, pp 1–45Kistler F, André E (2013) User-defined body gestures for an interactive storytelling scenario. Lect Notes Comput Sci (including subser Lect Notes Artif Intell Lect Notes Bioinform) 8118:264–281. https://doi.org/10.1007/978-3-642-40480-1_17Konda KR, Königs A, Schulz H, Schulz D (2012) Real time interaction with mobile robots using hand gestures. In: Proceedings of the seventh annual ACM/IEEE international conference on human–robot interaction—HRI’12. ACM Press, New York, pp 177–178Kray C, Nesbitt D, Dawson J, Rohs M (2010) User-defined gestures for connecting mobile phones, public displays, and tabletops. In: Proceedings of the 12th international conference on human computer interaction with mobile devices and services—MobileHCI’10. ACM Press, New York, pp 239–248Kurdyukova E, Redlin M, André E (2012) Studying user-defined iPad gestures for interaction in multi-display environment. In: Proceedings of the 2012 ACM international conference on intelligent user interfaces—IUI’12. ACM Press, New York, pp 93–96Lambert V, Coad J, Hicks P, Glacken M (2014) Social spaces for young children in hospital. Child Care Health Dev 40:195–204. https://doi.org/10.1111/cch.12016Lee S-S, Chae J, Kim H et al (2013) Towards more natural digital content manipulation via user freehand gestural interaction in a living room. In: Proceedings of the 2013 ACM international joint conference on pervasive and ubiquitous computing—UbiComp’13. ACM Press, New York, p 617Malinverni L, Mora-Guiard J, Pares N (2016) Towards methods for evaluating and communicating participatory design: a multimodal approach. Int J Hum Comput Stud 94:53–63. https://doi.org/10.1016/j.ijhcs.2016.03.004Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18:50–60. https://doi.org/10.1214/aoms/1177730491Marco J, Cerezo E, Baldassarri S et al (2009) Bringing tabletop technologies to kindergarten children. In: Proceedings of the 23rd British HCI Group annual conference on people and computers: celebrating people and technology, pp 103–111Michaud F, Caron S (2002) Roball, the rolling robot. Auton Robots 12:211–222. https://doi.org/10.1023/A:1014005728519Micire M, Desai M, Courtemanche A et al (2009) Analysis of natural gestures for controlling robot teams on multi-touch tabletop surfaces. In: Proceedings of the ACM international conference on interactive tabletops and surfaces—ITS’09. ACM Press, New York, pp 41–48Mora-Guiard J, Crowell C, Pares N, Heaton P (2016) Lands of fog: helping children with autism in social interaction through a full-body interactive experience. In: Proceedings of the 15th international conference on interaction design and children—IDC’16. ACM Press, New York, pp 262–274Morris MR (2012) Web on the wall: insights from a multimodal interaction elicitation study. In: Proceedings of the 2012 ACM international conference on interactive tabletops and surfaces. ACM Press, New York, pp 95–104Morris MR, Wobbrock JO, Wilson AD (2010) Understanding users’ preferences for surface gestures. Proc Graph Interface 2010:261–268Nacher V, Garcia-Sanjuan F, Jaen J (2016) Evaluating the usability of a tangible-mediated robot for kindergarten children instruction. In: 2016 IEEE 16th international conference on advanced learning technologies (ICALT). IEEE, New York, pp 130–132Nahapetyan VE, Khachumov VM (2015) Gesture recognition in the problem of contactless control of an unmanned aerial vehicle. Optoelectron Instrum Data Process 51:192–197. https://doi.org/10.3103/S8756699015020132Obaid M, Häring M, Kistler F et al (2012) User-defined body gestures for navigational control of a humanoid robot. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), pp 367–377Obaid M, Kistler F, Häring M et al (2014) A framework for user-defined body gestures to control a humanoid robot. Int J Soc Robot 6:383–396. https://doi.org/10.1007/s12369-014-0233-3Obaid M, Kistler F, Kasparavičiūtė G, et al (2016) How would you gesture navigate a drone?: a user-centered approach to control a drone. In: Proceedings of the 20th international academic Mindtrek conference—AcademicMindtrek’16. ACM Press, New York, pp 113–121Pares N, Soler M, Sanjurjo À et al (2005) Promotion of creative activity in children with severe autism through visuals in an interactive multisensory environment. In: Proceeding of the 2005 conference on interaction design and children—IDC’05. ACM Press, New York, pp 110–116Pfeil K, Koh SL, LaViola J (2013) Exploring 3D gesture metaphors for interaction with unmanned aerial vehicles. In: Proceedings of the 2013 international conference on intelligent user interfaces—IUI’13, pp 257–266. https://doi.org/10.1145/2449396.2449429Piaget J (1956) The child’s conception of space. Norton, New YorkPiaget J (1973) The child and reality: problems of genetic psychology. Grossman, New YorkPiumsomboon T, Clark A, Billinghurst M, Cockburn A (2013) User-defined gestures for augmented reality. CHI’13 extended abstracts on human factors in computing systems—CHI EA’13. ACM Press, New York, pp 955–960Pons P, Carrión A, Jaen J (2018) Remote interspecies interactions: improving humans and animals’ wellbeing through mobile playful spaces. Pervasive Mob Comput. https://doi.org/10.1016/j.pmcj.2018.12.003Puranam MB (2005) Towards full-body gesture analysis and recognition. University of Kentucky, LexingtonPyryeskin D, Hancock M, Hoey J (2012) Comparing elicited gestures to designer-created gestures for selection above a multitouch surface. In: Proceedings of the 2012 ACM international conference on interactive tabletops and surfaces—ITS’12. ACM Press, New York, pp 1–10Raffle HS, Parkes AJ, Ishii H (2004) Topobo: a constructive assembly system with kinetic memory. System 6:647–654. https://doi.org/10.1145/985692.985774Read JC, Markopoulos P (2013) Child–computer interaction. Int J Child-Comput Interact 1:2–6. https://doi.org/10.1016/j.ijcci.2012.09.001Read JC, Macfarlane S, Casey C (2002) Endurability, engagement and expectations: measuring children’s fun. In: Interaction design and children, pp 189–198Read JC, Markopoulos P, Parés N et al (2008) Child computer interaction. In: Proceeding of the 26th annual CHI conference extended abstracts on human factors in computing systems—CHI’08. ACM Press, New York, pp 2419–2422Robins B, Dautenhahn K (2014) Tactile interactions with a humanoid robot: novel play scenario implementations with children with autism. Int J Soc Robot 6:397–415. https://doi.org/10.1007/s12369-014-0228-0Robins B, Dautenhahn K, Te Boekhorst R, Nehaniv CL (2008) Behaviour delay and robot expressiveness in child–robot interactions: a user study on interaction kinesics. In: Proceedings of the 3rd ACMIEEE international conference on human robot interaction, pp 17–24. https://doi.org/10.1145/1349822.1349826Ruiz J, Li Y, Lank E (2011) User-defined motion gestures for mobile interaction. In: Proceedings of the 2011 annual conference on human factors in computing systems—CHI’11. ACM Press, New York, p 197Rust K, Malu M, Anthony L, Findlater L (2014) Understanding childdefined gestures and children’s mental models for touchscreen tabletop interaction. In: Proceedings of the 2014 conference on interaction design and children—IDC’14. ACM Press, New York, pp 201–204Salter T, Dautenhahn K, Te Boekhorst R (2006) Learning about natural human-robot interaction styles. Robot Auton Syst 54:127–134. https://doi.org/10.1016/j.robot.2005.09.022Sanghvi J, Castellano G, Leite I et al (2011) Automatic analysis of affective postures and body motion to detect engagement with a game companion. In: Proceedings of the 6th international conference on human–robot interaction—HRI’11. ACM Press, New York, pp 305–311Sanna A, Lamberti F, Paravati G, Manuri F (2013) A Kinect-based natural interface for quadrotor control. Entertain Comput 4:179–186. https://doi.org/10.1016/j.entcom.2013.01.001Sato E, Yamaguchi T, Harashima F (2007) Natural interface using pointing behavior for human–robot gestural interaction. IEEE Trans Ind Electron 54:1105–1112. https://doi.org/10.1109/TIE.2007.892728Schaper M-M, Pares N (2016) Making sense of body and space through full-body interaction design. In: Proceedings of the 15th international conference on interaction design and children—IDC’16. ACM Press, New York, pp 613–618Schaper M-M, Malinverni L, Pares N (2015) Sketching through the body: child-generated gestures in full-body interaction design. In: Proceedings of the 14th international conference on interaction design and children—IDC’15. ACM Press, New York, pp 255–258Seyed T, Burns C, Costa Sousa M et al (2012) Eliciting usable gestures for multi-display environments. In: Proceedings of the 2012 ACM international conference on interactive tabletops and surfaces—ITS’12. ACM Press, New York, p 41Shimon SSA, Morrison-Smith S, John N et al (2015) Exploring user-defined back-of-device gestures for mobile devices. In: Proceedings of the 17th international conference on human–computer interaction with mobile devices and services—MobileHCI’15. ACM Press, New York, pp 227–232Sipitakiat A, Nusen N (2012) Robo-blocks: a tangible programming system with debugging for children. In: Proceedings of the 11th international conference on interaction design and children—IDC’12. ACM Press, New York, p 98Soler-Adillon J, Ferrer J, Pares N (2009) A novel approach to interactive playgrounds: the interactive slide project. In: Proceedings of the 8th international conference on interaction design and children—IDC’09. ACM Press, New York, pp 131–139Stiefelhagen R, Fogen C, Gieselmann P et al (2004) Natural human–robot interaction using speech, head pose and gestures. In: 2004 IEEE/RSJ international conference on intelligent robots and systems (IROS) (IEEE Cat. No. 04CH37566). IEEE, New York, pp 2422–2427Subrahmanyam K, Greenfield PM (1994) Effect of video game practice on spatial skills in girls and boys. J Appl Dev Psychol 15:13–32. https://doi.org/10.1016/0193-3973(94)90004-3Sugiyama J, Tsetserukou D, Miura J (2011) NAVIgoid: robot navigation with haptic vision. In: SIGGRAPH Asia 2011 emerging technologies SA’11, vol 15, p 4503. https://doi.org/10.1145/2073370.2073378Takahashi T, Morita M, Tanaka F (2012) Evaluation of a tricycle-style teleoperational interface for children: a comparative experiment with a video game controller. In: 2012 IEEE RO-MAN: the 21st IEEE international symposium on robot and human interactive communication. IEEE, New York, pp 334–338Tanaka F, Takahashi T (2012) A tricycle-style teleoperational interface that remotely controls a robot for classroom children. In: Proceedings of the seventh annual ACM/IEEE international conference on human–robot interaction—HRI’12. ACM Press, New York, pp 255–256Tjaden L, Tong A, Henning P et al (2012) Children’s experiences of dialysis: a systematic review of qualitative studies. Arch Dis Child 97:395–402. https://doi.org/10.1136/archdischild-2011-300639Vatavu R-D (2012) User-defined gestures for free-hand TV control. In: Proceedings of the 10th European conference on interactive TV and video—EuroiTV’12. ACM Press, New York, pp 45–48Vatavu R-D (2017) Smart-Pockets: body-deictic gestures for fast access to personal data during ambient interactions. Int J Hum Comput Stud 103:1–21. https://doi.org/10.1016/j.ijhcs.2017.01.005Vatavu R-D, Wobbrock JO (2015) Formalizing agreement analysis for elicitation studies: new measures, significance test, and toolkit. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems—CHI’15. ACM Press, New York, pp 1325–1334Vatavu R-D, Wobbrock JO (2016) Between-subjects elicitation studies: formalization and tool support. In: Proceedings of the 2016 CHI conference on human factors in computing systems—CHI’16. ACM Press, New York, pp 3390–3402Voyer D, Voyer S, Bryden MP (1995) Magnitude of sex differences in spatial abilities: a meta-analysis and consideration of critical variables. Psychol Bull 117:250–270. https://doi.org/10.1037/0033-2909.117.2.250Wainer J, Robins B, Amirabdollahian F, Dautenhahn K (2014) Using the humanoid robot KASPAR to autonomously play triadic games and facilitate collaborative play among children with autism. IEEE Trans Auton Ment Dev 6:183–199. https://doi.org/10.1109/TAMD.2014.2303116Wang Y, Zhang L (2015) A track-based gesture recognition algorithm for Kinect. Appl Mech Mater 738–7399:334–338. https://doi.org/10.4028/www.scientific.net/AMM.738-739.334