Biomedical subjects

Miguel A Perez

Publications and source records attributed to Miguel A Perez.

3 recordsLinked to original sources

PubMed2006

Posture and motion variability in non-repetitive manual materials handling tasks.

In developing a motion prediction model it is important to initially consider the sources of variability that a model should reproduce. This initial step is followed by model evaluation, where the variability predicted by the model can be a useful test parameter. An existing lifting-motion dataset collected under controlled laboratory conditions was employed here to evaluate quantitatively some important sources of variability for lift motion modeling. The main source of variability was the segment being analyzed, which accounted for more than 20% of the overall variability. There was substantial left-right symmetry in individual segment variability estimates, which were largest for the upper arm segment and tended to be larger for the upper limbs than the lower limbs. Task-related factors accounted for variability mainly as a function of the segment being considered. Within-participant variability contributions to the dataset were relatively small, whereas the contribution of between-participants variability was dependent on the segment (as large as 50%) and could indicate different lifting strategies across participants. Variability was found to remain relatively constant across the different stages of the lifting movements. Implications of these results for the development and evaluation of motion prediction models are presented. Specifically, while task characteristics may be important modifiers of the mean segment trajectory during a lifting movement, their influence on variability differs based on the segment that is being considered. The relevance of the findings is discussed in terms of their utility in the ergonomic design of tasks and work spaces.

Adult↗

PubMed2003

Principal components analysis as an evaluation and classification tool for lower torso sEMG data.

The use of univariate statistical techniques on multivariate electromyography data can fail to uncover important relationships between variables. Principal components analysis (PCA) is a multivariate statistical technique that can be used as a data exploration tool, both by classifying participants and simplifying data structures. Past research using this technique has focused on discriminating between "patients" and "normals". This investigation explored the use of PCA on electromyography data from healthy participants, with the objective of elucidating any between-participant differences in the multivariate patterns of muscle coactivation. Results indicated that, even between healthy participants, quantitative and qualitative differences in muscle coactivation patterns exist and that, in the context of the lower torso, a large portion (>70%) of the empirically determined muscle activation could be synthesized in a theoretical three-parameter control model.

Abdominal Muscles↗

PubMed2002

Lower torso muscle activation patterns for high-magnitude static exertions: gender differences and the effects of twisting.

STUDY DESIGN: Surface electromyographic signals were collected from 14 lower torso muscles while participants resisted high-magnitude static trunk moments applied in a variety of directions. OBJECTIVES: To obtain a description of muscle activations in response to large moment magnitudes and axial twisting, including levels of agonistic and antagonistic muscle cocontraction. To assess differences in lower torso muscle activation patterns associated with gender and trial repetition. SUMMARY OF BACKGROUND DATA: Back pain is associated with mechanical loads in the back. Biomechanical modeling of these loads is facilitated by knowledge of typical muscle activation patterns. Previous efforts in obtaining such data have often limited their scope to low-magnitude exertions or relatively simple scenarios. METHODS: Eight male and eight female participants, matched by height and mass, performed static exertions in an apparatus that immobilized their lower body while the activation levels of seven bilateral torso muscles were measured using surface electromyography. Activation patterns were analyzed to assess differences resulting from a variety of factors. RESULTS: No significant differences in activation patterns were found between genders or repetitions, but moment magnitude and direction elicited substantial differential responses. Good repeatability was found between trial repetitions, as indicated by intraclass correlation coefficients (>0.65). Significant synergistic muscle coactivation, large intersubject variability (mean coefficient of variation 82.2%), and consistent levels of antagonism ranging from 10% to 30% maximum voluntary exertions were observed. CONCLUSIONS: Individuals of different genders, but similar anthropometry, have comparable muscular reactions to complex torso loads, suggesting similar motor control strategies. Future spine models should consider that the variability in muscle recruitment patterns is larger between subjects than within subjects. High-magnitude exertions, especially those with moment loads in more than one plane, require most muscles to be active (>5%) and moderate levels of antagonism.

Adult↗