Crosstalk in surface electromyography of the proximal forearm during gripping tasks

Mogk, Jeremy P.M.; Keir, Peter J.

doi:10.1016/s1050-6411(02)00071-8

Cited by 144 publications

(109 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It means that the multichannel sensor ring may be a kind of redundant sensor. Mogk and Keir (2003) found that the EMG signal amplitudes between adjacent electrode pairs (3 cm apart) share 40% common signal (or crosstalk), and the common signal is reduced to about 10% at 6 cm spacing and 2.5% at 9 cm, while only 2% common signal or crosstalk is found between flexor and extensor electrode pairs. On the other hand, we could place a similar sensor band on the other forearm to provide even more input possibilities (Saponas et al, 2008).…”

Section: Emg Sensor Arm Bandsmentioning

confidence: 95%

“…(1) Lower forearm positions, i.e., around the wrist Tang, Liu, Lv, & Sun, 2012;You, Rhee, & Shin, 2010, 2011; (2) Upper forearm positions, i.e., below the elbow (Andews, Morin, & McLean, 2009;Benko et al, 2009;Du, Lin, Shyu, & Chen, 2010;Du, Shyu, & Hu, 2006;Khushaba & Kodagoda, 2012;Khushaba et al, 2013;Mogk & Keir, 2003;Saponas et al, 2008;Shyu, Chen, Tatn, & Hu, 2002;Smith, Huberdeau, Tenore, & Thakor, 2009;Smith et al, 2008;Tenore et al, 2007Tenore et al, , 2009). …”

Section: Emg Sensor Arm Bandsmentioning

confidence: 99%

“…one pair across the ulnar border (Du et al, 2006(Du et al, , 2010Mogk & Keir, 2003;Shyu et al, 2002;Smith et al, 2008Smith et al, , 2009. In this scheme, however, there is no guarantee that electrodes are placed over the same muscles in all users.…”

Section: Emg Sensor Arm Bandsmentioning

confidence: 99%

“…The number of EMG channels placed in a narrow uniform band/ring, which is proposed in the literature, is seven sensors (Du et al, 2006(Du et al, , 2010Mogk & Keir, 2003;Shyu et al, 2002) and eight sensors (Andrews et al, 2009;Khushaba & Kodagoda, 2012;Khushaba et al, 2013;Saponas et al, 2008;Smith et al, 2009). When multiple sets of sensors were placed around the forearm, reasonable EMG signals with useful information could be acquired from the armband even the sensors are only approximately placed (Saponas et al, 2008).…”

Section: Emg Sensor Arm Bandsmentioning

confidence: 99%

“…The landmark sensor armband is placed below the elbow in several conditions, e.g. approximately one third of the distance from the proximal end of a line from medial epicondyle to the distal head of the radius (Andrews et al, 2009;Mogk & Keir, 2003), or approximately two inches or five centimeters below the elbow (Du et al, 2006(Du et al, , 2010Shyu et al, 2002;Smith et al, 2009). For the landmark of the starting electrode pair, there are several ways, such as placing just superior to the ulna (Andrews et al, 2009) or over the flexor carpi radialis (Mogk & Keir, 2003).…”

Section: Emg Sensor Arm Bandsmentioning

confidence: 99%

See 4 more Smart Citations

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface

Phinyomark

Quaine

Laurillau

Advances in Medical Technologies and Clinical Practice

View full text Add to dashboard Cite

Muscle-computer interfaces (MCIs) based on surface electromyography (EMG) pattern recognition have been developed based on two consecutive components: feature extraction and classification algorithms. Many features and classifiers are proposed and evaluated, which yield the high classification accuracy and the high number of discriminated motions under a single-session experimental condition. However, there are many limitations to use MCIs in the real-world contexts, such as the robustness over time, noise, or low-level EMG activities. Although the selection of the suitable robust features can solve such problems, EMG pattern recognition has to design and train for a particular individual user to reach high accuracy. Due to different body compositions across users, a feasibility to use anthropometric variables to calibrate EMG recognition system automatically/semi-automatically is proposed. This chapter presents the relationships between robust features extracted from actions associated with surface EMG signals and twelve related anthropometric variables. The strong and significant associations presented in this chapter could benefit a further design of the MCIs based on EMG pattern recognition.

show abstract

Section: Emg Sensor Arm Bandsmentioning

confidence: 95%

Section: Emg Sensor Arm Bandsmentioning

confidence: 99%

Section: Emg Sensor Arm Bandsmentioning

confidence: 99%

Section: Emg Sensor Arm Bandsmentioning

confidence: 99%

Section: Emg Sensor Arm Bandsmentioning

confidence: 99%

See 3 more Smart Citations

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface

Phinyomark

Quaine

Laurillau

Advances in Medical Technologies and Clinical Practice

View full text Add to dashboard Cite

show abstract

Pathophysiology of sex difference in refractoriness in lateral epicondylitis: Biomechanical study of wrist torque

Ikeda,

Yoshii,

Kohyama

et al. 2023

Journal Orthopaedic Research

View full text Add to dashboard Cite

Eccentric contractions of the wrist extensors worsen lateral epicondylitis (LE), whose pathophysiology may involve sex differences in wrist torque. This study aimed to investigate sex differences in wrist torque in patients with LE. The wrist extension and flexion torques of 22 patients with LE (11 males and 11 females) were measured. Maximum muscle output over time was measured for 20 s, initial torque was defined as muscle strength, and the degree of eccentric contraction was quantified and defined as the eccentric contraction index (ECI). The affected/unaffected side ratio of the wrist extensor, extensor/flexor ratio of muscle strength, and affected/unaffected side difference of ECI between sexes were statistically analyzed. Furthermore, correlations between wrist extensor torque, ECI, and Visual Analog Scale of pain during the examination were evaluated. Females were found to display lower affected/unaffected side ratios of the wrist extensor and wrist extension/flexion ratios for the affected side, compared with males; however, no differences were found in the wrist extension/flexion ratios for the unaffected side in both sexes. Additionally, females presented with larger differences between the affected and unaffected sides in the ECI. Based on correlations between wrist torques, ECI, and pain, females tended to suppress muscle output to prevent pain from eccentric contraction of wrist extensors more than males, which would induce an imbalance in muscle strength of the wrist extensors and flexors. This imbalance may result in chronic eccentric contraction of the wrist extensors with gripping, exacerbating LE.

show abstract

Analysis of crosstalk in the mechanomyographic signals generated by forearm muscles during different wrist postures

et al. 2015

View full text Add to dashboard Cite

show abstract

Crosstalk in surface electromyography of the proximal forearm during gripping tasks

Cited by 144 publications

References 22 publications

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface

Pathophysiology of sex difference in refractoriness in lateral epicondylitis: Biomechanical study of wrist torque

Analysis of crosstalk in the mechanomyographic signals generated by forearm muscles during different wrist postures

Contact Info

Product

Resources

About