Descriptive multidimensional statistical methods for analysing signals in a multifactorial biomedical database

Biomechanical comparison of isokinetic lifting and free lifting when applied to chronic low back pain rehabilitation

The study compares free and isokinetic lifting using a multivariate statistical analysis. Each of the 13 male subjects performed three free lifts and three isokinetic lifts using a CYBEX LIFTASK. The measurement variables were obtained from a 3D video system, two force plates and two strain-gauge transducers. Coupling of fuzzy space-time windowing and multiple correspondence analysis was used to show the links between the variables and the differences between the experimental situations. Isokinetic lifting had almost no points in common with free-lifting, but there was a similar range of extension for the different joints. Most free-lifting strategies could not be used in isokinetic lifting, as constraints between the subject and his environment were different. The main drawback of the isokinetic lifting was due to the necessity for individuals to reach the machine speed, yielding high transient efforts. The maximum vertical effort at the L5/S1 joint was about 1600, 1500 and 1400N for low, medium and high speed, whereas it was lower than 1300N, irrespective of the load, during free lifting. In the context of chronic low back pain rehabilitation, movement strategies used in free lifting could not be relearnt using an isokinetic machine. A better understanding of the common points and differences between isokinetic movement and free movement could help rehabilitation physicians to plan rehabilitation programmes, taking advantage of each kind of movement.

A review of analytical techniques for gait data. Part 1: Fuzzy, statistical and fractal methods

In recent years, several new approaches to gait data analysis have been explored, including fuzzy systems, multivariate statistical techniques and fractal dynamics. Through a critical survey of recent gait studies, this paper reviews the potential of these methods to strengthen the gait laboratory's analytical arsenal. It is found that time-honoured multivariate statistical methods are the most widely applied and understood. Although initially promising, fuzzy and fractal analyses of gait data remain largely unknown and their full potential is yet to be realized. The trend towards fusing multiple techniques in a given analysis means that additional research into the application of these two methods will benefit gait data analysis.