**7. Acknowledgment**

396 Health Management – Different Approaches and Solutions

In this paper, we have proposed a so-called FL model to analyze the walking stability and symmetry of different age subjects while walking on a normal pace. The most important finding is that human walking stabilities are not strictly monotone decreasing with age. Walking stability of human beings varies with age, but does not reduce in the elderly people

1. The variability of footprint increases with age for subjects over 30 years old, and it dramatically increases for the elderly over 60 years old, showing much less footprint

2. The variability of walking cycle declines with age. That is to say, the elderly subjects

3. The variability of orbital increases with age for all subjects. In other words, the elderly

4. Human dynamic stability decreases with age except the twenties, which proves previous assumptions. This data mining technology not only gets the contributing

Aging effects on motor control have been implicated as a key factor in adjusting posture during walking. Sensory feedback and muscular strength play important roles in maintaining stability

The footprint stability and walking stability of 20 years old subjects is less than that of over 30 years old. This cannot certify that 20 years old subjects have less quality of neuromuscular control. Nevertheless, they have much strength to control their walking pattern, so it shows a springily walking pattern. The orbital stability is strictly monotone decreasing with age. The

As we mentioned above, in most comprehensive opinions, walking stability will decrease with ageing. But the cycle stability increase with age. Why? It seems to be more confused to understand. In fact, in the three kinds of stability, only the cycle stability describes the relative stability of walking, which is the relative relationship among occurrence sequence of cycle events in a walking cycle, while the other two kinds of stability are absolute stability of posture. That indicates that the elderly subjects have a rigid and inflexible walking pattern. The elderly improves his/her walking stability by maintaining cycle stability more carefully, because it needs less strength to control cycle stability than the other two. That is to say, young subjects have more powerful muscles to control walking balance, while elderly subjects improve their walking stability by keeping their fixed walking patterns carefully.

One conclusion about gait symmetry is that, according to the attributes that selected out by APCLUSTER algorithm and our calculation analysis, we can classify some test objects in order to better meet the natural age groups. An appropriate grouping method to gait

Another conclusion is that, when the symmetry evaluation of normal walking gait, compared the trunk with the limbs, the latter gave larger contribution. Thus, if we have a device to measure gait symmetry of normal people, it may be wrong to wear it at the waist. It may very appropriate if we paste the device somewhere in the lower extremities (for

symmetry analysis will make the results of statistical analysis more meaningful.

example, shank or ankle), of course pair-wise and it will be more effective.

against the presence of unpredictable external perturbations or internal variations of gait.

orbital stability could express the ability of stability control more appropriately.

This is one of the most important findings of this paper.

dynamic stability features, but also makes the data acquisition simpler.

**6. Conclusions and discussions** 

have more cycle stability.

has weaker orbital stability.

**6.2 Discussions in clinic** 

**6.1 Main conclusions** 

always.

stability.

This work is supported in part by the Fukushima Prefectural Foundation for the Advancement of Science and Education (No.F-18-10), Japan, Shanghai, and Shanghai Leading Academic Discipline Project (J50103), China, and Pujiang Program from Science and Technology Commission of Shanghai Municipality, China.

The basic part of this work was implemented in the Biomedical Information Technology Lab, the University of Aizu, Fukushima, Japan.
