Investigating the performance of an amplitude-independent algorithm for detecting the hand muscle activity of stroke survivors

References

• Chu C, Patterson R. Soft robotic devices for hand rehabilitation and assistance: a narrative review. J Neuroeng Rehab. 2018;15(1):1–14.
   PubMed Web of Science ®Google Scholar
• Number of stroke patients [Internet]. [cited 2019 Feb 19]. Available from http://www.strokecenter.org/patients/about-stroke/stroke-statistics/.
   Google Scholar
• Cirstea M, Ptito A, Levin M. Arm reaching improvements with short-term practice depend on the severity of the motor deficit in stroke. Exp Brain Res. 2003;152(4):476–488.
   Google Scholar
• Yap HK, Lim JH, Nasrallah F, et al. Design and preliminary feasibility study of a soft robotic glove for hand function assistance in stroke survivors. Front Neurosci. 2017;11:547.
   PubMed Web of Science ®Google Scholar
• Bernocchi P, Mulè C, Vanoglio F, et al. Home-based hand rehabilitation with a robotic glove in hemiplegic patients after stroke: a pilot feasibility study. Top Stroke Rehabil. 2018;25(2):114–119.
   PubMed Web of Science ®Google Scholar
• Prange-Lasonder G, Radder B, Kottink A, et al. Applying a soft-robotic glove as assistive device and training tool with games to support hand function after stroke: preliminary results on feasibility and potential clinical impact. IEEE Int Conf Rehabil Robot. 2017;2017:1401–1406.
   PubMedGoogle Scholar
• Meeker C, Park S, Bishop L, et al. EMG pattern classification to control a hand orthosis for functional grasp assistance after stroke. Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR); London, UK; 2017.
   Google Scholar
• Lobo-Prat J, Kooren P, Stienen A, et al. Non-invasive control interfaces for intention detection in active movement-assistive devices. J Neuroeng Rehab. 2014;11(1):168.
   PubMed Web of Science ®Google Scholar
• Rosen J, Brand M, Fuchs M, et al. A myosignal-based powered exoskeleton system. IEEE Trans Syst Man Cybern A. 2001;31(3):210–222.
   Web of Science ®Google Scholar
• Lu Z, Tong K, Shin H, et al. Advanced myoelectric control for robotic hand-assisted training: Outcome from a stroke patient. Front Neurol. 2017;8:1–5.
   PubMed Web of Science ®Google Scholar
• Tong K, Ho S, Pang P, et al. An intention driven hand functions task training robotic system. Proceedings of the 32nd Annual International Conference of the IEEE EMBS; 2010 Aug 31–Sep 4; Buenos Aires, Argentina.
   Google Scholar
• Ho N, Tong K, Hu X, et al. An EMG-driven exoskeleton hand robotic training device on chronic stroke subjects. Proceedings of the IEEE International Conference on Rehabilitation Robotics; June 29–July 1; Zurich, ETH Zurich Science City, Switzerland; 2011.
   Google Scholar
• Kline T, Kamper D, Schmit B. Control system for pneumatically controlled glove to assist in grasp activities. Proceedings of the Proceedings of the IEEE 9th International Conference on Rehabilitation Robotics; 2005 Jun 28–Jul 1; Chicago, IL, USA.
   Google Scholar
• Ochoa J, Listenberger M, Kampur D, et al. Use of an electromyographically driven hand orthosis for training after stroke. Proceedings of the IEEE International Conference on Rehabilitation Robotics Rehab Week Zurich; 2011 Jun 29–Jul 1; ETH Zurich Science City, Switzerland.
   Google Scholar
• Thielbar KO, Triandafilou KM, Fischer HC, et al. Benefits of using a voice and EMG-driven actuated glove to support occupational therapy for stroke survivors. IEEE Trans Neural Syst Rehabil Eng. 2017;25(3):297–306.
   PubMed Web of Science ®Google Scholar
• Zecca M, Micera S, Carrozza M, et al. Control of multifunctional prosthetic hands by processing the electromyographic signal. Crit Rev Biomed Eng. 2002;30(4–6):459–485.
   PubMedGoogle Scholar
• Marchal-Crespo L, Reinkensmeyer D. Review of control strategies for robotic movement training after neurologic injury. J Neuroeng Rehabil. 2009;6(1):20.
   PubMed Web of Science ®Google Scholar
• Huang J, Huo W, Xu W, et al. Control of upper-limb power-assist exoskeleton using a human-robot interface based on motion intention recognition. IEEE Trans Automat Sci Eng. 2015;12(4):1257–1270.
   Web of Science ®Google Scholar
• Zhang X, Zhou P. Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes. J Electromyogr Kinesiol. 2012;22(6):901–907.
   PubMed Web of Science ®Google Scholar
• D’Anna C, Varrecchia T, Schmid M, et al. Using the frequency signature to detect muscular activity in weak and noisy myoelectric signals. Biomed Signal Process Control. 2019;52:69–76.
   Web of Science ®Google Scholar
• Xu Q, Quan Y, Yang L, et al. An adaptive algorithm for the determination of the onset and offset of muscle contraction by EMG signal processing. IEEE Trans Neural Syst Rehabil Eng. 2013;21(1):65–73.
   PubMed Web of Science ®Google Scholar
• Liu J, Liu Q. Use of the integrated profile for voluntary muscle activity detection using EMG signals with spurious background spikes: a study with incomplete spinal cord injury. Biomed Signal Process Control. 2016;24:19–24.
   Web of Science ®Google Scholar
• Yang D, Zhang H, Gu Y, et al. Accurate EMG onset detection in pathological, weak and noisy myoelectric signals. Biomed Signal Process Control. 2017;33:306–315.
   Web of Science ®Google Scholar
• Liu J, Ying D, Rymer W. EMG burst presence probability: a joint time-frequency representation of muscle activity and its application to onset detection. J Biomech. 2015;48(6):1193–1197.
   PubMed Web of Science ®Google Scholar
• Merlo A, Farina D, Merletti R. A fast and reliable technique for muscle activity detection from surface EMG signals. IEEE Trans Biomed Eng. 2003;50(3):319–323.
   Web of Science ®Google Scholar
• Kaur A, Agarwal R, Kumar A. Adaptive threshold method for peak detection of surface electromyography signal from around shoulder muscles. J Appl Stat. 2018;45(4):714–726.
   Web of Science ®Google Scholar
• Li X, Zhou P, Aruin A. Teager-Kaiser energy operation of surface EMG improves muscle activity onset detection. Ann Biomed Eng. 2007;35(9):1532–1538.
   PubMed Web of Science ®Google Scholar
• Severini G, Conforto S, Schmid M, et al. Novel formulation of a double threshold algorithm for the estimation of muscle activation intervals designed for variable SNR environments. J Electromyogr Kinesiol. 2012;22(6):878–885.
   PubMed Web of Science ®Google Scholar
• Vaisman L, Zariffa J, Popovic M. Application of singular spectrum-based change-point analysis to EMG-onset detection. J Electromyogr Kinesiol. 2010;20(4):750–760.
   PubMed Web of Science ®Google Scholar
• Vannozzi G, Conforto S, D’Alessio T. T. Automatic detection of surface EMG activation timing using a wavelet transform based method. J Electromyogr Kinesiol. 2010;20(4):767–772.
   PubMed Web of Science ®Google Scholar
• Liu J, Ying D, Rymer W. Robust muscle activity onset detection using an unsupervised electromyogram learning framework. PLoS One. 2015;10(6):1–15.
   Web of Science ®Google Scholar
• Bonato P, D'Alessio T, Knaflitz M. A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait. IEEE Trans Biomed Eng. 1998;45(3):287–299.
   PubMed Web of Science ®Google Scholar
• McCool P, Chatlani N, Petropoulakis L, et al. Lower arm electromyography (EMG) activity detection using local binary patterns. IEEE Trans Neural Syst Rehabil Eng. 2014;22(5):1003–1012.
   PubMed Web of Science ®Google Scholar
• Drapala J, Brzostowski K, Szpala A, et al. Two stage EMG onset detection method. Arch Control Sci. 2012;22(4):427–440.
   Google Scholar
• Solnik S, DeVita P, Rider P, et al. Teager-Kaiser operator improves the accuracy of EMG onset detection independent of signal-to-noise ratio. Acta Bioeng Biomech. 2008;10(2):65–68.
   PubMed Web of Science ®Google Scholar
• Staude G, Flachenecker C, Daumer M, et al. Onset detection in surface electromyographic signals: a systematic comparison of methods. EURASIP J Appl Signal Processing. 2001;2001(2):67–81.
   Google Scholar
• Reis M, Almeida C, Rocha R. On the performance of surface electromyography-based onset detection methods with real data in assistive technologies: Comparative analysis and enhancements via sensor fusion. Multimed Tools Appl. 2017;77(9):11491–11520.
   Web of Science ®Google Scholar
• Micera S, Vannozzi G, Sabatini A, et al. Improving detection of muscle activation. IEEE Eng Med Biol Mag. 2001;20(6):38–46.
   PubMedGoogle Scholar
• Tenan MS, Tweedell AJ, Haynes CA. Analysis of statistical and standard algorithms for detecting muscle onset with surface electromyography. PLoS One. 2017;12(5):e0177312–15.
   PubMed Web of Science ®Google Scholar
• Hameed H, Hassan W, Shafie S. Soft robotic glove system controlled with amplitude independent muscle activity detection algorithm by using single sEMG channel. Proceedings of the IEEE 5th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA 2018); 2018 Nov 28–30; Songkhla, Thailand. p. 1–6.
   Google Scholar
• Hameed H, Hassan W, Shafie S, et al. Amplitude independent versus amplitude dependent muscle activity detection algorithms: a comparative study. Indones J Electrical Eng Comput Sci. 2019;17(2):583–593.
   Google Scholar
• Hameed H, Hassan W, Shafie S, et al. An amplitude independent muscle activity detection algorithm based on adaptive zero crossing technique and mean instantaneous frequency of the sEMG signal. Proceedings of the IEEE Regional Symposium on Micro and Nanoelectronics (RSM); 2017 Aug 23–25; Batu Ferringhi, Malaysia.
   Google Scholar
• Hameed H, Hassan W, Shafie S, et al. Identifying the best forearm muscle to control a soft robotic glove system by using a single sEMG channel. Proceedings of the IEEE 3rd International Conference on Engineering Innovations in Healthcare (ASET2020); 2020 Feb 04–06; Dubai.
   Google Scholar
• Heywood S, Pua Y, McClelland J, et al. Low-cost electromyography Validation against a commercial system using both manual and automated activation timing thresholds. J Electromyogr Kinesiol. 2018;42:74–80.
   PubMed Web of Science ®Google Scholar