**6.1 Main conclusions**

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 always.


#### **6.2 Discussions in clinic**

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

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

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

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

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

The next study is to further deepen the existing clustering classification, including gait symmetry attributes and the relationship between weight and height in order to obtain meaningful results. It is our goal that combining gait symmetry attributes with a number of individual characteristics may construct a simple approach to determine a test object should belong to which group.

Clinical gait analysis is aimed at revealing a key aspect of abnormal gait and impact factors, so as to assist the rehabilitation assessment and treatment, but also help to assist the clinical diagnosis, evaluation. We hope that we can evaluate the symmetry degree of a person gait accurately, not whether symmetry or asymmetry. In other words, it can't use one piece of value, but use a set of indices to evaluate. And then each index may indicate an aspect of gait symmetry.

Further research needs to determine how these gait symmetry is related to actual fall risk. At least to a certain extent, the symmetry between the low-body such as legs seems to codetermine the stability of walking.
