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Biophysical music is a rapidly emerging area of electronic music performance. It investigates the creation of unconventional computing interfaces to directly configure the physiology of human movement with musical systems, which often are improvisational and adaptive. It draws on a transdisciplinary approach that combines neuromuscular studies, phenomenology, real-time data analysis, performance practice and music composition. Biophysical music instruments use muscle biosignals to directly integrate aspects of a performer’s physical gesture into the human–machine interaction and musical compositional strategies. This chapter will introduce the principles and challenges of biophysical music, detailing the use of physiological computing for musical performance and in particular the musical applications of muscle-based interaction.
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Beck, T. W., Housh, T. J., Cramer, J. T., Weir, J. P., Johnson, G. O., Coburn, J. W., et al. (2005). Mechanomyographic amplitude and frequency responses during dynamic muscle actions: A comprehensive review. Biomedical Engineering Online,4, 67. CrossRef
Berliner, P. F. (1994). Thinking in Jazz: The infinite art of improvisation. Chicago, IL: The University of Chicago Press. CrossRef
Camurri, A., & Coletta, P. (2007). A Platform for Real-Time Multimodal Processing (pp. 11–13). In 4th Sound and Music Computing Conference, Lefkada, July 2007.
Caramiaux, B. (2012). Studies on the relationship between gesture and sound in musical performance. Ph.D. thesis, University of Paris VI, Paris.
Caramiaux, B., Donnarumma, M., & Tanaka. A. (2015). Understanding gesture expressivity through muscle sensing. ACM Transactions on Computer-Human Interactions, 21(6), 31.
Cardinale, M., & Bosco, C. (2003). the use of vibration as an exercise intervention. Exercise and Sport Sciences Reviews,31(1), 3–7. CrossRef
Day, S. (2002). Important factors in surface EMG measurement. Technical report, Bortec Biomedical Ltd., Calgary.
Dobrian, C., & Koppelman, D. (2006). The ‘E’ in NIME: Musical expression with new computer interfaces (pp. 277–282). In International Conference on New Interfaces for Musical Expression, Paris. IRCAM—Centre Pompidou.
Donnarumma, M. (2011). XTH sense: A study of muscle sounds for an experimental paradigm of musical performance. In Proceedings of the International Computer Music Conference, Huddersfield.
Donnarumma, M. (2012). Incarnated sound in music for flesh II. Defining gesture in biologically informed musical performance. Leonardo Electronic Almanac,18(3), 164–175.
Donnarumma, M. (2014). Ominous: Playfulness and emergence in a performance for biophysical music. Body, Space & Technology.
Donnarumma, M. (2016). Configuring corporeality: Performing bodies, vibrations and new musical instruments. Ph.D. thesis, Goldsmiths, University of London.
Donnarumma, M., Caramiaux, B., & Tanaka, A. (2013a). Body and space: Combining modalities for musical expression. In Work in Progress for the Conference on Tangible, Embedded and Embodied Interaction, Barcelona. UPF–MTG.
Donnarumma, M., Caramiaux, B., & Tanaka, A. (2013b). Muscular interactions combining EMG and MMG sensing for musical practice. In Proceedings of the International Conference on New Interfaces for Musical Expression, Seoul. KAIST.
Dumas, B., Lalanne, D., & Oviatt, S. (2009). Multimodal interfaces: A survey of principles, models and frameworks. Human Machine Interaction, 3–26.
Dykstra-Erickson, E., & Arnowitz, J. (2005). Michel Waisvisz: The man and the hands. Interactions,12(5), 63–67. CrossRef
Fairclough, S. H. (2009). Fundamentals of physiological computing. Interacting with Computers,21(1), 133–145. CrossRef
Farina, D., Jiang, N., Rehbaum, H., Holobar, A., Graimann, B., Dietl, H., et al. (2014). The extraction of neural information from the surface EMG for the control of upper-limb prostheses: Emerging avenues and challenges. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 4320(C).
Fuller, M. (2005). Media ecologies: Materialist energies in art and technoculture. Cambridge, MA: MIT Press.
Gallagher, S. (1986). Body image and body schema: A conceptual clarification. The Journal of Mind and Behavior,7(4), 541–554.
Gallagher, S. (2001). Dimensions of embodiment: Body image and body schema in medical contexts. In S. Kay Toombs (Ed.), Phenomenology and medicine (pp. 147–175). Dordrecht: Kluwer Academic Publishers.
Godøy, R. (2003). Motor-mimetic music cognition. Leonardo,36(4), 317–319. CrossRef
Hofmann, D. (2013). Myoelectric Signal processing for prosthesis control. Ph.D. thesis, Gottingen Universität.
Hunt, A. (2000). Mapping strategies for musical performance. In M. M. Wanderley & M. Battier (Eds.), Trends in gestural control of music (pp. 231–258). Paris: IRCAM.
Islam, M. A., Sundaraj, K., Ahmad, R., Ahamed, N. U., & Ali, M. A. (2013). Mechanomyography Sensor development, related signal processing, and applications: A systematic review. IEEE Sensors Journal,13(7), 2499–2516. CrossRef
Jobe, F. W., Tibone, J. E., Perry, J., & Moynes, D. (1983). An EMG analysis of the shoulder in throwing and pitching. A preliminary report. The American Journal of Sports Medicine,11(1), 3–5. CrossRef
Jordà, S. (2005). Digital Lutherie: Crafting musical computers for new musics’ performance and improvisation. Ph.D. thesis, Unversitat Pompeu Fabra.
Kaniusas, E. (2012). Biomedical signals and sensors I. Linking physiological phenomena and biosignals. Biological and Medical Physics, Biomedical Engineering. Berlin: Springer.
Keogh, J., & Sugden, D. (1985). Movement skill development. New York, NY: Macmillan Publishing Co.
Knapp, R. B., & Lusted, H. S. (1988). A real-time digital signal processing system for bioelectric control of music (pp. 2556–2557). In Acoustics, Speech, and Signal Processing (ICASSP-88).
Knapp, R. B., & Lusted, H. S. (1990). A bioelectric controller for computer music applications. Computer Music Journal,14(1), 42–47. CrossRef
Latash, M. (2008). Neurophysiological basis of movement, 2nd (editio ed.). Champaign, IL: Human Kinetics.
Leman, M. (2008). Embodied music cognition and mediation technology. Cambridge, MA: MIT Press.
Lotze, M., Scheler, G., Tan, H.-R., Braun, C., & Birbaumer, N. (2003). The musician’s brain: Functional imaging of amateurs and professionals during performance and imagery. NeuroImage,20(3), 1817–1829. CrossRef
Lucier, A. (1976). Statement on: Music for solo performer. In D. Rosenboom (Ed.), Biofeedback and the Arts: Results of early experiments (pp. 60–61). Vancouver, BC, Canada: Aesthetic Research Centre of Canada, A.R.C.
Madeleine, P., Bajaj, P., Søgaard, K., & Arendt-Nielsen, L. (2001). Mechanomyography and electromyography force relationships during concentric, isometric and eccentric contractions. Journal of Electromyography and Kinesiology,11(2), 113–121. CrossRef
Medeiros, C., & Wanderley, M. (2014). A comprehensive review of sensors and instrumentation methods in devices for musical expression. Sensors,14(8), 13556–13591. CrossRef
Merleau-Ponty, M. (1962). Phenomenology of perception. Ebbw Vale: Routledge.
Merletti, R., & Parker, P. A. (2004). Electromyography: Physiology, engineering, and non-invasive applications. Hoboken, NJ: Wiley. CrossRef
Miranda, E. R., & Castet, J. (Eds.). (2014). Guide to brain-computer music interfacing. Berlin: Springer.
Moore, R. F. (1988). The dysfunction of MIDI. Computer Music Journal,12(1), 19–28. CrossRef
Nagashima, Y. (1998). Biosensorfusion: New interfaces for interactive multimedia art (number 1, pp. 8–11). In Proceedings of the International Computer Music Conference.
Oster, G., & Jaffe, J. S. (1980). Low frequency sounds from sustained contraction of human skeletal muscle. Biophysical Journal,30(1), 119–127. CrossRef
Oviatt, S., Coulston, R., Tomko, S., Xiao, B., Lunsford, R., Wesson, M., et al. (2003). Toward a theory of organized multimodal integration patterns during human-computer interaction (p. 44). In Proceedings of the International Conference on Multimodal Interfaces.
Rokeby, D. (1985). Dreams of an instrument maker. In Musicworks 20: Sound constructions. Toronto: The Music Gallery.
Rosenboom, D. (1990). Extended musical interface with the human nervous system, number 1. Leonardo.
Ryan, J. (1991). Some remarks on musical instrument design at STEIM. Contemporary Music Review,6(1), 3–17. CrossRef
Schmidt, R. A., & Lee, T. (1988). Motor control and learning (5th ed.). Champaign, IL: Human Kinetics.
Sheets-Johnston, M. (1999). The primacy of movement (2nd ed.). Amsterdam: John Benjamins Publishing Company. CrossRef
Silva, H., Carreiras, C., Lourenco, A., & Fred, A. (2013). Off-the-person electrocardiography (pp. 99–106). In Proceedings of the International Congress on Cardiovascular Technologies.
Silva, J., Heim, W., & Chau, T. (2004). MMG-based classification of muscle activity for prosthesis control (Vol. 2, pp. 968–71). In International Conference of the IEEE Engineering in Medicine and Biology Society.
Sudnow, D. (1978). Ways of the hand: The organization of improvised conduct. Cambridge, MA: Harvard University Press.
Tajadura-Jiménez, A., Fairhurst, M. T., Marquardt, N., & Bianchi-berthouze, N. (2015). As light as your footsteps: Altering walking sounds to change perceived body weight, emotional state and gait (pp. 2943–2952). In Proceedings of the ACM Conference on Human Factors in Computing Systems, Seoul. ACM.
Tanaka, A. (1993). Musical technical issues in using interactive instrument technology with application to the BioMuse (pp. 124–126). In Proceedings of the International Computer Music Conference.
Tanaka, A. (2012). The use of electromyogram signals (EMG) in musical performance: A personal survey of two decades of practice. eContact! Biotechnological Performance Practice / Pratiques de performance biotechnologique, 14.2.
Tanaka, A., & Knapp, R. B. (2002). Multimodal interaction in music using the electromyogram and relative position sensing (pp. 1–6). In Proceedings of the 2002 Conference on New Interfaces for Musical Expression.
Tarata, M. (2009). The electromyogram and mechanomyogram in monitoring neuromuscular fatigue: Techniques, results, potential use within the dynamic effort (pp. 67–77). In MEASUREMENT, Proceedings of the 7th International Conference, Smolenice.
Van Nort, D. (2015). [radical] signals from life: From muscle sensing to embodied machine listening/learning within a large-scale performance piece. In Proceedings of the International Conference on Movement and Computing (MoCo), Montreal, QC, Canada.
Wessel, D., & Wright, M. (2002). Problems and prospects for intimate musical control of computers. Computer Music Journal,26(3), 11–22. CrossRef
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