Comparison product of muscle contraction strength measuring device based on specifications and its uses

Rizal Mustofa, Budi Setiyana, Hari Peni Julianti, Rifky Ismail

Article ID: 2384
Vol 4, Issue 1, 2023
DOI: https://doi.org/10.54517/wt.v4i1.2384
VIEWS - 132 (Abstract)

Abstract

Currently, technological developments have been used in several health cases. One technology used for health is a tool to measure the strength of muscle contractions. So far, measuring muscle contractions still uses manual methods, namely the measurement muscle strength test method. Apart from that, health workers also measure manually by feeling the muscles to be measured. The need for tools to measure muscle contractions in the medical world is quite large because these tools can be used for various needs of doctors and nurses. There are many commercialized products on the market. The first aim of this article is to review four products that are available on the market. The second aim is to provide an overview of the use of the four products that have been carried out by previous researchers and the results. This article also discusses various aspects of product specifications. The research results show that each product has its own advantages. When we compare these products, it is better for us to return to the kind of product we are looking for. For example, if we want a product with high-class features that is equipped with several games, then we can choose MyoBoy. Myo armband and Trigno™ are used to identify several movement force conditions that are influenced by muscle strength, which has been equipped with an Inertial Measurement Unit (IMU) sensor. MyoWare is used to make bionic hands or bionic legs that can be controlled using electromyography (EMG) and has a relatively economical price.


Keywords

muscle contraction; surface electro myograph; non-invasive measurement; medical device

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References

1. Bi L, Feleke AG, Guan C. A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration. Biomedical Signal Processing and Control 2019; 51: 113–127. doi: 10.1016/j.bspc.2019.02.011

2. Hakonen M, Piitulainen H, Visala A. Current state of digital signal processing in myoelectric interfaces and related applications. Biomedical Signal Processing and Control 2015; 18: 334–359. doi: 10.1016/j.bspc.2015.02.009

3. Smith LH, Hargrove LJ. Comparison of surface and intramuscular EMG pattern recognition for simultaneous wrist/hand motion classification. In: Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 3–7 July 2013; Osaka, Japan. pp. 4223–4226. doi: 10.1109/embc.2013.6610477

4. Rajaratnam BS. A comparison of EMG signals from surface and fine-wire electrodes during shoulder abduction. International Journal of Physical Medicine & Rehabilitation 2014; 2(4): 1000206. doi: 10.4172/2329-9096.1000206

5. Kamavuako EN, Englehart KB, Jensen W, Farina D. Simultaneous and proportional force estimation in multiple degrees of freedom from intramuscular EMG. IEEE Transactions on Biomedical Engineering 2012; 59(7): 1804–1807. doi: 10.1109/tbme.2012.2197210

6. Kamavuako EN, Scheme EJ, Englehart KB. On the usability of intramuscular EMG for prosthetic control: A Fitts’ law approach. Journal of Electromyography and Kinesiology 2014; 24(5): 770–777. doi: 10.1016/j.jelekin.2014.06.009

7. Fu YL, Liang KC, Song W, Huang J. A hybrid approach to product prototype usability testing based on surface EMG images and convolutional neural network classification. Computer Methods and Programs in Biomedicine 2022; 221: 106870. doi: 10.1016/j.cmpb.2022.106870

8. Drost G, Stegeman DF, van Engelen BGM, Zwarts MJ. Clinical applications of high-density surface EMG: A systematic review. Journal of Electromyography and Kinesiology 2006; 16(6): 586–602. doi: 10.1016/j.jelekin.2006.09.005

9. Nikolaidis S, Hsu D, Srinivasa S. Human-robot mutual adaptation in collaborative tasks: Models and experiments. The International Journal of Robotics Research 2017; 36(5–7): 618–634. doi: 10.1177/0278364917690593

10. Mörtl A, Lawitzky M, Kucukyilmaz A, et al. The role of roles: Physical cooperation between humans and robots. The International Journal of Robotics Research 2012; 31(13): 1656–1674. doi: 10.1177/0278364912455366

11. Jarrassé N, Sanguineti V, Burdet E. Slaves no longer: Review on role assignment for human-robot joint motor action. Adaptive Behavior 2013; 22(1): 70–82. doi: 10.1177/1059712313481044

12. Lenzi T, De Rossi SMM, Vitiello N, Carrozza MC. Intention-based EMG control for powered exoskeletons. IEEE Transactions on Biomedical Engineering 2012; 59(8): 2180–2190. doi: 10.1109/tbme.2012.2198821

13. Pulliam CL, Lambrecht JM, Kirsch RF. Electromyogram-based neural network control of transhumeral prostheses. The Journal of Rehabilitation Research and Development 2011; 48(6): 739. doi: 10.1682/jrrd.2010.12.0237

14. Shafti A, Lazpita BU, Elhage O, et al. Analysis of comfort and ergonomics for clinical work environments. In: Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 16–20 August 2016; Orlando, FL, USA. pp. 1894–1897. doi: 10.1109/embc.2016.7591091

15. Wu Y, Martínez MÁ, Balaguer PO. Overview of the application of EMG recording in the diagnosis and approach of neurological disorders. In: Turker H (editor). Electrodiagnosis in New Frontiers of Clinical Research. IntechOpen; 2013. pp. 1–23. doi: 10.5772/56030

16. Chu YB, Parasuraman S, Khan MKAA. Electromyography (EMG) and human locomotion. Procedia Engineering 2012; 41: 486–492. doi: 10.1016/j.proeng.2012.07.202

17. Turker H, Sozen H. Surface electromyography in sports and exercise. In: Turker H (editor). Electrodiagnosis in New Frontiers of Clinical Research. IntechOpen; 2013. pp. 175–194. doi: 10.5772/56167

18. Ajoudani A, Tsagarakis N, Bicchi A. Tele-impedance: Teleoperation with impedance regulation using a body–machine interface. The International Journal of Robotics Research 2012; 31(13): 1642–1656. doi: 10.1177/0278364912464668

19. Lindner T, Schulze C, Woitge S, et al. The effect of the weight of equipment on muscle activity of the lower extremity in soldiers. The Scientific World Journal 2012; 2012: 1–8. doi: 10.1100/2012/976513

20. Tao W, Lai ZH, Leu MC, Yin Z. Worker activity recognition in smart manufacturing using IMU and sEMG signals with convolutional neural networks. Procedia Manufacturing 2018; 26: 1159–1166. doi: 10.1016/j.promfg.2018.07.152

21. Ali Hashim H, Mohammed SL, Gharghan SK. Accurate fall detection for patients with Parkinson’s disease based on a data event algorithm and wireless sensor nodes. Measurement 2020; 156: 107573. doi: 10.1016/j.measurement.2020.107573

22. Peterson V, Galván C, Hernández H, Spies R. A feasibility study of a complete low-cost consumer-grade brain-computer interface system. Heliyon 2020; 6(3): e03425. doi: 10.1016/j.heliyon.2020.e03425

23. Ottobock. MyoBoy. Available online: https://shop.ottobock.us/Prosthetics/Upper-Limb-Prosthetics/Myo-Hands-and-Components/Myo-Software/MyoBoy/p/757M11~5X-CHANGE (accessed on 31 March 2023).

24. Pauk J, Daunoraviciene K, Ziziene J, et al. Classification of muscle activity patterns in healthy children using biclustering algorithm. Biomedical Signal Processing and Control 2023; 84: 104731. doi: 10.1016/j.bspc.2023.104731

25. Lee YJ, Wei MY, Chen YJ. Multiple inertial measurement unit combination and location for recognizing general, fatigue, and simulated-fatigue gait. Gait & Posture 2022; 96: 330–337. doi: 10.1016/j.gaitpost.2022.06.011

26. Amazon. Myo gesture control Armband (white). Available online: https://camelcamelcamel.com/Myo-Gesture-Control-Armband-White/product/B00O69U344 (accessed on 13 July 2023).

27. SparkFun. MyoWare 2.0 muscle sensor. Available online: https://www.sparkfun.com/products/21265 (accessed on 29 March 2023).

28. MyoWare. MyoWare muscle sensor (AT-04-001). Available online: https://cdn.sparkfun.com/datasheets/Sensors/Biometric/MyowareUserManualAT-04-001.pdf (accessed on 3 January 2023).

29. Manualzz. MyoBoy® 757M11. Available online: https://manualzz.com/doc/en/4571090/myoboy%C2%AE-757m11 (accessed on 13 July 2023).

30. Prahm C, Kayali F, Sturma A, Aszmann O. PlayBionic: Game‐based interventions to encourage patient engagement and performance in prosthetic motor rehabilitation. PM&R 2018; 10(11): 1252–1260. doi: 10.1016/j.pmrj.2018.09.027

31. Delsys. Trigno sensor adhesive interface. Available online: https://delsys.com/shop/supplies-and-accessories/trigno-adhesive-interface/ (accessed on 13 July 2023).

32. Delsys Incorporated. Trigno® Wireless Biofeedback System: User’s Guide. Delsys Incorporated; 2021.

33. Bangaru SS, Wang C, Busam SA, Aghazadeh F. ANN-based automated scaffold builder activity recognition through wearable EMG and IMU sensors. Automation in Construction 2021; 126: 103653. doi: 10.1016/j.autcon.2021.103653

34. Tepe C, Erdim M. Classification of surface electromyography and gyroscopic signals of finger gestures acquired by Myo armband using machine learning methods. Biomedical Signal Processing and Control 2022; 75: 103588. doi: 10.1016/j.bspc.2022.103588

35. Tepe C, Demir MC. The effects of the number of channels and gyroscopic data on the classification performance in EMG data acquired by Myo armband. Journal of Computational Science 2021; 51: 101348. doi: 10.1016/j.jocs.2021.101348

36. Tepe C, Demir MC. Real-time classification of EMG Myo armband data using support vector machine. IRBM 2022; 43(4): 300–308. doi: 10.1016/j.irbm.2022.06.001

37. Heywood S, Pua YH, McClelland J, et al. Low-cost electromyography—Validation against a commercial system using both manual and automated activation timing thresholds. Journal of Electromyography and Kinesiology 2018; 42: 74–80. doi: 10.1016/j.jelekin.2018.05.010

38. Widhiada W, Parameswara MA, Santhiarsa IGNN, et al. Hybrid control system in bionic leg using MyoWare sensor. Journal of Southwest Jiaotong University 2021; 56(4): 104–116. doi: 10.35741/issn.0258-2724.56.4.11

39. Martins HVP, Setti JAP, Guimarães C, Campos DP. Development of a robotic orthosis for fingers flexion motion by surface myoelectric control: Open source prototype. Biomedical Signal Processing and Control 2023; 85: 105014. doi: 10.1016/j.bspc.2023.105014

40. Hassan S, Mwangi E, Kihato PK. IoT based monitoring system for epileptic patients. Heliyon 2022; 8(6): e09618. doi: 10.1016/j.heliyon.2022.e09618

41. Chaparro-Cárdenas SL, Castillo-Castañeda E, Lozano-Guzmán AA, et al. Characterization of muscle fatigue in the lower limb by sEMG and angular position using the WFD protocol. Biocybernetics and Biomedical Engineering 2021; 41(3): 933–943. doi: 10.1016/j.bbe.2021.06.003

42. Prahm C, Kayali F, Vujaklija I, et al. Increasing motivation, effort and performance through game-based rehabilitation for upper limb myoelectric prosthesis control. In: Proceedings of the 2017 International Conference on Virtual Rehabilitation (ICVR); 19–22 June 2017; Montreal, QC, Canada. pp. 1–6. doi: 10.1109/icvr.2017.8007517

43. de Boer E, Romkema S, Cutti AG, et al. Intermanual transfer effects in below-elbow myoelectric prosthesis users. Archives of Physical Medicine and Rehabilitation 2016; 97(11): 1924–1930. doi: 10.1016/j.apmr.2016.04.021

44. Sturma A, Hruby LA, Prahm C, et al. Rehabilitation of upper extremity nerve injuries using surface EMG biofeedback: Protocols for clinical application. Frontiers in Neuroscience 2018; 12. doi: 10.3389/fnins.2018.00906

45. Prahm C, Kayali F, Aszmann O. MyoBeatz: Using music and rhythm to improve prosthetic control in a mobile game for health. In: Proceedings of the 2019 IEEE 7th International Conference on Serious Games and Applications for Health (SeGAH); 5–7 August 2019; Kyoto, Japan. pp. 1–6. doi: 10.1109/segah.2019.8882432

46. Bouwsema H, van der Sluis CK, Bongers RM. Changes in performance over time while learning to use a myoelectric prosthesis. Journal of NeuroEngineering and Rehabilitation 2014; 11(1): 16. doi: 10.1186/1743-0003-11-16

47. Reeves J, Jones R, Liu A, et al. The between-day reliability of peroneus longus EMG during walking. Journal of Biomechanics 2019; 86: 243–246. doi: 10.1016/j.jbiomech.2019.01.037

48. Lynch C, Roumengous T, Mittal N, Peterson CL. Effects of stimulus waveform on transcranial magnetic stimulation metrics in proximal and distal arm muscles. Neurophysiologie Clinique 2022; 52(5): 366–374. doi: 10.1016/j.neucli.2022.07.002

49. Zaluski AJ, Campbell J, Hlasny M, et al. Activation of neuromuscular sub-regions of supraspinatus and infraspinatus during common rehabilitative exercises. Journal of Electromyography and Kinesiology 2021; 61: 102604. doi: 10.1016/j.jelekin.2021.102604

50. George LS, Hobbs SJ, Richards J, et al. The effect of cut-off frequency when high-pass filtering equine sEMG signals during locomotion. Journal of Electromyography and Kinesiology 2018; 43: 28–40. doi: 10.1016/j.jelekin.2018.09.001

51. Ismail R. Muscle power signal acquisition monitoring using surface EMG. Journal of Biomedical Research & Environmental Sciences 2022; 3(5): 663–667. doi: 10.37871/jbres1493

52. Putri FT, Caesarendra W, Królczyk G, et al. Human walking gait classification utilizing an artificial neural network for the ergonomics study of lower limb prosthetics. Prosthesis 2023; 5(3): 647–665. doi: 10.3390/prosthesis5030046

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