This systematic review aims to categorically analyses the literature on the assessment of biceps brachii (BB) muscle activity through mechanomyography (MMG). The application of our search criteria to five different databases identified 319 studies. A critical review of the 48 finally selected records, revealed the diversity of protocols and parameters that are employed in MMG-based assessments of BB muscle activity. The observations were categorized into the following: muscle torque, fatigue, strength and physiology. The available information on the muscle contraction protocol, sensor(s), MMG signal parameters and obtained results were then tabulated based on these categories for further analysis. The review affirms that - 1) MMG is suitable for skeletal muscle activity assessment and can be employed potentially for further investigation of the BB muscle activity and condition (e.g., force, torque, fatigue, and contractile properties), 2) a majority of the records focused on static contractions of the BB, and the analysis of dynamic muscle contractions using MMG is thus a research gap, and 3) very few studies have focused on the analysis of BB muscle activity under externally stimulated contractions. Taken together, the findings of this review on BB activity assessment using MMG affirm the potential of MMG as an alternative tool.

Cardiovascular diseases are some of the underlying reasons contributing to the relentless rise in mortality rates across the globe. In this regard, there is a genuine need to integrate advanced technologies into the medical realm to detect such diseases accurately. Moreover, numerous academic studies have been published using AI-based methodologies because of their enhanced accuracy in detecting heart conditions. This research extensively delineates the different heart conditions, e.g., coronary artery disease, arrhythmia, atherosclerosis, mitral valve prolapse/mitral regurgitation, and myocardial infarction, and their underlying reasons and symptoms and subsequently introduces AI-based detection methodologies for precisely classifying such diseases. The review shows that the incorporation of artificial intelligence in detecting heart diseases exhibits enhanced accuracies along with a plethora of other benefits, like improved diagnostic accuracy, early detection and prevention, reduction in diagnostic errors, faster diagnosis, personalized treatment schedules, optimized monitoring and predictive analysis, improved efficiency, and scalability. Furthermore, the review also indicates the conspicuous disparities between the results generated by previous algorithms and the latest ones, paving the way for medical researchers to ascertain the accuracy of these results through comparative analysis with the practical conditions of patients. In conclusion, AI in heart disease detection holds paramount significance and transformative potential to greatly enhance patient outcomes, mitigate healthcare expenditure, and amplify the speed of diagnosis.

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.

Wind is an important renewable energy source. The majority of wind farms in Pakistan are installed in Jhimpir, Sindh Wind Corridor. At this location, downstream turbines encounter upstream turbines’, wake, decreasing power output. To maximize the power output, there is a need to minimize these wakes. In this research, a method is proposed to maximize the power output using a Genetic Algorithm (GA). Hub heights and inter-turbine spacing are considered variables in this method. Two wind farms located at Jhimpir, Sindh, namely, Second and Third Three Gorges Wind Farms (TGWFs), have been analyzed. Three different cases are considered to maximize the power output. In Case 1, thesame hub heights and inter-turbine spacing without wake effects are considered. In Case 2, the same hub heights and inter-turbine spacing with wake effects are considered. In Case 3, variable hub heights and inter-turbine spacing with wake effects are considered. The results revealed that TGWFs, with variable hub heights and inter-turbine spacing, produce more power output. It is also revealed that the increase in power output, in the case of two different hub heights, is greater in comparison to three different hub heights. Eventually, the proposed method may help in the layout optimization of a wind farm.

This study analyzed the 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 the isometric elbow flexion, forearm pronation, and supination tasks at an elbow joint angle of 90 deg. The MMG signals from the biceps brachii (BB), brachialis (BRA), and brachioradialis (BRD) muscles were recorded using accelerometers. The cross-correlation coefficient was used to quantify the crosstalk in MMG signals, recorded in a direction transverse to muscle fiber 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. The 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 three muscle pairs. The 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 (0.915–21.565%) with increases in the torque levels.