Regional Electromyography of the Infraspinatus and Supraspinatus Muscles During Standing Isometric External Rotation Exercises.

Background: Whole-muscle electromyography (EMG) data of the rotator cuff support external rotation (ER) strengthening exercises during shoulder rehabilitation. However, distinct neuroanatomic regions in the supraspinatus and infraspinatus exist. Differences in regional muscle activity occur during rehabilitation exercises, but little information is available for ER exertions.

Level of exoskeleton support influences shoulder elevation, external rotation and forearm pronation during simulated work tasks in females.

Despite growing literature, limited research details the influence of passive upper limb exoskeletons on upper limb kinematics. Two bolting tasks and a tracing task were completed at two heights (overhead and between waist and overhead height) for four exoskeleton conditions (no exoskeleton, and 3 levels of exoskeleton assistance) by female participants. Motion capture data, ratings of perceived exertion and discomfort, and task duration were recorded.

MuscleNET: mapping electromyography to kinematic and dynamic biomechanical variables by machine learning.

This paper proposes machine learning models for mapping surface electromyography (sEMG) signals to regression of joint angle, joint velocity, joint acceleration, joint torque, and activation torque.The regression models, collectively known as MuscleNET, take one of four forms: ANN (forward artificial neural network), RNN (recurrent neural network), CNN (convolutional neural network), and RCNN (recurrent convolutional neural network). Inspired by conventional biomechanical muscle models, delayed kinematic signals were used along with sEMG signals as the machine learning model's input; specifically, the CNN and RCNN were modeled with novel configurations for these input conditions.

Wrapping technique and wrapping height interact to modify physical exposures during manual pallet wrapping.

Occupational tasks often involve musculoskeletal demands that contribute to injury risk. In pallet wrapping tasks, 36% of workplace claims involve over exertion and repetitive exposures (Workplace Safety and Prevention Services, 2012). Tools that modify how the wrap is handled by workers have been introduced to help mitigate over exertion and extreme postures wherever possible.

Upper extremity and trunk body segment parameters are affected by BMI and sex.

Although body mass index (BMI) relates to body segment parameters (BSPs), unknowns persist over whether: 1) BSPs relate to BMI group classifications, 2) sex influences BMI/BSP relationships, and 3) simple anthropometric measures sufficiently predict BSPs. Dual energy X-ray absorptiometry (DXA) scans and anthropometric measures were obtained from 76 participants (33M, 43F) of varying body composition. Trunk, neck and head (TNH), arm, forearm and hand masses were obtained from DXA scans and center of mass locations (COM) estimated from geometric models.

Ratings of perceived fatigue predict fatigue induced declines in muscle strength during tasks with different distributions of effort and recovery.

The purpose of this study was to quantify the relationship between ratings of perceived fatigue (RPF), using a modified Borg CR-10 scale, and muscle fatigue accumulation, as defined by maximal voluntary contraction strength (MVC) declines, during two complex MVC-relative tasks (conditions) that cause muscle fatigue and allow recovery. Nine female participants completed the fatiguing tasks, composed of a series of submaximal, isometric efforts (task plateaus) requiring isometric flexion at the distal interphalangeal joint of the thumb. Significant partial correlations between RPF and MVC, while controlling for task plateau intensity (%MVC), were found in 6/9 participants.

Arm posture influences on regional supraspinatus and infraspinatus activation in isometric arm elevation efforts.

This study aimed to evaluate the effect of arm posture on activation of the anterior and posterior regions of supraspinatus and the superior and middle regions of infraspinatus during resisted isometric arm elevations. Thirty-one healthy participants performed 18 isometric resistance exertions against a force cube in three elevation planes (flexion, scaption, abduction) and three elevation angles (30°, 90°, 150°) in maximal and sub-maximal resistance conditions. EMG data were obtained using four pairs of fine wire electrodes.

Algorithmically detectable directional changes in upper extremity motion indicate substantial myoelectric shoulder muscle fatigue during a repetitive manual task.

Repetitive workplace tasks are associated with fatigue-induced changes to shoulder muscular strategies, potentially altering kinematics and elevating susceptibility to tissue overexposures. Accessible and reliable methods to detect shoulder muscle fatigue in the workplace are therefore valuable. Detectable changes in joint motion may provide a plausible fatigue identification method.

Application of a symbolic motion structure representation algorithm to identify upper extremity kinematic changes during a repetitive task.

Efficient and holistic identification of fatigue-induced movement strategies can be limited by large between-subject variability in descriptors of joint angle data. One promising alternative to traditional, or computationally intensive methods is the symbolic motion structure representation algorithm (SMSR), which identifies the basic spatial-temporal structure of joint angle data using string descriptors of temporal joint angle trajectories. This study attempted to use the SMSR to identify changes in upper extremity time series joint angle data during a repetitive goal directed task causing muscle fatigue.