Electronic Sources
Myoelectric Prostheses:
Ottobock: Myoelectric Prosthesis 101.
“Myoelectric Prosthetics 101.” Ottobock., www.ottobockus.com/prosthetics/info-for-new-amputees/prosthetics-101/myoelectric-prosthetics-101/.
-Information from Ottobock about one of their myoelectric prostheses formatted for patients that are looking to purchase the system. Includes explanation of how the system works and is powered as well as potential features. Moving forward, this is an important source because the myoelectric prosthesis that we are modifying is from Ottobock.
Myoelectric control of prosthetic hands: state-of-the-art review
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4968852/
Some really great info about feature extraction and pattern recognition. Good resource moving forward with theoretical portions of project
Myoelectric and Body Power, Design Options for Upper-Limb Prosthesis
Stevens, Phil M., and M. Jason Highsmith. “Myoelectric and Body Power, Design Options for Upper-Limb Prostheses.” Journal of Prosthetics and Orthotics, vol. 29, no. 4S, Oct. 2017, pp. P1–P3., doi:10.1097/jpo.0000000000000150.
-Editorial article about the benefits of myoelectric and body-powered upper-limb prosthetics and their control strategies. Includes information about how many upper-limb amputees exist (~500,000), with approximately 41,000 being considered major amputations. On top of this information about market size, information about market potential and funding is presented, including the fact that myoelectric prostheses are not considered necessary by funding sources like insurance.
journals.lww.com/jpojournal/Abstract/2017/10001/Myoelectric_and_Body_Power,_Design_Options_for.1.aspx (includes other references)
The Reality of Myoelectric Prostheses: Understanding What Makes These Devices Difficult for Some Users to Control
Chadwell, Alix, et al. “The Reality of Myoelectric Prostheses: Understanding What Makes These Devices Difficult for Some Users to Control.” Frontiers in Neurorobotics, vol. 10, 22 Aug. 2016, doi:10.3389/fnbot.2018.00015.
– Interesting article about the evaluation of effective myoelectric prosthetic use. They did a lot of EMG evaluation for the characterization of amputee ability for muscular control. Could use this as a good example of translating EMG data to characteristics in the prosthesis itself. Also, this article includes some interesting human factor aspects that need to be considered in the design process if we want to account for the psychosocial aspects.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4992705/
Surface EMG Sensors and Neural Stimulation:
Refined Myoelectric Control in Below-Elbow Amputees Using Artificial Neural Networks and a Data Glove
Sebelius, Fredrik CP, Birgitta N. Rosen, and Göran N. Lundborg. “Refined myoelectric control in below-elbow amputees using artificial neural networks and a data glove.” The Journal of hand surgery 30.4 (2005): 780-789.
This article discusses a glove that was programmed using an artificial network to mimic the actions of the hand using EMG signals. Shows examples of the hand positioning used in learning data sets.
https://www.sciencedirect.com/science/article/pii/S0363502305000109
A human-assisting manipulator teleoperated by EMG signals and arm motions
O. Fukuda, T. Tsuji, M. Kaneko and A. Otsuka, “A human-assisting manipulator teleoperated by EMG signals and arm motions,” in IEEE Transactions on Robotics and Automation, vol. 19, no. 2, pp. 210-222, April 2003. doi: 10.1109/TRA.2003.808873
When using EMG signals for a prosthesis, you should place electrodes on the forearm muscles. When looking at the signals, the signal patterns look very different at the beginning and the end of the signal, so if using a neural network, you must use a large amount of learning data and many learning iterations.
https://ieeexplore.ieee.org/abstract/document/1192150
Neural Networks for Online Classification of Hand and Finger Movements Using Surface EMG signals
G. Tsenov, A. H. Zeghbib, F. Palis, N. Shoylev and V. Mladenov, “Neural Networks for Online Classification of Hand and Finger Movements Using Surface EMG signals,” 2006 8th Seminar on Neural Network Applications in Electrical Engineering, Belgrade, Serbia & Montenegro, 2006, pp. 167-171.
doi: 10.1109/NEUREL.2006.341203
EMG signals can be used to convert amputee muscle movement into myoelectric signals used in prostheses. Shows example electrode placement and discusses signal processing.
https://ieeexplore.ieee.org/abstract/document/4147191
An Epidermal Stimulation and Sensing Platform for Sensorimotor Prosthetic Control, Management of Lower Back Exertion, and Electrical Muscle Activation
Xu, Baoxing, et al. “Flexible Electronics: An Epidermal Stimulation and Sensing Platform for Sensorimotor Prosthetic Control, Management of Lower Back Exertion, and Electrical Muscle Activation (Adv. Mater. 22/2016).” Advanced Materials, vol. 28, no. 22, 2016, pp. 4563–4563., doi:10.1002/adma.201670154.
-Paper by from the University of Virginia’s Department of Mechanical and
Aerospace Engineering on a skin-mounted sensor. They which acquire EMG, temperature, and strain signals. In addition, these sensors provide sensory information through “simulation electrodes.” These electrodes provide electrical simulation which simulate pressure, vibration, touch, etc.
https://onlinelibrary.wiley.com/doi/full/10.1002/adma.201504155
Online Electromyographic Control of a Robotic Prosthesis
Shenoy, P., Miller, K., Crawford, B., & Rao, R. (2008). Online Electromyographic Control of a Robotic Prosthesis. IEEE Transactions on Biomedical Engineering, 55(3), 1128-1135. doi:10.1109/tbme.2007.909536
-Shows pictures of electrode placement used
Quantifying Pattern Recognition – Based Myoelectric contril of Multifunctional Transradial Prostheses
Li, G., & Kuiken, T. (2010). EMG pattern recognition control of multifunctional prostheses by transradial amputees. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(2), 185-192. doi:10.1109/iembs.2009.5333628
-Shows more pictures of electrode placement, study is about quantifying performance improvement when pattern classification algorithms were used
Control Systems:
Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control – A Review
A. Fougner, O Stavdahl, P. Kyberd, Y. Losier, P. Parker, ‘Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control – A Review’, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 20, p. 663-677, 2012.
-Review article covering a taxonomy of terminology commonly used to describe control systems of myoeletric protheses. The paper provides a landscape view of the main components of a myoelectric control system: preprocessing, intent interpretation, and output. It subdivides each of these components into hardware and software as well as where commercially available and state of the art research falls in the landscape of these subdivisions. A focus is placed on intent interpretation and the various methods employed to capture various degrees of freedom, accuracy, and functionality.
https://www.ncbi.nlm.nih.gov/pubmed/22665514
An Alternative Myoelectric Pattern Recognition Approach for the Control of Hand Prostheses: A Case Study of Use in Daily Life by a Dysmelia Subject
Mastinu, Enzo et al. “An Alternative Myoelectric Pattern Recognition Approach for the Control of Hand Prostheses: A Case Study of Use in Daily Life by a Dysmelia Subject” IEEE journal of translational engineering in health and medicine vol. 6 2600112. 12 Mar. 2018, doi:10.1109/JTEHM.2018.2811458
-Novel approach to advanced myoelectric control concerning both hand opening and closing as well as more complex multifunctional motions. The goal of the study was to determine its functionality and reliability in a daily setting. In terms of the results of the study, the group found that users reported more intuitive control when selecting the different grips, but also a higher uncertainty during proportional continuous movements. Additionally, the transition to daily use introduced EMG disturbances not seen in laboratory such as motion and skin contact disruption.
-Useful additional information gained:
Determining Electrode Placement- BioPatRec Matlab software
MPR Control Chain/Scheme- A robust, real-time control scheme for multifunction myoelectric control by Englehart K, Hudgins B
Artificial Limb Controller- Embedded System for Prosthetic Control Using Implanted Neuromuscular Interfaces Accessed Via an Osseointegrated Implant by Mastinu E, Doguet P, Botquin Y, Hakansson B, Ortiz-Catalan M- contains software library for preprocessing, windowing, feature extraction, pattern classification, motor control
Parameters- Sample Frequency (1000 Hz), Sample Gathering (sliding time window of 100 ms with 50 ms time increments)
Proportional activation for hand open and close was implemented using the mean absolute value of the most active channel per movement, analogously to standard DC
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5881457/
Pairwise Linear Discriminant Analysis of Electromyographic signals
De Silva, Oscar, et al. “Pairwise Linear Discriminant Analysis of Electromyographic
signals” Memorial University of Newfoundland.
-Approach to creating and implementing a pairwise linear discriminant analysis on electromyographic signals. Contains further classification schema for multi-functional prostheses that are not applicable. Provides a good layout and understanding of the facets involved in an LDA of electromyographic signals with data segmentation, feature extraction, and feature projection.
Important Details:
Sample Rate- 1000 Hz
Data segmentation- minimum speed needed to overcome delay is 300 ms
Feature extraction- Mean absolute value, zero crossing, slope sign changes, signal length
Market Potential:
Forecast of the robotic prosthetic market in the United States:
https://www.grandviewresearch.com/industry-analysis/robotic-prosthetics-market
Analysis of market value and forecast, dynamics, and segmentation:
https://www.futuremarketinsights.com/reports/orthopaedic-prosthetics-marke