Surface EMG Signal Acquisition Analysis and Classification for the Operation of a Prosthetic Limb

Samarawickrama, Kasun G.; Ranasinghe, Sadun; Wickramasinghe, Yasoja; Mallehevidana, Wageesha; Marasinghe, Vidarshi; Wijesinghe, Kanchana

doi:10.17706/ijbbb.2018.8.1.32-41

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…4a, b, respectively. Notably, the impedance of the sealed sample (over 10 MΩ at and between 50 and 150 Hz, which is the most dominant range of EMG 37 , and overload at <50 Hz) did not decrease even when the wiring was stretched at 150% strain, while the other three samples exhibited low impedance (<10 kΩ in the range from 50 to 150 Hz) (Fig. 4c).…”

Section: Mechanical and Electrical Properties Of The Elastic Kirigamimentioning

confidence: 97%

Elastic kirigami patch for electromyographic analysis of the palm muscle during baseball pitching

et al. 2019

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Surface electromyography (sEMG) is widely used to analyze human movements, including athletic performance. For baseball pitchers, a very precise movement is required to pitch the ball into the strike zone. The palm muscles appear to play a key role in this movement, and a real-time recording of sEMG from the palm muscle is useful in the analysis of motion during baseball pitching. However, the currently available devices with rigid and bulky electrodes (including connective wires) impede natural movements of the wearer and recording of sEMG from the palm muscles during vigorous action. Here, we describe a skin-contact patch consisting of kirigami-based stretchable wirings and conductive polymer nanosheet-based ultraconformable bioelectrodes, which address the challenge of mechanical mismatch between human skin and electrical devices. The key strategy is a kirigami-inspired wiring design and a mechanical gradient structure from nanosheet-based flexible bioelectrodes to a bulk wearable device. This approach would buffer the mechanical stress applied to the skin-contact bioelectrodes during an arm swing movement. With this patch, we precisely measure sEMG at the abductor pollicis brevis muscle (APBM) in a baseball player during ball pitching. We observe differences in the activity of the APBM between different types of pitches-fastball and curveball. This sEMG measurement system will enable the analysis of motion in unexplored muscle areas, such as on the palm and the sole, leading to a deeper understanding of muscular activity during performance in a wide range of sports and other movements.

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Section: Mechanical and Electrical Properties Of The Elastic Kirigamimentioning

confidence: 97%

Elastic kirigami patch for electromyographic analysis of the palm muscle during baseball pitching

et al. 2019

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“…Clinical settings of EMG use a needle that is inserted into the muscle. For measuring ECG on the go and better ease of use, surface EMG is chosen [30], [31]. Muscle activity is measured in voltage and represents the amount of force exerted by the muscle in real-time.…”

Section: Emg Unitmentioning

confidence: 99%

MyWear: A Novel Smart Garment for Automatic Continuous Vital Monitoring

Sethuraman

Kompally

Mohanty

et al. 2021

IEEE Trans. Consumer Electron.

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Wearables are getting large acceptance in the continuous monitoring of health status and physiological data. Medical devices and their connectivity through Internet along with the electronics health record (EHR) and AI analytics making smart healthcare possible. Internet-of-Medical-Things (IoMT)-end devices like wearables and implantables are key for smart healthcare. Smart garment is a specific wearable which can be used for smart healthcare. This paper presents the design and development of a smart garment called MyWear that continuously monitors and collects physiological data. It can analyze muscle activity, stress levels, and heart rate variations and send all the data to the cloud. Through abnormal variations in vitals, it can also predict the risk of heart failure and with the in-built alert system, it can notify the associated medical officials if necessary. We also propose a deep neural network model that classifies heartbeat data into abnormalities with 96.9% accuracy and 97.3% precision.

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“…EMG has become a reliable and cost-effective method for signal acquisition in limbs. Placing electrodes inappropriate position of skin is an important criterion to get the optimum EMG signals [8], [9].…”

Section: Electromyography (Emg)mentioning

confidence: 99%

Application of digital signal processing and machine learning for Electromyography: A review

Omar¹

2021

AJMedTech

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This paper reviewed the Application of Digital Signal Processing (DPS) and Machine Learning (ML) for Electromyography (EMG) by previous studies. There is a need of the DSP and ML application into the EMG study to classify the signal in order to minimize the EMG noise of signal and the EMG signal characteristic. The common techniques analysis of signal processing is disccussed and compared to identify the best techniques used in order to process from raw data of EMG signal info EMG signal analysis, then some types of machine learning is discussed to identify which types of machine learning have gave the best performance of EMG signal identification and signal characteristic with the highest percentage of the accuracy and efficiency. Digital signal processing and the technique of signal analysis and machine learning for classification method in order to provide the best method and classification for EMG signal.

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Surface EMG Signal Acquisition Analysis and Classification for the Operation of a Prosthetic Limb

Cited by 8 publications

References 9 publications

Elastic kirigami patch for electromyographic analysis of the palm muscle during baseball pitching

Elastic kirigami patch for electromyographic analysis of the palm muscle during baseball pitching

MyWear: A Novel Smart Garment for Automatic Continuous Vital Monitoring

Application of digital signal processing and machine learning for Electromyography: A review

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