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Flexible gesture input with radars: systematic literature review and taxonomy of radar sensing integration in ambient intelligence environments

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Abstract

We examine radar-based gesture input for interactive computer systems, a technology that has recently grown in terms of commercial availability, affordability, and popularity among researchers and practitioners, where radar sensors are leveraged to detect user input performed in mid-air, on the body, and around physical objects and digital devices. We analyze forty-five academic papers published on this topic between 2010 and 2021, and report results regarding gesture recognition techniques, application types, and evaluation approaches for radar-based gesture input. Our findings reveal that (1) deep learning techniques, such as Convolutional Neural Networks, have been the most popular approach for radar-based gesture recognition, (2) application opportunities for implementing radar gestures have been diverse, but without any clear contender for a game changer in this area, and (3) the gesture sets employed in prior work have been small with a median of just six gesture types. Based on these findings, we draw ten implications for integrating radar-based gesture sensing in ambient intelligence environments.

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Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Notes

  1. https://libraries.acm.org/digital-library/acm-guide-to-computing-literature

  2. https://ieeexplore.ieee.org/Xplore/home.jsp

  3. https://dl.acm.org

  4. https://www.design-burger.com/media/ambient-interfaces

  5. https://walabot.com

References

  • Aarts E, Encarnação J (2006) True visions: the emergence of ambient intelligence. Springer, Berlin

    Book  Google Scholar 

  • Abtahi P, Zhao DY, E. JL, et al (2017) Drone Near Me: Exploring Touch-Based Human-Drone Interaction. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1(3)

  • Adib F, Hsu CY, Mao H, et al (2015) Capturing the Human Figure through a Wall. ACM Trans Graph 34(6)

  • Ahmed S, Kallu KD, Ahmed S, et al (2021) Hand Gestures Recognition Using Radar Sensors for Human-Computer-Interaction: A Review. Remote Sensing 13(3)

  • Aigner R, Wigdor D, Benko H, et al (2012) Understanding Mid-Air Hand Gestures: A Study of Human Preferences in Usage of Gesture Types for HCI. Tech. Rep. MSR-TR-2012-111, Microsoft Research

  • Altmann M, Ott P, Stache NC, et al (2021) Multi-Modal Cross Learning for an FMCW Radar Assisted by Thermal and RGB Cameras to Monitor Gestures and Cooking Processes. IEEE Access 9:22,295–22,303

  • Amin MG, Zeng Z, Shan T (2019) Hand Gesture Recognition based on Radar Micro-Doppler Signature Envelopes. In: Proc. IEEE Radar ’19, pp 1–6

  • Andrei AT, Siean AI, Vatavu RD (2022) Tap4Light: Smart Lighting Interactions by Tapping with a Five-Finger Augmentation Device. In: Proceedings of the 13th Augmented Human International Conference. ACM, New York, NY, USA, AH2022

  • Appert C, Zhai S (2009) Using strokes as command shortcuts: cognitive benefits and toolkit support. In: Proceedings of the ACM Int. Conf. on human factors in computing systems. ACM, New York, NY, USA, CHI ’09, pp 2289–2298

  • Ardito C, Buono P, Costabile MF, et al (2015) Interaction with large displays: a survey. ACM Computing Surveys 47(3)

  • Attygalle NT, Leiva LA, Kljun M, et al (2021) No interface, no problem: gesture recognition on physical objects using radar sensing. Sensors 21(17)

  • Avrahami D, Patel M, Yamaura Y, et al (2018) Below the surface: unobtrusive activity recognition for work surfaces using RF-radar sensing. In: Proceedings of the 23rd ACM international conference on intelligent user interfaces. ACM, New York, NY, USA, IUI ’18, pp 439–451

  • Avrahami D, Patel M, Yamaura Y, et al (2019) Unobtrusive activity recognition and position estimation for work surfaces using RF-radar sensing. ACM Trans Interact Intell Syst 10(1)U

  • Bailly G, Vo DB, Lecolinet E, et al (2011) Gesture-aware remote controls: Guidelines and interaction technique. In: Proceedings of the 13th International Conference on Multimodal Interfaces. ACM, New York, NY, USA, ICMI ’11, pp 263–270

  • Bailly G, Müller J, Rohs M, et al (2012) ShoeSense: A New Perspective on Gestural Interaction and Wearable Applications. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, CHI ’12, pp 1239–1248

  • Bannon A, Capraru R, Ritchie M (2020) Exploring gesture recognition with low-cost CW radar modules in comparison to FMCW architectures. In: Proceedings of the IEEE international radar conference, RADAR ’20, pp 744–748

  • Berenguer AD, Oveneke MC, Khalid H, et al (2019) GestureVLAD: Combining Unsupervised Features Representation and Spatio-Temporal Aggregation for Doppler-Radar Gesture Recognition. IEEE Access 7:137,122–137,135

  • Choi JW, Ryu SJ, Kim JH (2019) Short-Range Radar Based Real-Time Hand Gesture Recognition Using LSTM Encoder. IEEE Access 7:33,610–33,618

  • Cirelli M, Nakamura R (2014) A Survey on Multi-Touch Gesture Recognition and Multi-Touch Frameworks. In: Proceedings of the 9th ACM international conference on interactive tabletops and surfaces. ACM, New York, NY, USA, ITS ’14, pp 35–44

  • Cook DJ, Das SK (2004) Smart environments: technologies, protocols, and applications. John Wiley & Sons Inc, Hoboken, New Jersey

    Book  Google Scholar 

  • Cook DJ, Augusto JC, Jakkula VR (2009) Ambient intelligence: technologies, aaplications, and opportunities. Pervasive Mobile Comput 5(4):277–298

    Article  Google Scholar 

  • van Dantzich M, Robbins D, Horvitz E, et al (2002) Scope: Providing Awareness of Multiple Notifications at a Glance. In: Proceedings of the Working Conference on Advanced Visual Interfaces. ACM, New York, NY, USA, AVI ’02, pp 267–281

  • Dekker B, Jacobs S, Kossen A, et al (2017) Gesture recognition with a low power FMCW radar and a deep convolutional neural network. In: Proceedings of European radar conference, EURAD ’17, pp 163–166

  • Epstein B (1998) Script for digital living room conference keynote. https://epstein.org/ambient-intelligence

  • Fan T, Ma C, Gu Z et al (2016) Wireless hand gesture recognition based on continuous-wave doppler radar sensors. IEEE Trans Microwave Theory Tech 64(11):4012–4020

    Article  Google Scholar 

  • Freeman WT, Weissman CD (1995) Television Control by Hand Gestures. In: Proceedings of the IEEE international workshop on automatic face and gesture recognition

  • Gheran BF, Vanderdonckt J, Vatavu RD (2018) Gestures for Smart Rings: Empirical Results, Insights, and Design Implications. In: Proceedings of the ACM Int. Conf. on Designing Interactive Systems. ACM, New York, NY, USA, DIS ’18, pp 623–635

  • Gheran BF, Villarreal-Narvaez S, Vatavu RD, et al (2022) RepliGES and GEStory: visual tools for systematizing and consolidating knowledge on user-defined gestures. In: Proceedings of the 2022 international conference on advanced visual interfaces. ACM, New York, NY, USA, AVI 2022

  • Gigie A, Rani S, Chowdhury A, et al (2019) An Agile Approach for Human Gesture Detection Using Synthetic Radar Data. In: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM international symposium on wearable computers. ACM, New York, NY, USA, UbiComp/ISWC ’19 Adjunct, pp 558–564

  • Goenetxea J, Moreno A, Unzueta L, et al (2010) Interactive and Stereoscopic Hybrid 3D Viewer of Radar Data with Gesture Recognition. In: Graña Romay M, Corchado E, Garcia Sebastian MT (eds) Proceedings of 5th International Conference on Hybrid Artificial Intelligence Systems, HAIS ’10, Lecture Notes in Computer Science, vol 6076. Springer, Berlin, pp 213–220

  • Greenberg S, Marquardt N, Ballendat T et al (2011) Proxemic interactions: The new ubicomp? Interactions 18(1):42–50

    Article  Google Scholar 

  • Hayashi E, Lien J, Gillian N, et al (2021) RadarNet: Efficient Gesture Recognition Technique Utilizing a Miniature Radar Sensor. In: Proceedings of the ACM Int. Conf. on human factors in computing systems. ACM, New York, NY, USA, CHI ’21

  • Hazra S, Santra A (2018) Robust gesture recognition using millimetric-wave radar system. IEEE Sens Lett 2(4):1–4

    Article  Google Scholar 

  • Hazra S, Santra A (2019) Short-Range Radar-Based Gesture Recognition System Using 3D CNN with Triplet Loss. IEEE Access 7:125,623–125,633

  • Helal S, Chandra R, Kravets R (eds) (2013) The 19th annual international conference on mobile computing and networking, MobiCom’13. ACM, New York, NY, USA

  • Islam MR, Lee D, Jahan LS, et al (2018) GlassPass: Tapping Gestures to Unlock Smart Glasses. In: Proceedings of the 9th augmented human international conference. ACM, New York, NY, USA, AH ’18

  • Iwamoto T, Karino A, Hida M, et al (2010) Development of Wall Amusements Utilizing Gesture Input. In: Yang HS, Malaka R, Hoshino J, et al (eds) Proceedings of international conference on entertainment computing, ICEC ’10, Lecture Notes in Computer Science, vol 6243. Springer, Berlin, Heidelberg, pp 499–501

  • Jiang Y, Yin S, Dong J, et al (2021) A review on soft sensors for monitoring, control, and optimization of industrial processes. IEEE Sens J 21(11):12,868–12,881

  • Jones BR, Benko H, Ofek E, et al (2013) IllumiRoom: Peripheral Projected Illusions for Interactive Experiences. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, NY, USA, CHI ’13, pp 869–878

  • Kern N, Steiner M, Lorenzin R et al (2020) Robust doppler-based gesture recognition with incoherent automotive radar sensor networks. IEEE Sens Lett 4(11):1–4

    Article  Google Scholar 

  • Kristensson PO, Zhai S (2004) SHARK2: A Large Vocabulary Shorthand Writing System for Pen-Based Computers. In: Proceedings of the 17th Annual ACM Symposium on User Interface Software and Technology. ACM, New York, NY, USA, UIST ’04, pp 43–52

  • Lee HR, Park J, Suh YJ (2020) Improving classification accuracy of hand gesture recognition based on 60 GHz FMCW radar with deep learning domain adaptation. Electronics 9(12)

  • Lee KS, Lee SP (2010) A Real-Time Audio System for Adjusting the Sweet Spot to the Listener’s Position. IEEE Transactions on Consumer Electronics 56(2):835–843

    Article  Google Scholar 

  • Leiva LA, Kljun M, Sandor C, et al (2020) The Wearable Radar: Sensing Gestures Through Fabrics. In: Proceedings of the 22nd international conference on human-computer interaction with mobile devices and services. ACM, New York, NY, USA, MobileHCI ’20

  • Li C, Peng Z, Huang TY et al (2017) A review on recent progress of portable short-range noncontact microwave radar systems. IEEE Trans Microwave Theory Tech 65(5):1692–1706

    Article  Google Scholar 

  • Li Z, Robucci R, Banerjee N, et al (2015) Tongue-n-Cheek: Non-Contact Tongue Gesture Recognition. In: Proceedings of the 14th international conference on information processing in sensor networks. ACM, New York, NY, USA, IPSN ’15, pp 95–105

  • Liberati A, Altman D, Tetzlaff J et al (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol 62:e1-34

    Article  Google Scholar 

  • Lien J, Gillian N, Karagozler ME, et al (2016) Soli: Ubiquitous Gesture Sensing with Millimeter Wave Radar. ACM Trans Graph 35(4)

  • Liu H, Wang Y, Zhou A, et al (2020) Real-time arm gesture recognition in smart home scenarios via millimeter wave sensing. Proc ACM Interact Mob Wearable Ubiquitous Technol 4(4)

  • Liu Z, Liu X, Zhang J et al (2019) Opportunities and challenges of wireless human sensing for the smart IoT world: a survey. IEEE Netw 33(5):104–110

    Article  Google Scholar 

  • Lucero A, Mason J, Wiethoff A et al (2016) Rethinking our interactions with light. Interactions 23(6):54–59

    Article  Google Scholar 

  • Magrofuoco N, Pérez-Medina JL, Roselli P, et al (2019) Eliciting contact-based and contactless gestures with radar-based sensors. IEEE Access 7:176,982–176,997

  • Magrofuoco N, Roselli P, Vanderdonckt J (2021) Two-dimensional stroke gesture recognition: a survey. ACM Comput Surv 54(7)

  • Marquardt N, Greenberg S (2015) Proxemic interactions: from theory to practice. Springer, Cham

    Book  Google Scholar 

  • Molchanov P, Gupta S, Kim K, et al (2015) Short-range FMCW monopulse radar for hand-gesture sensing. In: Proceedings of the IEEE Int. Conf. on radar conference, RadarCon ’15, pp 1491–1496

  • Nacenta MA, Kamber Y, Qiang Y, et al (2013) Memorability of Pre-Designed and User-Defined Gesture Sets. In: Proceedings of the ACM Int. Conf. on human factors in computing systems. ACM, New York, NY, USA, CHI ’13, pp 1099–1108

  • Offermans SA, Essen HA, Eggen JH (2014) User interaction with everyday lighting systems. Personal Ubiquitous Comput 18(8):2035–2055

    Article  Google Scholar 

  • Palipana S, Salami D, Leiva LA, et al (2021) Pantomime: Mid-Air Gesture Recognition with Sparse Millimeter-Wave Radar Point Clouds. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies 5(1):27:1–27:27

  • Patra A, Geuer P, Munari A, et al (2018) Mm-Wave Radar Based Gesture Recognition: Development and Evaluation of a Low-Power, Low-Complexity System. In: Proceedings of the 2nd ACM workshop on millimeter wave networks and sensing systems. ACM, New York, NY, USA, mmNets ’18, pp 51–56

  • Poupyrev I, Gong NW, Fukuhara S, et al (2016) Project Jacquard: Interactive Digital Textiles at Scale. In: Proceedings of the 2016 CHI conference on human factors in computing systems. ACM, New York, NY, USA, CHI ’16, pp 4216–4227

  • Čopič Pucihar K, Sandor C, Kljun M, et al (2019) The Missing Interface: Micro-Gestures on Augmented Objects. In: Extended Abstracts of the 2019 CHI conference on human factors in computing systems. ACM, New York, NY, USA, CHI EA ’19, pp 1–6

  • Čopič Pucihar K, Attygalle NT, Kljun M, et al (2022) Solids on Soli: Millimetre-wave radar sensing through materials. Proc ACM Hum-Comput Interact 6(EICS)

  • Rekik Y, Vatavu RD, Grisoni L (2014) Understanding Users’ Perceived Difficulty of Multi-Touch Gesture Articulation. In: Proceedings of the 16th ACM Int. Conf. on multimodal interaction. ACM, New York, NY, USA, ICMI ’14, pp 232–239

  • Reski N, Alissandrakis A, Kerren A (2020) Exploration of Time-Oriented Data in Immersive Virtual Reality Using a 3D Radar Chart Approach. In: Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society. ACM, New York, NY, USA, NordiCHI ’20

  • Sakamoto T, Gao X, Yavari E, et al (2017) Radar-based hand gesture recognition using I-Q echo plot and convolutional neural network. In: Proceedings of the IEEE Int. Conf. on antenna measurements & applications, CAMA ’17, pp 393–395

  • Sakamoto T, Gao X, Yavari E et al (2018) Hand gesture recognition using a radar echo I-Q plot and a convolutional neural network. IEEE Sens Lett 2(3):1–4

    Article  Google Scholar 

  • Salami D, Hasibi R, Palipana S, et al (2022) Tesla-Rapture: A Lightweight Gesture Recognition System from mmWave Radar Sparse Point Clouds. IEEE Trans Mobile Comput

  • Sang Y, Shi L, Liu Y (2018) Micro hand gesture recognition system using ultrasonic active Sensing. IEEE Access 6:49,339–49,347

  • Santhalingam PS, Du Y, Wilkerson R, et al (2020) Expressive ASL Recognition using Millimeter-wave Wireless Signals. In: Proceedings of 17th annual IEEE international conference on sensing, communication, and networking, SECON ’20, pp 1–9

  • Schipor OA, Vatavu RD (2018) Invisible, inaudible, and impalpable: Users’ preferences and memory performance for digital content in thin air. IEEE Pervasive Comput 17(4):76–85

    Article  Google Scholar 

  • Shaker G, Smith K, Omer AE et al (2018) Non-invasive monitoring of glucose level changes utilizing a Mm-Wave radar system. Int J Mobile Hum Comput Interaction (IJMHCI) 10(3):10–29

    Article  Google Scholar 

  • Shui PL, Liu HW, Bao Z (2009) Range-spread target detection based on cross time-frequency distribution features of two adjacent received signals. IEEE Trans Signal Process 57(10):3733–3745

    Article  MathSciNet  MATH  Google Scholar 

  • Siddaway AP, Wood AM, Hedges LV (2019) How to do a systematic review: a best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses. Ann Rev Psychol 70(1):747–770

    Article  Google Scholar 

  • Siean AI, Pamparău C, Vatavu RD (2022) Scenario-Based Exploration of Integrating Radar Sensing into Everyday Objects for Free-Hand Television Control. In: ACM international conference on interactive media experiences. ACM, New York, NY, USA, IMX ’22, pp 357–362

  • Skaria S, Al-Hourani A, Lech M et al (2019) Hand-gesture recognition using two-antenna doppler radar with deep convolutional neural networks. IEEE Sens J 19(8):3041–3048

    Article  Google Scholar 

  • Skolnik M (2008) The Radar Handbook, 3rd edn. Technology & Engineering, McGraw-Hill Education

    Google Scholar 

  • Sluÿters A, Lambot S, Vanderdonckt J (2022) Hand Gesture Recognition for an Off-the-Shelf Radar by Electromagnetic Modeling and Inversion. In: Proceedings of 27th ACM international conference on intelligent user interfaces. ACM, New York, NY, USA, IUI ’22, pp 506–522

  • Stec K, Larsen LB (2018) Gestures for Controlling a Moveable TV. In: Proceedings of the 2018 ACM International conference on interactive experiences for TV and online video. ACM, New York, NY, USA, TVX ’18, pp 5–14

  • Sun Y, Fei T, Schliep F, et al (2018) Gesture Classification with Handcrafted Micro-Doppler Features using a FMCW Radar. In: Proceedings of the IEEE MTT-S International conference on microwaves for intelligent mobility, ICMIM ’18, pp 1–4

  • Townley A (2018) Broadband Mm-wave transceivers for sensing and communication. Tech. rep., No. UCB/EECS-2020-25

  • Vanattenhoven J, Geerts D, Vanderdonckt J, et al (2019) The impact of comfortable viewing positions on smart TV gestures. In: Proceedings of the international conference on information systems and computer science, INCISCOS ’19, pp 296–303

  • Vatavu RD (2012) User-Defined Gestures for Free-Hand TV Control. In: Proceedings of the 10th European conference on interactive TV and video. ACM, New York, NY, USA, EuroITV ’12, pp 45–48

  • Vatavu RD (2017) Beyond features for recognition: human-readable measures to understand users’ whole-body gesture performance. Int J Hum-Comput Interaction 33(9):713–730

    Article  Google Scholar 

  • Vatavu RD (2017) Smart-Pockets: body-deictic gestures for fast access to personal data during ambient interactions. Int J Hum-Comput Stud 103:1–21

    Article  Google Scholar 

  • Vatavu RD (2022) Are ambient intelligence and augmented reality two sides of the same coin? Implications for human-computer interaction. In: Extended Abstracts of the 2022 CHI conference on human factors in computing systems. ACM, New York, NY, USA, CHI EA ’22

  • Vatavu RD (2023) iFAD gestures: understanding users’ gesture input performance with index-finger augmentation devices. In: Proceedings of the CHI conference on human factors in computing systems, CHI ’23

  • Vatavu RD, Bilius LB (2021) GestuRING: A web-based tool for designing gesture input with rings, ring-like, and ring-Ready Devices. In: Proceedings of the 34th ACM symposium on user interface software and technology. ACM, New York, NY, USA, UIST ’21, pp 710–723

  • Vatavu RD, Pentiuc SG (2008) Interactive coffee tables: interfacing TV within an intuitive, fun and shared experience. In: Proceedings of the 6th European conference on changing television environments. Springer-Verlag, Berlin, Heidelberg, EUROITV ’08, pp 183–187

  • Velloso E, Schmidt D, Alexander J, et al (2015) The feet in human–computer interaction: a survey of foot-based interaction. ACM Computing Surveys 48(2)

  • Villarreal-Narvaez S, Vanderdonckt J, Vatavu RD, et al (2020) A Systematic Review of Gesture Elicitation Studies: What Can We Learn from 216 Studies? In: Proceedings of the ACM Int. Conf. on Designing Interactive Systems. ACM, New York, NY, USA, DIS ’20, pp 855–872

  • Villarreal-Narvaez S, Şiean AI, Sluÿters A, et al (2022) Informing Future Gesture Elicitation Studies for Interactive Applications That Use Radar Sensing. In: Proceedings of the ACM international conference on advanced cisual interfaces. ACM, New York, NY, USA, AVI ’22

  • Wan Q, Li Y, Li C, et al (2014) Gesture recognition for smart home applications using portable radar sensors. In: Proceedings of the 36th annual international conference of the IEEE engineering in medicine and biology society, pp 6414–6417

  • Wang L, Cui Z, Cao Z, et al (2019a) Fine-Grained Gesture Recognition Based on High Resolution Range Profiles of Terahertz Radar. In: Proceedings of the 2019 IEEE international geoscience and remote sensing symposium, IGARSS ’19, pp 1470–1473

  • Wang P, Lin J, Wang F, et al (2020a) A Gesture Air-Writing Tracking Method that Uses 24 GHz SIMO Radar SoC. IEEE Access 8:152,728–152,741

  • Wang S, Song J, Lien J, et al (2016) Interacting with Soli: Exploring Fine-Grained Dynamic Gesture Recognition in the Radio-Frequency Spectrum. In: Proceedings of the 29th annual symposium on user interface software and technology. ACM, New York, NY, USA, UIST ’16, pp 851–860

  • Wang X, Min R, Cui Z, et al (2020b) Micro gesture recognition with terahertz radar based on diagonal profile of range-doppler map. In: Proceedings of the IEEE international geoscience and remote sensing symposium, IGARSS ’20, pp 770–773

  • Wang Y, Wang S, Zhou M, et al (2019b) TS-I3D based hand gesture recognition method with radar sensor. IEEE Access 7:22,902–22,913

  • Wang Z, Lou X, Yu Z et al (2019) Enabling non-invasive and real-time human-machine interactions based on wireless sensing and fog computing. Personal Ubiquitous Comput 23(1):29–41

    Article  Google Scholar 

  • Wang Z, Yu Z, Lou X et al (2021) Gesture-radar: a dual doppler radar based system for robust recognition and quantitative profiling of human gestures. IEEE Trans Hum-Mach Syst 51(1):32–43

    Article  Google Scholar 

  • Weiser M, Gold R, Brown JS (1999) The origins of ubiquitous computing research at PARC in the late 1980s. IBM Syst J 38(4):693–696

    Article  Google Scholar 

  • Wohlin C (2014) Guidelines for Snowballing in Systematic Literature Studies and a Replication in Software Engineering. In: Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering. ACM, New York, NY, USA, EASE ’14

  • Xia Z, Luomei Y, Zhou C et al (2021) Multidimensional feature representation and learning for robust hand-gesture recognition on commercial millimeter-wave radar. IEEE Trans Geosci Remote Sens 59(6):4749–4764

    Article  Google Scholar 

  • Xuan L, Daisong G, Moli Z, et al (2019) Comparison on User Experience of Mid-Air Gesture Interaction and Traditional Remotes Control. In: Proceedings of the seventh international symposium of Chinese CHI. ACM, New York, NY, USA, Chinese CHI ’19, pp 16–22

  • Yan Y, Yu C, Yi X, et al (2018) HeadGesture: Hands-Free Input Approach Leveraging Head Movements for HMD Devices. Proc ACM Interact Mob Wearable Ubiquitous Technol 2(4)

  • Yeo HS, Quigley A (2017) Radar sensing in human-computer interaction. Interactions 25(1):70–73

    Article  Google Scholar 

  • Yeo HS, Ens B, Quigley A (2017) Tangible UI by Object and Material Classification with Radar. In: SIGGRAPH Asia 2017 Emerging Technologies. ACM, New York, NY, USA, SA ’17

  • Yeo HS, Minami R, Rodriguez K, et al (2018) Exploring tangible interactions with radar sensing. Proc ACM Interact Mob Wearable Ubiquitous Technol 2(4)

  • Zhang G, Lan S, Zhang K, et al (2020a) Temporal-Range-Doppler Features Interpretation and Recognition of Hand Gestures Using mmW FMCW Radar Sensors. In: Proceedings of the 14th European Conference on Antennas and Propagation, EuCAP ’20, pp 1–4

  • Zhang K, Yu Z, Zhang D, et al (2020b) RaCon: A gesture recognition approach via Doppler radar for intelligent human-robot interaction. In: Proceedings of the IEEE international conference on pervasive computing and communications workshops, pp 1–6

  • Zhang L, Tan S, Yang J (2017) Hearing Your Voice is Not Enough: An Articulatory Gesture Based Liveness Detection for Voice Authentication. In: Proceedings of the ACM SIGSAC conference on computer and communications Security. ACM, New York, NY, USA, CCS ’17, pp 57–71

  • Zhang Z, Tian Z, Mu Z, et al (2018a) Application of FMCW Radar for Dynamic Continuous Hand Gesture Recognition. In: Proceedings of the 11th EAI international conference on mobile multimedia communications. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), Brussels, Belgium, MOBIMEDIA ’18, pp 298–303

  • Zhang Z, Tian Z, Zhou M (2018) Latern: dynamic continuous hand gesture recognition using FMCW radar sensor. IEEE Sens J 18(8):3278–3289

    Article  Google Scholar 

  • Zheng L, Bai J, Zhu X, et al (2021) Dynamic Hand Gesture Recognition in In-Vehicle Environment Based on FMCW Radar and Transformer. Sensors 21(19)

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Acknowledgements

The authors acknowledge funding received from Wallonie-Bruxelles International (WBI), Belgium under grant no. SUB/2021/519018 and UEFISCDI, Romania under grant no. PN-III-CEI-BIM-PBE-2020-0001 (1BM/2021).

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  1. MintViz Lab, MANSiD Research Center, Ştefan cel Mare University of Suceava, Suceava, 720229, Romania

    Alexandru-Ionuţ Şiean, Cristian Pamparău & Radu-Daniel Vatavu

  2. Louvain School of Management, LouRIM, Université catholique de Louvain, Louvain-la-Neuve, 1348, Belgium

    Arthur Sluÿters & Jean Vanderdonckt

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Şiean, AI., Pamparău, C., Sluÿters, A. et al. Flexible gesture input with radars: systematic literature review and taxonomy of radar sensing integration in ambient intelligence environments. J Ambient Intell Human Comput 14, 7967–7981 (2023). https://doi.org/10.1007/s12652-023-04606-9

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