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Human Movement Variability and Aging

Nicholas Stergiou, Jenny A. Kent, and Denise McGrath

An optimal level of variability enables us to interact adaptively and safely to a continuously changing environment, where often our movements must be adjusted in a matter of milliseconds. A large body of research exists that demonstrates natural variability in healthy gait (along with variability in other, healthy biological signals such as heart rate) and a loss of this variability in aging and injury, as well as in a variety of neurodegenerative and physiological disorders. We submit that this field of research is now in pressing need of an innovative “next step” that goes beyond the many descriptive studies that characterize levels of variability in various patient populations. We need to devise novel therapies that will harness the existing knowledge on biological variability and create new possibilities for those in the grip of disease. We also propose that the nature of the specific physiological limitation present in the neuromuscular apparatus may be less important in the physiological complexity framework than the control mechanisms adopted by the older individual in the coordination of the available degrees of freedom. The theoretical underpinnings of this framework suggest that interventions designed to restore healthy system dynamics may optimize functional improvements in older adults. We submit that interventions based on the restoration of optimal variability and movement complexity could potentially be applied across a range of diseases or dysfunctions as it addresses the adaptability and coordination of available degrees of freedom, regardless of the internal constraints of the individual.

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A Comparison of Algorithms for Body-Worn Sensor-Based Spatiotemporal Gait Parameters to the GAITRite Electronic Walkway

Barry R. Greene, Timothy G. Foran, Denise McGrath, Emer P. Doheny, Adrian Burns, and Brian Caulfield

This study compares the performance of algorithms for body-worn sensors used with a spatiotemporal gait analysis platform to the GAITRite electronic walkway. The mean error in detection time (true error) for heel strike and toe-off was 33.9 ± 10.4 ms and 3.8 ± 28.7 ms, respectively. The ICC for temporal parameters step, stride, swing and stance time was found to be greater than 0.84, indicating good agreement. Similarly, for spatial gait parameters—stride length and velocity—the ICC was found to be greater than 0.88. Results show good to excellent concurrent validity in spatiotemporal gait parameters, at three different walking speeds (best agreement observed at normal walking speed). The reported algorithms for body-worn sensors are comparable to the GAITRite electronic walkway for measurement of spatiotemporal gait parameters in healthy subjects.