Assessment of Low-Density Force Myography Armband for Classification of Upper Limb Gestures
Abstract
:1. Introduction
2. Materials and Methods
2.1. FMG Sensors
2.2. Data Acquisition and Transfer Setup
2.3. Experimental Setup
2.4. Subjects
2.5. Experimental Protocol
2.5.1. Static Limb Position Protocol
Effect of the Number of Sensors
Effect of Sampling Rate
2.5.2. Dynamic Limb Position Protocol
2.6. Data Collection
3. Results
3.1. Classification Performance of the Static Protocol
3.1.1. Effect of the Number of Sensors on Classification Accuracy
3.1.2. Effect of Sampling Rates on Classification Accuracy
3.2. Classification Performance of the Dynamic Protocol
3.3. Effect of Limb Position Variation on Hand Gestures
3.4. Effect of Limb Position Variations on Wrist and Forearm Gestures
4. Discussion
5. Conclusions
- The study results indicate that the number of sensors used in the FMG band significantly impacts its performance, with the 7S FMG band showing higher classification accuracy than the 5S and 3S FMG bands.
- Compared to the number of sensors used, the sampling rate has less of an impact on the performance of the FMG band. The study also determined that a sampling rate of 10 Hz or above is sufficient for recognizing upper limb movements.
- The developed LD-FMG band demonstrates good discrimination between different gestures in static protocols compared to dynamic protocols.
- Among variations in limb positions, the shoulder joint had the least effect on the prediction accuracy of the gestures. However, it was also observed that limb positions affected fine finger movements (hand gestures) and hand movements (wrist and forearm movements).
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Biryukova, E.V.; Yourovskaya, V. A model of human hand dynamics. In Advances in the Biomechanics of the Hand and Wrist; Springer: Berlin/Heidelberg, Germany, 1994; pp. 107–122. [Google Scholar]
- Carey, S.L.; Lura, D.J.; Highsmith, M.J. Differences in myoelectric and body-powered upper-limb prostheses: Systematic literature review. J. Rehabil. Res. Dev. 2015, 52, 247–262. [Google Scholar] [CrossRef] [PubMed]
- Puchhammer, G.J.M. Michelangelo 03-A versatile hand prosthesis, featuring superb controllability and sophisticated bio mimicry. J. Rehabil. Res. Dev. 2008, 8, 162–163. [Google Scholar]
- Medynski, C.; Rattray, B. Bebionic prosthetic design. In Proceedings of the 2011 MyoElectric Controls/Powered Prosthetics Symposium, Fredericton, NB, Canada, 14–19 August 2011. [Google Scholar]
- Connolly, C. Prosthetic hands from touch bionics. Ind. Robot. 2008, 35, 290–293. [Google Scholar] [CrossRef]
- Geethanjali, P.; Ray, K.; Shanmuganathan, P. Actuation of prosthetic drive using EMG signal. In Proceedings of the TENCON 2009–2009 IEEE Region 10 Conference, Singapore, 23–26 November 2009. [Google Scholar]
- Sikdar, S.; Rangwala, H.; Eastlake, E.B.; Hunt, I.A.; Nelson, A.J.; Devanathan, J.; Shin, A.; Pancrazio, J.J. Novel method for predicting dexterous individual finger movements by imaging muscle activity using a wearable ultrasonic system. IEEE Trans. Neural Syst. Rehabil. Eng. 2013, 22, 69–76. [Google Scholar] [CrossRef] [PubMed]
- Castellini, C.; Artemiadis, P.; Wininger, M.; Ajoudani, A.; Alimusaj, M.; Bicchi, A.; Caputo, B.; Craelius, W.; Dosen, S.; Englehart, K.; et al. Proceedings of the first workshop on peripheral machine interfaces: Going beyond traditional surface electromyography. Front. Neurorobot. 2014, 8, 22. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Phillips, S.L.; Craelius, W. Residual kinetic imaging: A versatile interface for prosthetic control. Robotica 2005, 23, 277–282. [Google Scholar] [CrossRef] [Green Version]
- Yungher, D.A.; Wininger, M.T.; Barr, J.; Craelius, W.; Threlkeld, A.J. Surface muscle pressure as a measure of active and passive behavior of muscles during gait. Med. Eng. Phys. 2011, 33, 464–471. [Google Scholar] [CrossRef]
- Jiang, X.; Merhi, L.-K.; Xiao, Z.G.; Menon, C. Exploration of Force Myography and surface Electromyography in hand gesture classification. Med. Eng. Phys. 2017, 41, 63–73. [Google Scholar] [CrossRef]
- Oskoei, M.A.; Hu, H. Myoelectric control systems—A survey. Biomed. Signal Process. Control. 2007, 2, 275–294. [Google Scholar] [CrossRef]
- Criswell, E. Cram’s Introduction to Surface Electromyography; Jones & Bartlett Publishers: Burlington, MA, USA, 2010. [Google Scholar]
- Esposito, D.; Andreozzi, E.; Fratini, A.; Gargiulo, G.D.; Savino, S.; Niola, V.; Bifulco, P. A piezoresistive sensor to measure muscle contraction and mechanomyography. Sensors 2018, 18, 2553. [Google Scholar] [CrossRef] [Green Version]
- Booth, R.; Goldsmit, P.B. A wrist-worn piezoelectric sensor array for gesture input. J. Med. Biol. Eng. 2018, 38, 284–295. [Google Scholar] [CrossRef]
- Xiao, Z.G.; Menon, C.J.S. A review of force myography research and development. Sensors 2019, 19, 4557. [Google Scholar] [CrossRef] [Green Version]
- Dementyev, A.; Paradiso, J. WristFlex: Low-power gesture input with wrist-worn pressure sensors. In Proceedings of the 27th annual ACM Symposium on User Interface Software and Technology, Honolulu, HI, USA, 5–8 October 2014. [Google Scholar]
- Ha, N.; Withanachchi, G.P.; Yihun, Y. Performance of forearm FMG for estimating hand gestures and prosthetic hand control. J. Bionic Eng. 2019, 16, 88–98. [Google Scholar] [CrossRef]
- Connan, M.; Ramírez, E.R.; Vodermayer, B.; Castellini, C. Assessment of a wearable force-and electromyography device and comparison of the related signals for myocontrol. Front. Neurorobot. 2016, 10, 17. [Google Scholar] [CrossRef] [Green Version]
- Xiao, Z.G.; Menon, C. Towards the development of a wearable feedback system for monitoring the activities of the upper-extremities. J. Neuroeng. Rehabil. 2014, 11, 2. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Radmand, A.; Scheme, E.; Englehart, K. High-resolution muscle pressure mapping for upper-limb prosthetic control. In Proceedings of the MEC–Myoelectric Control Symposium, Fredericton, NB, Canada, 18–22 August 2014. [Google Scholar]
- Castellini, C.; Kõiva, R.; Pasluosta, C.; Viegas, C.; Eskofier, B.M. Tactile myography: An off-line assessment of able-bodied subjects and one upper-limb amputee. Technologies 2018, 6, 38. [Google Scholar] [CrossRef] [Green Version]
- Ravindra, V.; Castellini, C. A comparative analysis of three non-invasive human-machine interfaces for the disabled. Front. Neurorobot. 2014, 8, 24. [Google Scholar] [CrossRef] [Green Version]
- Li, N.; Yang, D.; Jiang, L.; Liu, H.; Cai, H. Combined use of FSR sensor array and SVM classifier for finger motion recognition based on pressure distribution map. J. Bionic Eng. 2012, 9, 39–47. [Google Scholar] [CrossRef]
- Wininger, M. Pressure signature of forearm as predictor of grip force. J. Rehabil. Res. Dev. 2008, 45, 883–892. [Google Scholar] [CrossRef]
- Ahmadizadeh, C.; Merhi, L.-K.; Pousett, B.; Sangha, S.; Menon, C. Toward intuitive prosthetic control: Solving common issues using force myography, surface electromyography, and pattern recognition in a pilot case study. IEEE Robot. Autom. Mag. 2017, 24, 102–111. [Google Scholar] [CrossRef]
- Xiao, Z.G.; Menon, C. Performance of forearm FMG and sEMG for estimating elbow, forearm and wrist positions. J. Bionic Eng. 2017, 14, 284–295. [Google Scholar] [CrossRef]
- Radmand, A.; Scheme, E.; Englehart, K. High-density force myography: A possible alternative for upper-limb prosthetic control. J. Rehabil. Res. Dev. 2016, 53, 443–456. [Google Scholar] [CrossRef]
- Cho, E.; Chen, R.; Merhi, L.-K.; Xiao, Z.; Pousett, B.; Menon, C. Force myography to control robotic upper extremity prostheses: A feasibility study. Front. Bioeng. Biotechnol. 2016, 4, 18. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kadkhodayan, A.; Jiang, X.; Menon, C. Continuous prediction of finger movements using force myography. J. Med. Biol. Eng. 2016, 36, 594–604. [Google Scholar] [CrossRef]
- Sadarangani, G.P.; Jiang, X.; Simpson, L.A.; Eng, J.J.; Menon, C. Force myography for monitoring grasping in individuals with stroke with mild to moderate upper-extremity impairments: A preliminary investigation in a controlled environment. Front. Bioeng. Biotechnol. 2017, 5, 42. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Nowak, M.; Eiband, T.; Castellini, C. Multi-modal myocontrol: Testing combined force-and electromyography. In Proceedings of the 2017 International Conference on Rehabilitation Robotics (ICORR), London, UK, 17–20 July 2017. [Google Scholar]
- Delva, M.L.; Menon, C. FSR based force myography (FMG) stability throughout non-stationary upper extremity tasks. In Proceedings of the Future Technologies Conference, Vancouver, BC, Canada, 29–30 November 2017. [Google Scholar]
- Ferigo, D.; Merhi, L.-K.; Pousett, B.; Xiao, Z.G.; Menon, C. A case study of a force-myography controlled bionic hand mitigating limb position effect. J. Bionic Eng. 2017, 14, 692–705. [Google Scholar] [CrossRef]
- Anvaripour, M.; Saif, M. Hand gesture recognition using force myography of the forearm activities and optimized features. In Proceedings of the 2018 IEEE International Conference on Industrial Technology (ICIT), Lyon, France, 19–22 February 2018. [Google Scholar]
- Xiao, Z.G.; Menon, C. An investigation on the sampling frequency of the upper-limb force myographic signals. Sensors 2019, 19, 2432. [Google Scholar] [CrossRef] [Green Version]
- Godiyal, A.K.; Singh, U.; Anand, S.; Joshi, D. Analysis of force myography based locomotion patterns. Measurement 2019, 140, 497–503. [Google Scholar] [CrossRef]
- Lei, G.; Zhang, S.; Fang, Y.; Wang, Y.; Zhang, X. Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition. Sensors 2021, 21, 3872. [Google Scholar] [CrossRef]
- Zakia, U.; Menon, C. Dataset on Force Myography for Human–Robot Interactions. Data 2022, 7, 154. [Google Scholar] [CrossRef]
- Prakash, A.; Sharma, N.; Sharma, S. An affordable transradial prosthesis based on force myography sensor. Sens. Actuators A Phys. 2021, 325, 112699. [Google Scholar] [CrossRef]
- Interlink FSR 400 Series Data Sheet. Available online: https://www.interlinkelectronics.com/fsr-400-series (accessed on 10 January 2022).
- Lukowicz, P.; Hanser, F.; Szubski, C.; Schobersberger, W. Detecting and interpreting muscle activity with wearable force sensors. In Proceedings of the International Conference on Pervasive Computing, Dublin, Ireland, 7–10 May 2006. [Google Scholar]
- Junker, H.; Amft, O.; Lukowicz, P.; Tröster, G. Gesture spotting with body-worn inertial sensors to detect user activities. Pattern Recognit. 2008, 41, 2010–2024. [Google Scholar] [CrossRef]
- Ramot, Y.; Haim-Zada, M.; Domb, A.J.; Nyska, A. Biocompatibility and safety of PLA and its copolymers. Adv. Drug Deliv. Rev. 2016, 107, 153–162. [Google Scholar] [CrossRef]
- Qadir, M.U.; Haq, I.U.; Khan, M.A.; Ahmad, M.N.; Shah, K.; Akhtar, N. Design, Development and Evaluation of Novel Force Myography Based 2-Degree of Freedom Transradial Prosthesis. IEEE Access 2021, 9, 130020–130031. [Google Scholar] [CrossRef]
- Peerdeman, B.; Boere, D.; Witteveen, H.; Huis in ’t Veld, R.; Hermens, H.; Stramigioli, S.; Rietman, H.; Veltink, P.; Misra, S. Myoelectric forearm prostheses: State of the art from a user-centered perspective. J. Rehabil. Res. Dev. 2011, 48, 719. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mizuno, H.; Tsujiuchi, N.; Koizumi, T. Forearm motion discrimination technique using real-time EMG signals. In Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, 30 August–3 September 2011. [Google Scholar]
- Sarrafian, S.K.; Melamed, J.L.; Goshgarian, G.M. Study of wrist motion in flexion and extension. Clin. Orthop. Relat. Res. 1977, 126, 153–159. [Google Scholar] [CrossRef]
- Xiong, Y.; Quek, F. Hand motion gesture frequency properties and multimodal discourse analysis. Int. J. Comput. Vis. 2006, 69, 353–371. [Google Scholar] [CrossRef] [Green Version]
- Shoemaker, R. Data acquisition for the twenty-first century with a brief look at the Nyquist sampling theorem. In Proceedings of the American Power Conference, Chicago, IL, USA, 13–15 April 1992. [Google Scholar]
- Aizawa, J.; Masuda, T.; Koyama, T.; Nakamaru, K.; Isozaki, K.; Okawa, A.; Morita, S. Three-dimensional motion of the upper extremity joints during various activities of daily living. J. Biomech. 2010, 43, 2915–2922. [Google Scholar] [CrossRef]
- Ha, N.; Withanachchi, G.P.; Yihun, Y. Force myography signal-based hand gesture classification for the implementation of real-time control system to a prosthetic hand. In Proceedings of the 2018 Design of Medical Devices Conference; ASME International, Minneapolis, MN, USA, 10–12 December 2018. [Google Scholar]
- Mitchell, B.; Whited, L. Anatomy, Shoulder and Upper Limb, Forearm Muscles; StatPearls: Treasure Island, FL, USA, 2019. [Google Scholar]
- Donatelli, R.A. Physical Therapy of the Shoulder-E-Book; Elsevier Health Sciences: Amsterdam, The Netherlands, 2011. [Google Scholar]
- Farrell, T.R.; Weir, R.F. The optimal controller delay for myoelectric prostheses. IEEE Trans. Neural Syst. Rehabil. Eng. 2007, 15, 111–118. [Google Scholar] [CrossRef] [PubMed]
- Paternò, L.; Dhokia, V.; Menciassi, A.; Bilzon, J.; Seminati, E. A personalised prosthetic liner with embedded sensor technology: A case study. Biomed. Eng. Online 2020, 19, 71. [Google Scholar] [CrossRef] [PubMed]
- Anany, D.; Lucas, G.; Waris, H.; Chi-Hung, Y.; Liarokapis, M. A soft exoglove equipped with a wearable muscle-machine interface based on forcemyography and electromyography. IEEE Robot. Autom. Lett. 2019, 4, 3240–3246. [Google Scholar]
Ref. No. | Year | Sensor’s Quantity | Sampling Rate (Hz) | Sensor Housing | Protocol Type |
---|---|---|---|---|---|
[24] | 2012 | 32 | 100 | - | Static |
[23] | 2014 | 10 | 10 | No | Static |
[20,29] | 2014 | 8 | 10 | No | Static |
[30] | 2016 | 8 | 30 | No | Dynamic |
[21] | 2016 | 126 | 20 | Socket | Static and Dynamic |
[19] | 2016 | 10 | 10 | Yes | Static |
[31] | 2017 | 6 | 10 | No | Dynamic |
[32] | 2017 | 10 | 196 | Yes | Dynamic |
[33] | 2017 | 10 | 16 | Yes | Static |
[11] | 2017 | 16 | 10 | No | Static |
[26] | 2017 | 16 | 10 | Socket | Dynamic |
[34] | 2017 | 80 | 10 | Socket | Static and Dynamic |
[35] | 2018 | 8 | 10 | No | Static |
[36] | 2019 | 8 | 1k | No | Static |
[18] | 2019 | 3 | 25 | Yes | Static |
[37] | 2019 | 8 | 100 | - | Dynamic |
[38] | 2021 | 16 | 1–1k | No | Static |
[39] | 2022 | 16 | 10–50 | No | Dynamic |
Session | Sequence Steps | No. of Sensors | Sampling Rate |
---|---|---|---|
1 | 1 | 3 | 10 |
2 | 5 | 10 | |
3 | 7 | 10 | |
2 | 1 | 7 | 5 |
2 | 7 | 20 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Rehman, M.U.; Shah, K.; Haq, I.U.; Iqbal, S.; Ismail, M.A.; Selimefendigil, F. Assessment of Low-Density Force Myography Armband for Classification of Upper Limb Gestures. Sensors 2023, 23, 2716. https://doi.org/10.3390/s23052716
Rehman MU, Shah K, Haq IU, Iqbal S, Ismail MA, Selimefendigil F. Assessment of Low-Density Force Myography Armband for Classification of Upper Limb Gestures. Sensors. 2023; 23(5):2716. https://doi.org/10.3390/s23052716
Chicago/Turabian StyleRehman, Mustafa Ur, Kamran Shah, Izhar Ul Haq, Sajid Iqbal, Mohamed A. Ismail, and Fatih Selimefendigil. 2023. "Assessment of Low-Density Force Myography Armband for Classification of Upper Limb Gestures" Sensors 23, no. 5: 2716. https://doi.org/10.3390/s23052716
APA StyleRehman, M. U., Shah, K., Haq, I. U., Iqbal, S., Ismail, M. A., & Selimefendigil, F. (2023). Assessment of Low-Density Force Myography Armband for Classification of Upper Limb Gestures. Sensors, 23(5), 2716. https://doi.org/10.3390/s23052716