Applying Radical Constructivism to Machine Learning: A Pilot Study in Assistive Robotics
Markus Nowak, Claudio Castellini & Carlo Massironi
Log in to download the full text for free
> Citation
> Similar
> References
> Add Comment
Abstract
Context: In this article we match machine learning (ML) and interactive machine learning (iML) with radical constructivism (RC) to build a tentative radical constructivist framework for iML; we then present a pilot study in which RC-framed iML is applied to assistive robotics, namely upper-limb prosthetics (myocontrol. Problem: Despite more than 40 years of academic research, myocontrol is still unsolved, with rejection rates of up to 75. This is mainly due to its unreliability - the inability to correctly predict the patient’s intent in daily life. Method: We propose a description of the typical problems posed by ML-based myocontrol through the lingo of RC, highlighting the advantages of such a modelisation. We abstract some aspects of RC and project them onto the concepts of ML, to make it evolve into the concept of RC-framed iML. Results: Such a projection leads to the design and development of a myocontrol system based upon RC-framed iML, used to foster the co-adaptation of human and prosthesis. The iML-based myocontrol system is then compared to a traditional ML-based one in a pilot study involving human participants in a goal-reaching task mimicking the control of a prosthetic hand and wrist. Implications: We argue that the usage of RC-framed iML in myocontrol could be of great help to the community of assistive robotics, and that the constructivist perspective can lead to principled design of the system itself, as well as of the training/calibration/co-adaptation procedure. Constructivist content: Ernst von Glasersfeld’s RC is the leading principle pushing for the usage of RC-framed iML; it also provides guidelines for the design of the system, the human/machine interface, the experiments and the experimental setups.
Key words: Machine learning, interactive machine learning, radical constructivism, assistive robotics, human-machine interaction, co-adaptation.
Supplementary Material: Document 1 · Document 2
Citation
Nowak M., Castellini C. & Massironi C. (2018) Applying radical constructivism to machine learning: A pilot study in assistive robotics. Constructivist Foundations 13(2): 250–262. http://constructivist.info/13/2/250
Export article citation data:
Plain Text ·
BibTex ·
EndNote ·
Reference Manager (RIS)
Similar articles
References
Card S. K., Newell A. & Moran T. P. (1983) The psychology of human-computer interaction. Lawrence Erlbaum, Hillsdale NJ.
▸︎ Google︎ Scholar
Castellini C. (2016) Incremental learning of muscle synergies: From calibration to interaction. In: Bianchi M. & Moscatelli A. (eds.) Human and robot hands: Sensorimotor synergies to bridge the gap between neuroscience and robotics. Springer, New York: 171–193.
▸︎ Google︎ Scholar
Castellini C., Artemiadis P., Wininger M., Ajoudani A., Alimusaj M., Bicchi A., Caputo B., Craelius W., Dosen S., Englehart K., Farina D., Gijsberts A., Godfrey S., Hargrove L., Ison M., Kuiken T., Markovic M., Pilarski P., Rupp R. & Scheme E. (2014) Proceedings of the first workshop on peripheral machine interfaces: Going beyond traditional surface electromyography. Frontiers in Neurorobotics 8: 22.
▸︎ Google︎ Scholar
Connan M., Ruiz Ramírez E., Vodermayer B. & Castellini C. (2016) Assessment of a wearable force- and electromyography device and comparison of the related signals for myocontrol. Frontiers in Neurorobotics 10: 17.
▸︎ Google︎ Scholar
Fails J. A. & Olsen D. R. (2003) Interactive machine learning. In: Proceedings of the 8th international conference on Intelligent user interfaces (IUI ’03) ACM, New York NY: 39–45.
▸︎ Google︎ Scholar
Farina D., Jiang N. & Rehbaum H. (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 22(4): 797–809.
▸︎ Google︎ Scholar
Fisher R. A. (1936) The use of multiple measurements in taxonomic problems. Annals of Human Genetics 7(2): 179–188.
▸︎ Google︎ Scholar
Fougner A., Stavdahl Ø., Kyberd P. J., Losier Y. G. & Parker P. A. (2012) Control of upper limb prostheses: Terminology and proportional myoelectric control – A review. IEEE Transactions on Neural Systems and Rehabilitation Engineering 20(5): 663–677.
▸︎ Google︎ Scholar
Gijsberts A., Bohra R., Sierra González D., Werner A., Nowak M., Caputo B., Roa Garzón M. A. & Castellini C. (2014) Stable myoelectric control of a hand prosthesis using non-linear incremental learning. Frontiers in Neurorobotics 8: 8.
▸︎ Google︎ Scholar
Glasersfeld E. von (1983) Learning as constructive activity. In: Bergeron J. C. & Herscovics N. (eds.) Proceedings of the 5th Annual Meeting of the North American Group of Psychology in Mathematics Education, Vol. 1. PME-NA, Montreal: 41–101.
http://cepa.info/1373
Hahne J., Markovic M. & Farina D. (2017) User adaptation in myoelectric man-machine Interfaces. Scientific Reports 7: 4437.
▸︎ Google︎ Scholar
Iturrate I., Chavarriaga R., Montesano L., Minguez J. & del R. Millán J. (2015) Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control. Scientific Reports 5: 13893.
▸︎ Google︎ Scholar
Jiang N., Dosen S., Müller K. R. & Farina D. (2012) Myoelectric control of artificial limbs: Is there a need to change focus? IEEE Signal Processing Magazine 29(5): 148–152.
▸︎ Google︎ Scholar
Lock B., Simon A. M., Stubblefield K. & Hargrove L. (2011) Prosthesis-guided training for practical use of pattern recognition control of prostheses. In: Parker P. A. (ed.) MEC ’11 Raising the standard. UNB, Fredericton: 61–64.
▸︎ Google︎ Scholar
Mathewson K. W. & Pilarski P. M. (2017) Reinforcement learning based embodied agents modelling human users through interaction and multi-sensory perception. In: AAAI spring symposium on interactive multi-sensory object perception for embodied agents, March 27–29, Stanford University, USA: 477–481.
▸︎ Google︎ Scholar
Micera S., Carpaneto J. & Raspopovic S. (2010) Control of hand prostheses using peripheral information. IEEE Reviews in Biomedical Engineering 3: 48–68.
▸︎ Google︎ Scholar
Peerdeman B., Boere D., Witteveen H., in ’t Veld R. H., Hermens H., Stramigioli S., Rietman H., Veltink P. & Misra S. (2011) Myoelectric forearm prostheses: State of the art from a user-centered perspective. Journal of Rehabilitation Research and Development 48(6): 719–738.
▸︎ Google︎ Scholar
Ravindra V. & Castellini C. (2014) A comparative analysis of three non-invasive human-machine interfaces for the disabled. Frontiers in Neurorobotics 8: 24.
▸︎ Google︎ Scholar
Samuel A. L. (1959) Some studies in machine learning using the game of checkers. IBM Journal of Research and Development 3(3): 210–229.
▸︎ Google︎ Scholar
Sarkar A. (2016) Constructivist design for interactive machine learning. In: Proceedings of the 2016 CHI conference extended abstracts on human factors in computing systems (CHI EA ’16) ACM, New York: 1467–1475.
▸︎ Google︎ Scholar
Schweitzer W., Thali M. J. & Egger D. (2018) Case-study of a user-driven prosthetic arm design: Bionic hand versus customized body-powered technology in a highly demanding work environment. Journal of NeuroEngineering and Rehabilitation 15: 1.
▸︎ Google︎ Scholar
Shalev-Shwartz S. & Ben-David S. (2014) Understanding machine learning: From theory to algorithms. Cambridge University Press, New York.
▸︎ Google︎ Scholar
Simon A. M., Hargrove L. J., Lock B. A. & Kuiken T. A. (2011) Target achievement control test: Evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses. Journal of Rehabilitation Research & Development 48(6): 619–628.
▸︎ Google︎ Scholar
Simon A. M., Lock B. A. & Stubblefield K. A. (2012) Patient training for functional use of pattern recognition–controlled prostheses. Journal of Prosthetics and Orthotics 24(2): 56–64.
▸︎ Google︎ Scholar
Strazzulla I., Nowak M., Controzzi M., Cipriani C. & Castellini C. (2017) Online bimanual manipulation using surface electromyography and incremental learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering 25: 227–234.
▸︎ Google︎ Scholar
Wagstaff K. L. (2012) Machine learning that matters. In: Langford J. & Pineau J. (eds.) Proceedings of the 29th international conference on machine learning (ICML-12) Omnipress, Madison WI: 529–536.
▸︎ Google︎ Scholar
Comments: 0
To stay informed about comments to this publication and post comments yourself, please log in first.
