Smart ArM: a customizable and versatile robotic arm prosthesis platform for Cybathlon and research.
Arm prosthesis
Cybathlon
Rehabilitation engineering
Research test-bed
Robotic arm
Journal
Journal of neuroengineering and rehabilitation
ISSN: 1743-0003
Titre abrégé: J Neuroeng Rehabil
Pays: England
ID NLM: 101232233
Informations de publication
Date de publication:
05 Aug 2024
05 Aug 2024
Historique:
received:
12
02
2024
accepted:
17
07
2024
medline:
6
8
2024
pubmed:
6
8
2024
entrez:
5
8
2024
Statut:
epublish
Résumé
In the last decade, notable progress in mechatronics paved the way for a new generation of arm prostheses, expanding motor capabilities thanks to their multiple active joints. Yet, the design of control schemes for these advanced devices still poses a challenge, especially with the limited availability of command signals for higher levels of arm impairment. When addressing this challenge, current commercial devices lack versatility and customizing options to be employed as test-beds for developing novel control schemes. As a consequence, researchers resort to using lab-specific experimental apparatuses on which to deploy their innovations, such as virtual reality setups or mock prosthetic devices worn by unimpaired participants. To meet this need for a test-bed, we developed the Smart Arm platform, a human-like, multi-articulated robotic arm that can be worn as a trans-humeral arm prosthesis. The design process followed three principles: provide a reprogrammable embedded system allowing in-depth customization of control schemes, favor easy-to-buy parts rather than custom-made components, and guarantee compatibility with industrial standards in prosthetics. The Smart ArM platform includes motorized elbow and wrist joints while being compatible with commercial prosthetic hands. Its software and electronic architecture can be easily adapted to build devices with a wide variety of sensors and actuators. This platform was employed in several experiments studying arm prosthesis control and sensory feedback. We also report our participation in Cybathlon, where our pilot with forearm agenesia successfully drives the Smart Arm prosthesis to perform activities of daily living requiring both strength and dexterity. These application scenarios illustrate the versatility and adaptability of the proposed platform, for research purposes as well as outside the lab. The Smart Arm platform offers a test-bed for experimenting with prosthetic control laws and command signals, suitable for running tests in lifelike settings where impaired participants wear it as a prosthetic device. In this way, we aim at bridging a critical gap in the field of upper limb prosthetics: the need for realistic, ecological test conditions to assess the actual benefit of a technological innovation for the end-users.
Sections du résumé
BACKGROUND
BACKGROUND
In the last decade, notable progress in mechatronics paved the way for a new generation of arm prostheses, expanding motor capabilities thanks to their multiple active joints. Yet, the design of control schemes for these advanced devices still poses a challenge, especially with the limited availability of command signals for higher levels of arm impairment. When addressing this challenge, current commercial devices lack versatility and customizing options to be employed as test-beds for developing novel control schemes. As a consequence, researchers resort to using lab-specific experimental apparatuses on which to deploy their innovations, such as virtual reality setups or mock prosthetic devices worn by unimpaired participants.
METHODS
METHODS
To meet this need for a test-bed, we developed the Smart Arm platform, a human-like, multi-articulated robotic arm that can be worn as a trans-humeral arm prosthesis. The design process followed three principles: provide a reprogrammable embedded system allowing in-depth customization of control schemes, favor easy-to-buy parts rather than custom-made components, and guarantee compatibility with industrial standards in prosthetics.
RESULTS
RESULTS
The Smart ArM platform includes motorized elbow and wrist joints while being compatible with commercial prosthetic hands. Its software and electronic architecture can be easily adapted to build devices with a wide variety of sensors and actuators. This platform was employed in several experiments studying arm prosthesis control and sensory feedback. We also report our participation in Cybathlon, where our pilot with forearm agenesia successfully drives the Smart Arm prosthesis to perform activities of daily living requiring both strength and dexterity.
CONCLUSION
CONCLUSIONS
These application scenarios illustrate the versatility and adaptability of the proposed platform, for research purposes as well as outside the lab. The Smart Arm platform offers a test-bed for experimenting with prosthetic control laws and command signals, suitable for running tests in lifelike settings where impaired participants wear it as a prosthetic device. In this way, we aim at bridging a critical gap in the field of upper limb prosthetics: the need for realistic, ecological test conditions to assess the actual benefit of a technological innovation for the end-users.
Identifiants
pubmed: 39103888
doi: 10.1186/s12984-024-01423-9
pii: 10.1186/s12984-024-01423-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
136Subventions
Organisme : Agence Nationale de la Recherche
ID : BYCEPS, ANR-18-CE19-0004
Informations de copyright
© 2024. The Author(s).
Références
Castellini C, Artemiadis P, Wininger M, Ajoudani A, Alimusaj M, Bicchi A, et al. Proceedings of the first workshop on peripheral machine interfaces: Going beyond traditional surface electromyography. Front Neurorobot. 2014;8:22.
doi: 10.3389/fnbot.2014.00022
pubmed: 25177292
pmcid: 4133701
Johannes MS, Faulring EL, Katyal KD, Para MP, Helder JB, Makhlin A, et al. The modular prosthetic limb. In: Wearable robotics. Elsevier; 2020. pp. 393–444.
Cipriani C, Controzzi M, Carrozza MC. The SmartHand transradial prosthesis. J Neuroeng Rehabil. 2011;8(1):29.
doi: 10.1186/1743-0003-8-29
pubmed: 21600048
pmcid: 3120755
Bennett DA, Mitchell JE, Truex D, Goldfarb M. Design of a myoelectric transhumeral prosthesis. IEEE/ASME Trans Mechatron. 2016;21(4):1868–79.
doi: 10.1109/TMECH.2016.2552999
Kyberd PJ, Poulton AS, Sandsjö L, Jönsson S, Jones B, Gow D. The ToMPAW modular prosthesis: a platform for research in upper-limb prosthetics. J Prosthet Orthot.. 2007;19(1):15–21.
doi: 10.1097/JPO.0b013e31802d46f8
Kashiwakura J, Alva PGS, Guerra IM, Bona C, Atashzar SF, Farina D. Task-oriented design of a multi-degree of freedom upper limb prosthesis with integrated myocontrol and sensory feedback. IEEE Trans Med Robot Bion. 2023.
Grebenstein M, Albu-Schäffer A, Bahls T, Chalon M, Eiberger O, Friedl W, et al. The DLR hand arm system. In: 2011 IEEE International Conference on Robotics and Automation. IEEE; 2011. pp. 3175–82.
Bandara D, Gopura R, Hemapala K, Kiguchi K. Development of a multi-DoF transhumeral robotic arm prosthesis. Med Eng Physics. 2017;48:131–41.
doi: 10.1016/j.medengphy.2017.06.034
Dawson MR, Sherstan C, Carey JP, Hebert JS, Pilarski PM. Development of the Bento Arm: an improved robotic arm for myoelectric training and research. Proc MEC. 2014;14:60–4.
Krausz NE, Rorrer RA, Weir RF. Design and fabrication of a six degree-of-freedom open source hand. IEEE Trans Neural Syst Rehabil Eng. 2016;24(5):562–72.
doi: 10.1109/TNSRE.2015.2440177
pubmed: 26087495
Stoelen MF, de Azambuja R, López Rodríguez B, Bonsignorio F, Cangelosi A. The GummiArm project: a replicable and variable-stiffness robot arm for experiments on embodied AI. Front Neurorobot. 2022;16.
doi: 10.3389/fnbot.2022.836772
pubmed: 35360828
pmcid: 8963345
Nurpeissova A, Tursynbekov T, Shintemirov A. An Open-Source Mechanical Design of ALARIS Hand: A 6-DOF Anthropomorphic Robotic Hand. In: 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2021:1177–83.
Blana D, Kyriacou T, Lambrecht JM, Chadwick EK. Feasibility of using combined EMG and kinematic signals for prosthesis control: a simulation study using a virtual reality environment. J Electromyogr Kinesiol. 2016;29:21–7.
doi: 10.1016/j.jelekin.2015.06.010
pubmed: 26190031
pmcid: 4940208
Hauschild M, Davoodi R, Loeb GE. A virtual reality environment for designing and fitting neural prosthetic limbs. IEEE Trans Neural Syst Rehabil Eng. 2007;15(1):9–15.
doi: 10.1109/TNSRE.2007.891369
pubmed: 17436870
Phelan I, Arden M, Garcia C, Roast C. Exploring virtual reality and prosthetic training. In: Virtual reality (VR). IEEE; 2015. pp. 353–4.
Kaliki RR, Davoodi R, Loeb GE. Evaluation of a noninvasive command scheme for upper-limb prostheses in a virtual reality reach and grasp task. IEEE Trans Biomed Eng. 2013;60(3):792–802.
doi: 10.1109/TBME.2012.2185494
pubmed: 22287229
Mick S, Segas E, Dure L, Halgand C, Benois-Pineau J, Loeb GE, et al. Shoulder kinematics plus contextual target information enable control of multiple distal joints of a simulated prosthetic arm and hand. J NeuroEng Rehabil.. 2021;18:1–17.
doi: 10.1186/s12984-020-00793-0
Segas E, Mick S, Leconte V, Dubois O, Klotz R, Cattaert D, etal. Intuitive movement-based prosthesis control enables arm amputees to reach naturally in virtual reality. Elife. 2023;12:RP87317.
Garcia-Rosas R, Oetomo D, Manzie C, Tan Y, Choong P. Task-space synergies for reaching using upper-limb prostheses. IEEE Trans Neural Syst Rehabil Eng. 2020;28(12):2966–77.
doi: 10.1109/TNSRE.2020.3036320
pubmed: 33151883
ten Kate J, Smit G, Breedveld P. 3D-printed upper limb prostheses: a review. Disabil Rehabil Assist Technol.. 2017;12(3):300–14.
doi: 10.1080/17483107.2016.1253117
pubmed: 28152642
Vujaklija I, Farina D. 3D printed upper limb prosthetics. Expert Rev Med Devices. 2018;15(7):505–12.
doi: 10.1080/17434440.2018.1494568
pubmed: 29949397
Dunai L, Novak M, García Espert C. Human hand anatomy-based prosthetic hand. Sensors. 2020;21(1):137.
doi: 10.3390/s21010137
pubmed: 33379252
pmcid: 7795667
Weiner P, Starke J, Hundhausen F, Beil J, Asfour T. The Kit prosthetic hand: design and control. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; 2018. pp. 3328–34.
Laffranchi M, Boccardo N, Traverso S, Lombardi L, Canepa M, Lince A, et al. The Hannes hand prosthesis replicates the key biological properties of the human hand. Sci Robot. 2020;5(46):eabb0467.
Controzzi M, Clemente F, Barone D, Ghionzoli A, Cipriani C. The SSSA-MyHand: a dexterous lightweight myoelectric hand prosthesis. IEEE Trans Neural Syst Rehabil Eng. 2016;25(5):459–68.
doi: 10.1109/TNSRE.2016.2578980
pubmed: 27305682
Cuellar JS, Plettenburg D, Zadpoor AA, Breedveld P, Smit G. Design of a 3D-printed hand prosthesis featuring articulated bio-inspired fingers. Proc Inst Mech Eng, Part H J Eng Med. 2021;235(3):336–45.
doi: 10.1177/0954411920980889
Torres EB, Zipser D. Simultaneous control of hand displacements and rotations in orientation-matching experiments. J Appl Physiol. 2004;96(5):1978–87.
doi: 10.1152/japplphysiol.00872.2003
pubmed: 14688032
Marchand C, Mick S, Jarrassé N. Team Smart ArM; 2024. https://team-sam.fr . Accessed 4 Jan 2024.
OttoBock SE. Michaelangelo/Axon-Bus System; 2024. https://shop.ottobock.us/Prosthetics/Upper-Limb-Prosthetics/Michelangelo-Axon-Bus-System/c/2006 . Accessed 4 Jan 2024.
De Leva P. Adjustments to Zatsiorsky-Seluyanov’s segment inertia parameters. J Biomech. 1996;29(9):1223–30.
doi: 10.1016/0021-9290(95)00178-6
pubmed: 8872282
Taska Prosthetics Ltd. TASKA Hand—Specifications; 2024. https://www.taskaprosthetics.com/support/download . Accessed 4 Jan 2024.
Legrand M, Marchand C, Richer F, Touillet A, Martinet N, Paysant J, et al. Simultaneous control of 2DOF upper-limb prosthesis with body compensations-based control: a multiple cases study. IEEE Trans Neural Syst Rehabil Eng. 2022;30:1745–54.
doi: 10.1109/TNSRE.2022.3186266
pubmed: 35749322
Resnik L, Klinger SL, Etter K. The DEKA Arm: its features, functionality, and evolution during the veterans affairs study to optimize the DEKA Arm. Prosthet Orthot Int. 2014;38(6):492–504.
doi: 10.1177/0309364613506913
pubmed: 24150930
Mobius Bionics LLC. LUKE Arm Detail Page; 2024. https://mobiusbionics.com/luke-arm/ . Accessed 4 Jan 2024.
Kuiken TA, Li G, Lock BA, Lipschutz RD, Miller LA, Stubblefield KA, et al. Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. JAMA. 2009;301(6):619–28.
doi: 10.1001/jama.2009.116
pubmed: 19211469
pmcid: 3036162
OttoBock SE. DynamicArm; 2024. https://shop.ottobock.us/Prosthetics/Upper-Limb-Prosthetics/Myoelectric-Elbows/DynamicArm-Elbow/DynamicArm/p/12K100N%7E550 . Accessed 4 Jan 2024.
Cutti AG, Davalli A, Gazzotti V, Ninu A. Performance Evaluation of the New Otto Bock “DynamicArm” by Means of Biomechanical Modelling. In: MEC ’05 Integrating Prosthetics and Medicine: Proceedings of the 2005 MyoElectric Controls/Powered Prosthetics Symposium; 2005. .
Fillauer LLC. U3 Comparison; 2024. https://www.utaharm.com/u3-comparison/ . Accessed 4 Jan 2024.
Toledo C, Leija L, Muñoz R, Vera A, Ramirez A. Upper limb prostheses for amputations above elbow: a review. In: 2009 Pan American Health Care Exchanges (PAHCE); 2009:104–108.
Marchand C, Mick S, Jarrassé N. VIDEOS—Team Smart ArM; 2024. https://team-sam.fr/en/participations/ . Accessed 4 Jan 2024.
Capsi-Morales P, Piazza C, Grioli G, Bicchi A, Catalano MG. The SoftHand Pro platform: a flexible prosthesis with a user-centered approach. J NeuroEng Rehabil. 2023;20(1):20.
doi: 10.1186/s12984-023-01130-x
pubmed: 36755249
pmcid: 9906824
ETH Zurich / CYBATHLON. CYBATHLON 2020 Global Edition Day 1; 2020. https://youtu.be/GamQ9VgAlBI?t=6287 . Accessed 4 Jan 2024.
Kumar S. Theories of musculoskeletal injury causation. Ergonomics. 2001;44(1):17–47.
doi: 10.1080/00140130120716
pubmed: 11214897
Reilly M, Kontson K. Computational musculoskeletal modeling of compensatory movements in the upper limb. J Biomech. 2020;108.
doi: 10.1016/j.jbiomech.2020.109843
pubmed: 32635990
Jarrassé N, De Montalivet E, Richer F, Nicol C, Touillet A, Martinet N, et al. Phantom-mobility-based prosthesis control in transhumeral amputees without surgical reinnervation: a preliminary study. Front Bioeng Biotechnol. 2018;6:164.
doi: 10.3389/fbioe.2018.00164
pubmed: 30555823
pmcid: 6282038
Reilly KT, Sirigu A. The motor cortex and its role in phantom limb phenomena. Neuroscientist. 2008;14(2):195–202.
doi: 10.1177/1073858407309466
pubmed: 17989169
Merad M, de Montalivet E, Legrand M, Mastinu E, Ortiz-Catalan M, Touillet A, et al. Assessment of an automatic prosthetic elbow control strategy using residual limb motion for transhumeral amputated individuals with socket or osseointegrated prostheses. IEEE Trans Med Robot Bion. 2020;2(1):38–49.
doi: 10.1109/TMRB.2020.2970065
Auvray M, Kechabia Y, Arnold G, Jarrassé N. Providing proprioceptive feedback by means of vibrotactile stimuli: a way to improve body integration of a prosthetic arm. In: World Haptics Conference (WHC) 2019;2019.
Campanelli A, Tiboni M, Vérité F, Saudrais C, Mick S, Jarrassé N. Innovative multi vibrotactile-skin stretch (MuViSS) haptic device for sensory motor feedback from a robotic prosthetic hand. Mechatronics. 2024; Accepted, in revision.
Sungeelee V, Jarrassé N, Sanchez T, Caramiaux B. Comparing teaching strategies of a machine learning-based prosthetic arm. In: 29th Annual ACM Conference on Intelligent User Interfaces (IUI); 2024. Accepted, in revision.
Bennett DA, Goldfarb M. IMU-based wrist rotation control of a transradial myoelectric prosthesis. IEEE Trans Neural Syst Rehabil Eng. 2017;26(2):419–27.
doi: 10.1109/TNSRE.2017.2682642
pubmed: 28320673
pmcid: 10734105
Lauretti C, Davalli A, Sacchetti R, Guglielmelli E, Zollo L. Fusion of M-IMU and EMG signals for the control of trans-humeral prostheses. In: 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob). IEEE; 2016. pp. 1123–1128.
Stival F, Michieletto S, DeAgnoi A, Pagello E. Toward a better robotic hand prosthesis control: using EMG and IMU features for a subject independent multi joint regression model. In: 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob). IEEE; 2018. pp. 185–92.