No abstract

This study aimed to quantify the association of four anthropometric parameters of the human arm, namely, the arm circumference (CA), arm length (LA), skinfold thickness (ST) and inter-sensor distance (ISD), with amplitude (RMS) and crosstalk (CT) of mechanomyography (MMG) signals. Twenty-five young, healthy, male participants were recruited to perform forearm flexion, pronation and supination torque tasks. Three accelerometers were employed to record the MMG signals from the biceps brachii (BB), brachialis (BRA) and brachioradialis (BRD) at 80% maximal voluntary contraction (MVC). Signal RMS was used to quantify the amplitude of the MMG signals from a muscle, and cross-correlation coefficients were used to quantify the magnitude of the CT among muscle pairs (BB & BRA, BRA & BRD, and BB & BRD). For all investigated muscles and pairs, RMS and CT showed negligible to low negative correlations with CA, LA and ISD (r = −0.0001-−0.4611), and negligible to moderate positive correlations with ST (r = 0.004-0.511). However, almost all of these correlations were statistically insignificant (p > 0.05). These findings suggest that RMS and CT values for the elbow flexor muscles recorded and quantified using accelerometers appear invariant to anthropometric parameters. conclusion CA, LA and ISD were found to exhibit negligible to low negative correlations with RMS and CT, whereas ST exhibited negligible to moderate positive correlations with RMS and CT. These findings suggest that the MMG signal parameters and CT acquired using accelerometers appear invariant to the four anthropometric parameters but might depend on other physiological and subject-based factors. The findings obtained in this study might aid our understanding of CT mechanisms in MMG signals, which could be useful in future research for devising techniques for the reduction or removal of CT in MMG.

This study analysed crosstalk in mechanomyographic (MMG) signals from elbow flexors during isometric muscle actions from 20% to 100% maximum voluntary isometric contraction (MVIC). Twenty-five young, healthy, male participants performed isometric elbow flexion, forearm pronation and supination tasks at an elbow joint angle of 90°. The MMG signals from the biceps brachii (BB), brachialis (BRA) and brachioradialis (BRD) muscles were recorded using accelerometers. Cross-correlation coefficient was used to quantify the crosstalk in MMG signals, recorded in a direction transverse to muscle fibre axis, among the muscle pairs (P1: BB and BRA, P2: BRA and BRD and P3: BB and BRD). In addition, the MMG RMS and MPF were quantified. Mean normalized RMS and mean MPF exhibited increasing (r > 0.900) and decreasing (r < -0.900) trends, respectively, with increases in the effort levels in all three tasks. The magnitude of crosstalk ranged from 0.915 to 21.565% in all muscle pairs. Crosstalk was found to exhibit high positive correlations with submaximal to maximal flexion [P1 (r = 0.970), P2 (r = 0.951), and P3 (r = 0.824)], pronation [P1 (r = 0.811), P2 ( r = 0.763), and P3 (r = 0.901)] and supination [P1 (r = 0.898), P2 (r = 0.838), and P3 (r = 0.852)] torque levels (eight out of nine p-values were < 0.05). Regardless of the high positive correlation between crosstalk and level of effort, the crosstalk remained at a low range (0.915% to 21.565%) with increases in the torque levels.

No abstract

Robots are the best replacement of man power in every field of life because they can work with a better efficiency under hard environments. Recently, plenty of work has been carried out on mimicking the human hand motion. However, most of the designers emphasized on designing new actuating system with less degree of freedom or reduce number of actuators along with enhanced performance. Bird claw motion is a natural example with simpler design and has many advantages over human hand such as less number of joints, easy structure and better grip. In this manuscript we propose a robot model which is based on a pigeon’s claw and a hardware structure is implemented to verify that the proposed model contain almost half joints than a human hand and hence offers much lower cost along with easier management. Furthermore, smaller size of the palm also facilitates in handling small items. We have presented software and hardware model of a pigeon’s claw in support of the argument.