The objective of the present study was to determine the instantaneous moment arms of 18 major muscle sub-regions crossing the glenohumeral joint during coronal-plane abduction and sagittal-plane flexion. Muscle moment-arm data for sub-regions of the shoulder musculature during humeral elevation are currently not available. The tendon-excursion method was used to measure instantaneous muscle moment arms in eight entire upper-extremity cadaver specimens. Significant differences in moment arms were reported across sub-regions of the deltoid, pectoralis major, latissimus dorsi, subscapularis, infraspinatus and supraspinatus ( P < 0.01). The most effective abductors were the middle and anterior deltoid, whereas the most effective adductors were the teres major, middle and inferior latissimus dorsi (lumbar vertebrae and iliac crest fibers, respectively), and middle and inferior pectoralis major (sternal and lower-costal fibers, respectively). In flexion, the superior pectoralis major (clavicular fibers), anterior and posterior supraspinatus, and anterior deltoid were the most effective flexors, whereas the teres major and posterior deltoid had the largest extensor moment arms. Division of multi-pennate shoulder muscles of broad origins into sub-regions highlighted distinct functional differences across those sub-regions. Most significantly, we found that the superior sub-region of the pectoralis major had the capacity to exert substantial torque in flexion, whereas the middle and inferior sub-regions tended to behave as a stabilizer and extensor, respectively. Knowledge of moment arm differences between muscle sub-regions may assist in identifying the functional effects of muscle sub-region tears, assist surgeons in planning tendon reconstructive surgery, and aid in the development and validation of biomechanical computer models used in implant design.
Quantitative gait assessment is important in diagnosis and management of Parkinson's disease (PD); however, gait characteristics of a cohort are dispersed by patient physical properties including age, height, body mass, and gender, as well as walking speed, which may limit capacity to discern some pathological features. The aim of this study was twofold. First, to use a multiple regression normalization strategy that accounts for subject age, height, body mass, gender, and self-selected walking speed to identify differences in spatial-temporal gait features between PD patients and controls; and second, to evaluate the effectiveness of machine learning strategies in classifying PD gait after gait normalization. Spatial-temporal gait data during self-selected walking were obtained from 23 PD patients and 26 aged-matched controls. Data were normalized using standard dimensionless equations and multiple regression normalization. Machine learning strategies were then employed to classify PD gait using the raw gait data, data normalized using dimensionless equations, and data normalized using the multiple regression approach. After normalizing data using the dimensionless equations, only stride length, step length, and double support time were significantly different between PD patients and controls (p < 0.05); however, normalizing data using the multiple regression method revealed significant differences in stride length, cadence, stance time, and double support time. Random Forest resulted in a PD classification accuracy of 92.6% after normalizing gait data using the multiple regression approach, compared to 80.4% (support vector machine) and 86.2% (kernel Fisher discriminant) using raw data and data normalized using dimensionless equations, respectively. Our multiple regression normalization approach will assist in diagnosis and treatment of PD using spatial-temporal gait data.
Reverse total shoulder arthroplasty increases the moment arms of the major abductors, flexors, adductors, and extensors of the glenohumeral joint, thereby reducing muscle effort during common tasks such as lifting and pushing.
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