EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee.

Electromyography (EMG) is commonly used to measure electrical activity of the skeletal muscles. As exoskeleton technology advances, these signals may be used to predict human intent for control purposes. This study used an artificial neural network trained and tested with knee flexion angles and knee muscle EMG signals to predict knee flexion angles during gait at 50, 100, 150, and 200 ms into the future. The hypothesis of this study was that the algorithm's prediction accuracy would only be affected by time into the future, not subject, gender or side, and that as time into the future increased, the prediction accuracy would decrease. A secondary hypothesis was that as the number of algorithm training trials increased, the prediction accuracy of the artificial neural network (ANN) would increase. The results of this study indicate that only time into the future affected the accuracy of knee flexion angle prediction (p < 0.001), whereby greater time resulted in reduced accuracy (0.68 to 4.62 degrees root mean square error (RMSE) from 50 to 200 ms). Additionally, increased number of training trials resulted in increased angle prediction accuracy.

Trunk muscle activity among older and obese individuals during one-handed carrying.

Manual material handling (MMH) is associated with the development of work-related musculoskeletal disorders (MSDs). One-handed carrying is a particularly challenging form of MMH. Age and obesity have been increasing among the general and working populations in the United States and worldwide. While older and obese workers are more susceptible to MSDs in comparison to younger, healthy workers, the effects of one-handed carrying on trunk muscle activity among these populations have not been comprehensively studied. In this paper, we evaluate the effects of age and obesity on trunk muscle activity of six trunk muscle pairs during one-handed carrying of different loads. The results suggest that older and obese individuals do not exhibit considerably larger muscle activity than young and non-obese individuals while carrying a load of approximately 10 kg in one hand for relatively short distances. Accordingly, 10 kg appears to be an acceptable load to be occasionally carried in one hand by older and/or obese individuals from a muscle activity perspective.

Effects of age and obesity on trunk kinetics and kinematics during dominant side one-handed carrying.

The proportions of older and obese people are increasing in both the general and working populations worldwide. Older and obese individuals are more susceptible to work-related musculoskeletal disorders (MSDs) in comparison with healthy, younger individuals. Manual material handling (MMH) is associated with the development of work-related MSDs. Although previous research has suggested that one-handed carrying is a particularly undesirable method of MMH, the effects of one-handed carrying on trunk kinetics and kinematics among older and/or obese people have not been adequately studied. The objective of this study was to examine the effects of age and obesity on trunk angles and moments during dominant side one-handed carrying of various load magnitudes. Twenty (20) participants divided into four groups with respect to age (young and older) and obesity (obese and non-obese) carried different loads (No-load [0 kg], Light [5.67 kg], and Heavy [10.21 kg]) in their dominant hand for approximately 6 m. Three-dimensional (3D) trunk angles and moments approximately about the L4/L5 vertebral segment were calculated using Visual3D. The findings indicated that while carrying a load in the dominant hand plays an important role in changing trunk kinematics and kinetics, the results were not dependent on age and/or obesity category. Absolute moments were greatest among participants in the obese groups; however, these moments were mitigated when normalized to body weight and height (%BW * Ht). Age did not exacerbate the effects of load magnitude on trunk kinetics and kinematics.