ArticleSensors (Basel, Switzerland)2024
A Surface Electromyography (sEMG) System Applied for Grip Force Monitoring.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Innovative stimulated muscle contraction signals based digital muscle marker: a reliable tool for assessing muscle in persons with stroke.Journal of neuroengineering and rehabilitation · 2025Article
- Wearable sensors and machine learning fusion-based fall risk prediction in covert cerebral small vessel disease.Frontiers in neuroscience · 2025Article
- Replay-Based Incremental Learning Framework for Gesture Recognition Overcoming the Time-Varying Characteristics of sEMG Signals.Sensors (Basel, Switzerland) · 2024Article
- Research Advances in Electrospun Nanofiber Membranes for Non-Invasive Medical Applications.Micromachines · 2024Review
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Authors and funding
14 authors.
Funding
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Abstract
Muscles play an indispensable role in human life. Surface electromyography (sEMG), as a non-invasive method, is crucial for monitoring muscle status. It is characterized by its real-time, portable nature and is extensively utilized in sports and rehabilitation sciences. This study proposed a wireless acquisition system based on multi-channel sEMG for objective monitoring of grip force. The system consists of an sEMG acquisition module containing four-channel discrete terminals and a host computer receiver module, using Bluetooth wireless transmission. The system is portable, wearable, low-cost, and easy to operate. Leveraging the system, an experiment for grip force prediction was designed, employing the bald eagle search (BES) algorithm to enhance the Random Forest (RF) algorithm. This approach established a grip force prediction model based on dual-channel sEMG signals. As tested, the performance of acquisition terminal proceeded as follows: the gain was up to 1125 times, and the common mode rejection ratio (CMRR) remained high in the sEMG signal band range (96.94 dB (100 Hz), 84.12 dB (500 Hz)), while the performance of the grip force prediction algorithm had an R
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