A review on the recent developments in design and integration of electromyography textile electrodes for biosignal monitoring

Etana, Bulcha Belay; Malengier, Benny; Timothy, Kwa; Wojciech, Sitek; Krishnamoorthy, Janarthanan; Van Langenhove, Lieva

doi:10.1177/15280837231175062

Cited by 4 publications

(1 citation statement)

References 127 publications

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“…The main disadvantages of the gelled electrode are its fixed form (rigid circular form), drying of its gel on prolonged usage, lower wearability while performing sports or outdoor activities, and its disposal after a single use. In recent decades, dry electrodes have been proposed to replace wet electrodes on the grounds of their wearability (embedded into fabric and other accessories), reasonably good performance, and sustainability features (such as washability and reusability) [ 8 , 9 , 10 , 11 , 12 ]. Unlike the conventional Ag/AgCl gelled electrodes, the wearable dry electrodes can be designed in different shapes (circular, rectangular patch), and require minimal power for functioning.…”

Section: Introductionmentioning

confidence: 99%

Integrating Wearable Textiles Sensors and IoT for Continuous sEMG Monitoring

Etana,

Malengier,

Krishnamoorthy

et al. 2024

Sensors

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Surface electromyography is a technique used to measure the electrical activity of muscles. sEMG can be used to assess muscle function in various settings, including clinical, academic/industrial research, and sports medicine. The aim of this study is to develop a wearable textile sensor for continuous sEMG monitoring. Here, we have developed an integrated biomedical monitoring system that records sEMG signals through a textile electrode embroidered within a smart sleeve bandage for telemetric assessment of muscle activities and fatigue. We have taken an “Internet of Things”-based approach to acquire the sEMG, using a Myoware sensor and transmit the signal wirelessly through a WiFi-enabled microcontroller unit (NodeMCU; ESP8266). Using a wireless router as an access point, the data transmitted from ESP8266 was received and routed to the webserver-cum-database (Xampp local server) installed on a mobile phone or PC for processing and visualization. The textile electrode integrated with IoT enabled us to measure sEMG, whose quality is similar to that of conventional methods. To verify the performance of our developed prototype, we compared the sEMG signal recorded from the biceps, triceps, and tibialis muscles, using both the smart textile electrode and the gelled electrode. The root mean square and average rectified values of the sEMG measured using our prototype for the three muscle types were within the range of 1.001 ± 0.091 mV to 1.025 ± 0.060 mV and 0.291 ± 0.00 mV to 0.65 ± 0.09 mV, respectively. Further, we also performed the principal component analysis for a total of 18 features (15 time domain and 3 frequency domain) for the same muscle position signals. On the basis on the hierarchical clustering analysis of the PCA’s score, as well as the one-way MANOVA of the 18 features, we conclude that the differences observed in the data for the different muscle types as well as the electrode types are statistically insignificant.

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