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Any voluntary motion of the body causes an internal perturbation of balance. Load transfer during manual material handling may increase these perturbations. This study investigates effects of stance condition on postural control during lifting. Nineteen healthy subjects repeatedly lifted and lowered a load between a desk and a shelf. The base of support was varied between parallel and step stance. Ground reaction force and segmental kinematics were measured. Load transfer during lifting perturbed balance. In parallel stance postural response consisted of axial movements in the sagittal plane. Such strategy was accompanied by increased posterior shear forces after lift-off. Lifting in step stance provided extended support in anterior/posterior direction. The postural control mechanisms in the sagittal plane are less complex as compared to parallel stance. However, lifting in step stance was asymmetrical and thus accompanied by distinct lateral transfer of the body. Lateral shear forces were larger as compared to parallel stance. Both lifting techniques exhibit positive and negative aspects. We cannot recommend either one as being better in terms of postural control.

Spectral parameters of the surface electromyographic (EMG) signal from lumbar back muscles assessed during a fatiguing isometric contraction can be used to classify different categories of low back pain (LBP) subjects and control subjects without LBP. In the test protocol currently used at the NeuroMuscular Research Center at Boston University, subjects contract their back muscles at 80% of their maximal voluntary contraction (MVC) force. This fatigue-based protocol has been successfully applied to persons with subacute or chronic LBP; those in acute pain, however, have not been included because of their inability to perform a maximal exertion. In this paper we will examine the force sensitivity of the currently used EMG parameters and also give an overview of some of our efforts to develop new test procedures. Our goal is to develop force-insensitive surface EMG parameters that can be used for classification purposes in populations of subjects who develop low trunk extension forces. In addition, the development of a model to predict MVC from anthropometrical measurements will be presented.