ABSTRACT
In this paper we present a low-intrusive approach to the detection of expressive full-body movement qualities. We focus on two qualities: Lightness and Fragility and we detect them using the data captured by four wearable devices, two Inertial Movement Units (IMU) and two electromyographs (EMG), placed on the forearms. The work we present in the paper stems from a strict collaboration with expressive movement experts (e.g., contemporary dance choreographers) for defining a vocabulary of basic movement qualities. We recorded 13 dancers performing movements expressing the qualities under investigation. The recordings were next segmented and the perceived level of each quality for each segment was ranked by 5 experts using a 5-points Likert scale. We obtained a dataset of 150 segments of movement expressing Fragility and/or Lightness. In the second part of the paper, we define a set of features on IMU and EMG data and we extract them on the recorded corpus. We finally applied a set of supervised machine learning techniques to classify the segments. The best results for the whole dataset were obtained with a Naive Bayes classifier for Lightness (F-score 0.77), and with a Support Vector Machine classifier for Fragility (F-score 0.77). Our approach can be used in ecological contexts e.g., during artistic performances.
- Sarah Fdili Alaoui, Frederic Bevilacqua, and Christian Jacquemin. 2015. Interactive Visuals As Metaphors for Dance Movement Qualities. ACM Trans. Interact. Intell. Syst. 5, 3, Article 13 (Sept. 2015), 24 pages. Google ScholarDigital Library
- P. Alborno, G. Volpe, M. Mancini, R. Niewiadomski, S. Piana, and A. Camurri. 2017. The Multi-Event-Class Synchronization (MECS) Algorithm. Submitted to IEEE Transactions on Human-Machine Systems (2017).Google Scholar
- Zainal Arief, Indra Adji Sulistijono, and Roby Awal Ardiansyah. 2015. Comparison of five time series EMG features extractions using Myo Armband. In Electronics Symposium (IES), 2015 International. IEEE, 11–14.Google ScholarCross Ref
- Anthony H Bateman, Alison H McGregor, Anthony MJ Bull, Peter MM Cashman, and Robert C Schroter. 2006. Assessment of the timing of respiration during rowing and its relationship to spinal kinematics. Biology of Sport 23, 4 (2006), 353.Google Scholar
- Paolo Bernasconi and Jana Kohl. 1993. Analysis of co-ordination between breathing and exercise rhythms in man. J. Physiol 471 (1993), 693–706.Google ScholarCross Ref
- Antonio Camurri, Ingrid Lagerlöf, and Gualtiero Volpe. 2003. Recognizing Emotion from Dance Movement: Comparison of Spectator Recognition and Automated Techniques. Int. J. Hum.-Comput. Stud. 59, 1-2 (2003), 213–225. Google ScholarDigital Library
- Antonio Camurri, Barbara Mazzarino, Matteo Ricchetti, Renee Timmers, and Gualtiero Volpe. 2004. Multimodal Analysis of Expressive Gesture in Music and Dance Performances. In Gesture-Based Communication in Human-Computer Interaction: 5th International Gesture Workshop, GW 2003, Genova, Italy, April 15- 17, 2003, Selected Revised Papers, Antonio Camurri and Gualtiero Volpe (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 20–39.Google ScholarCross Ref
- Antonio Camurri, Barbara Mazzarino, and Gualtiero Volpe. 2004. Expressive interfaces. Cognition, Technology & Work 6, 1 (2004), 15–22. Google ScholarDigital Library
- Antonio Camurri, Gualtiero Volpe, Stefano Piana, Maurizio Mancini, Radoslaw Niewiadomski, Nicola Ferrari, and Corrado Canepa. 2016. The dancer in the eye: towards a multi-layered computational framework of qualities in movement. In Proceedings of the 3rd International Symposium on Movement and Computing. ACM, 6. Google ScholarDigital Library
- George Caridakis, Amaryllis Raouzaiou, Elisabetta Bevacqua, Maurizio Mancini, Kostas Karpouzis, Lori Malatesta, and Catherine Pelachaud. 2007. Virtual agent multimodal mimicry of humans. Language Resources and Evaluation 41, 3 (2007), 367–388.Google ScholarCross Ref
- Simon Fothergill, Helena M. Mentis, Pushmeet Kohli, and Sebastian Nowozin. 2012. Instructing People for Training Gestural Interactive Systems. ACM, 1737– 1746. Google ScholarDigital Library
- Rui Alberto Esteves Freixo. 2015. Electromyography and inertial sensor-based gesture detection and control. Master’s thesis. Universidade de Porto.Google Scholar
- Kozaburo Hachimura, Katsumi Takashina, and Mitsu Yoshimura. 2005. Analysis and evaluation of dancing movement based on LMA. In Robot and Human Interactive Communication, 2005. ROMAN 2005. IEEE International Workshop on. IEEE, 294–299.Google ScholarCross Ref
- Alexandros Kitsikidis, Kosmas Dimitropoulos, Erdal Yilmaz, Stella Douka, and Nikos Grammalidis. 2014.Google Scholar
- Multi-sensor Technology and Fuzzy Logic for Dancer’s Motion Analysis and Performance Evaluation within a 3D Virtual Environment. In Universal Access in Human-Computer Interaction. Design and Development Methods for Universal Access: 8th International Conference, UAHCI 2014, Held as Part of HCI International 2014, Heraklion, Crete, Greece, June 22-27, 2014, Proceedings, Part I, Constantine Stephanidis and Margherita Antona (Eds.). Springer International Publishing, 379–390.Google Scholar
- Andrea Kleinsmith and Nadia Bianchi-Berthouze. 2013. Affective body expression perception and recognition: A survey. IEEE Transactions on Affective Computing 4, 1 (2013), 15–33. Google ScholarDigital Library
- Rudolf Laban and Frederick Charles Lawrence. 1947. Effort. Macdonald & Evans.Google Scholar
- João Diogo Faria Lopes. 2016. Gesture Spotting from IMU and EMG Data for Human-Robot Interaction. Master’s thesis. Universidade de Coimbra.Google Scholar
- Vincenzo Lussu, Radoslaw Niewiadomski, Gualtiero Volpe, and Antonio Camurri. 2016. Using the Audio Respiration Signal for Multimodal Discrimination of Expressive Movement Qualities. In Human Behavior Understanding: 7th International Workshop, HBU 2016, Amsterdam, The Netherlands, October 16, 2016, Proceedings, Mohamed Chetouani, Jeffrey Cohn, and Albert Ali Salah (Eds.). Springer International Publishing, 102–115.Google Scholar
- Aymeric Masurelle, Slim Essid, and Gaël Richard. 2013. Multimodal classification of dance movements using body joint trajectories and step sounds. In Image Analysis for Multimedia Interactive Services (WIAMIS), 2013 14th International Workshop on. IEEE, 1–4.Google ScholarCross Ref
- Michael Neff and Eugene Fiume. 2003. Aesthetic Edits for Character Animation. In Proceedings of the 2003 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA ’03). Eurographics Association, 239–244. Google ScholarDigital Library
- Radoslaw Niewiadomski, Maurizio Mancini, and Stefano Piana. 2013. Human and virtual agent expressive gesture quality analysis and synthesis. Coverbal Synchrony in Human-Machine Interaction (2013), 269–292.Google Scholar
- Prajwal Paudyal, Ayan Banerjee, and Sandeep K.S. Gupta. 2016. SCEPTRE: A Pervasive, Non-Invasive, and Programmable Gesture Recognition Technology. In Proceedings of the 21st International Conference on Intelligent User Interfaces (IUI ’16). ACM, New York, NY, USA, 282–293. Google ScholarDigital Library
- Rodrigo Quian Quiroga, Thoma Kreuz, and Peter Grassberger. 2002. Event synchronization: A simple and fast method to measure synchronicity and time delay patterns. Phys. Rev. E 66 (Oct 2002), 041904. Issue 4.Google Scholar
- Bernstein Ran, Shafir Tal, Tsachor Rachelle, Studd Karen, and Schuster Assaf. 2015. Multitask Learning for Laban Movement Analysis. In Proceedings of the 2Nd International Workshop on Movement and Computing (MOCO ’15). 37–44. Google ScholarDigital Library
- Dilip Swaminathan, Harvey Thornburg, Jessica Mumford, Stjepan Rajko, Jodi James, Todd Ingalls, Ellen Campana, Gang Qian, Pavithra Sampath, and Bo Peng. 2009. A Dynamic Bayesian Approach to Computational Laban Shape Quality Analysis. Advances in Human-Computer Interaction 362651 (2009), 17. Google ScholarDigital Library
- Mahmoud Tavakoli, Carlo Benussi, and Joao Luis Lourenco. 2017. Single channel surface EMG control of advanced prosthetic hands: A simple, low cost and efficient approach. Expert Systems with Applications 79 (2017), 322 – 332. Google ScholarDigital Library
- Arthur Truong, Hugo Boujut, and Titus Zaharia. 2016. Laban descriptors for gesture recognition and emotional analysis. The Visual Computer 32, 1 (2016), 83–98. Google ScholarDigital Library
- Nicholas Ward, Miguel Ortiz, Francisco Bernardo, and Atau Tanaka. 2016. Designing and Measuring Gesture Using Laban Movement Analysis and Electromyogram. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (UbiComp ’16). ACM, New York, NY, USA, 995–1000. Google ScholarDigital Library
Index Terms
- Low-intrusive recognition of expressive movement qualities
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