*6.2.1 Single trial*

When the gait events are correctly detected, the spatiotemporal gait parameters can be extracted, such as gait phase duration, gait cycle distribution, foot angle, stride length, and gait speed, as shown in **Figure 18** for a single trial.

As is seen in **Figure 18**, a gait cycle is divided into four successive phases, which are defined as follows:


Hemiparesis can lead to unilateral paresis, i.e., weakness of one side of the body. Compared with the normal gait of healthy subjects, several conclusions can be drawn for the pathological gait of patients from the results shown in **Figure 18**, some of which are as follows:


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*6.2.2 Multiple trials*

**Figure 18.**

**6.3 Rehabilitation process evaluation**

*Applications of MEMS Gyroscope for Human Gait Analysis*

5.The patient's gait speeds of both feet are clearly reduced, and the stride lengths of both lower limbs are greatly shortened, which supports the hypothesis that the healthy side is influenced by the affected side, and the patient even has no

As discussed above, insufficient dorsiflexion of foot motion means that the patient is not capable of lifting the toe adequately during the swing phase, thereby resulting in a quick translation from swing to stance. This disorder could not only yield abnormal proportions of gait phases, affecting the gait symmetry and gait regularity, but also be dangerous to patients for being a high risk of fall as it alters the load distribution.

More trials were performed for a rich data to increase the variability of gait patterns. For demonstration purpose, the average values and standard deviations of the durations of each gait phase and their relative percentages in each gait cycle are calculated for each concerned gait phase of all the trials, as shown in **Figures 19** and **20**. The result of multiple trials further confirms the conclusions made from that of the single trial.

To verify the ability of IMU-based gait analysis system for evaluating the rehabilitation process, a patient's gait was measured once a week for 1 month. The

asymptomatic side due to the so-called compensatory responses.

*Estimated spatiotemporal gait parameters. (a) Healthy subject and (b) Hemiparesis patient.*

*DOI: http://dx.doi.org/10.5772/intechopen.86837*

*Applications of MEMS Gyroscope for Human Gait Analysis DOI: http://dx.doi.org/10.5772/intechopen.86837*

#### **Figure 18.**

*Gyroscopes - Principles and Applications*

Patients during the course of their rehabilitation were recruited as volunteers in our study. For comparison purpose, young healthy subjects were also recruited as volunteers to participate in the study. Prior to each trial, the subjects were asked to stand still for a few seconds to perform initial alignment of the system [52]. During each trial, the patients were instructed to walk at their comfortable speed along a straight-line path about 10 m long, which is along a hospital corridor and free of obstacles. All patients were asked to perform two consecutive walks in forward and backward directions, respectively, and return at the starting position at the end of each trial. For the healthy subjects, the experiment was performed with the same procedure, except that four consecutive walks were performed and the predefined path was 20 m long on a flat floor of a modern

When the gait events are correctly detected, the spatiotemporal gait parameters can be extracted, such as gait phase duration, gait cycle distribution, foot angle,

As is seen in **Figure 18**, a gait cycle is divided into four successive phases, which

stride length, and gait speed, as shown in **Figure 18** for a single trial.

• HS (HS-FF): the phase lasting from HS event to FF event

• HO (HO-TO): the phase lasting from HO event to TO event

Hemiparesis can lead to unilateral paresis, i.e., weakness of one side of the body. Compared with the normal gait of healthy subjects, several conclusions can be drawn for the pathological gait of patients from the results shown in **Figure 18**,

1.The patient exhibits a reduced gait cadence with longer gait cycle and an

2.The patient is affected on the left side, as the stance phases of the left foot are shorter than that of the right side, while the opposite is true for the swing phases, which is in turn due to the affected lower limb that cannot support the

3.The patient exhibits a significantly diminished extension-flexion foot movement, especially for his left foot, where the shortened HS phases are manifes-

4.The patient exhibits a shortened stride length, as although the covered distance of the patient is half that of the healthy subject, the numbers of strides

body weight well alone and creates a highly unstable situation.

tations of insufficient foot dorsiflexion during the swing phase.

**6.1 Experiment setup**

office building.

*6.2.1 Single trial*

**6.2 Experiment results**

are defined as follows:

• ST (FF-HO): the stance phase

• SW (HO-HS): the swing phase

taken were almost the same.

irregular and asymmetric gait pattern.

some of which are as follows:

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*Estimated spatiotemporal gait parameters. (a) Healthy subject and (b) Hemiparesis patient.*

5.The patient's gait speeds of both feet are clearly reduced, and the stride lengths of both lower limbs are greatly shortened, which supports the hypothesis that the healthy side is influenced by the affected side, and the patient even has no asymptomatic side due to the so-called compensatory responses.

As discussed above, insufficient dorsiflexion of foot motion means that the patient is not capable of lifting the toe adequately during the swing phase, thereby resulting in a quick translation from swing to stance. This disorder could not only yield abnormal proportions of gait phases, affecting the gait symmetry and gait regularity, but also be dangerous to patients for being a high risk of fall as it alters the load distribution.

#### *6.2.2 Multiple trials*

More trials were performed for a rich data to increase the variability of gait patterns. For demonstration purpose, the average values and standard deviations of the durations of each gait phase and their relative percentages in each gait cycle are calculated for each concerned gait phase of all the trials, as shown in **Figures 19** and **20**. The result of multiple trials further confirms the conclusions made from that of the single trial.

#### **6.3 Rehabilitation process evaluation**

To verify the ability of IMU-based gait analysis system for evaluating the rehabilitation process, a patient's gait was measured once a week for 1 month. The

**Figure 19.**

*Durations of the gait phases over multiple trials. (a) Healthy subject and (b) Hemiparesis patient.*

**Figure 20.**

*Pie charts of the gait phases. (a) Healthy subject and (b) Hemiparesis patient.*

**Figure 21.**

*Aligned extension-flexion angles of foot during rehabilitation. (a) First week, (b) Second week, (c) Third week and (d) Fourth week.*

estimated foot angles of extension-flexion movement are shown in **Figure 21**, in which each region of maximum foot dorsiflexion angles is marked by an ellipse. For a better comparison, the angles over successive gait cycles are segmented and aligned along the time axis with the same starting point.

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concerned phase.

**Figure 22.**

*(d) Fourth week.*

**7. Conclusions**

*Applications of MEMS Gyroscope for Human Gait Analysis*

As expected, the dorsiflexion range of the affected foot increased gradually as the treatment proceeded. If not, healthcare professionals may need to modify their treatment plans. Meanwhile, the gait symmetry of patient was also improved, as shown in **Figure 22** in terms of the percentages of the gait cycles spent in each

*Averaged percentages of gait phases during rehabilitation. (a) First week, (b) Second week, (c) Third week and* 

The conclusions drawn from **Figures 21** and **22** are consistent with that drawn in Sections 6.1 and 6.2. Based on the quantified spatiotemporal gait parameters that are provided by gyroscope-based foot-mounted gait analysis system, gait performance can be characterized to assess the rehabilitation process of patients with gait abnormalities, which is useful for deciding appropriate medical intervention.

Quantification of human gait via wearable inertial sensors has been attracting increasing interests in recent years, ranging from aiding pathologic diagnosis, choosing appropriate therapy, evaluating treatment efficacy, and assessing rehabilitation outcomes to monitoring gait degradation, predicting fall risks, and preventing elderly falls. This chapter demonstrated that gait analysis system constructed of foot-mounted MEMS gyroscopes could provide a promising way for estimating spatiotemporal gait parameters and has various potential uses in future research and clinical applications. Such systems are not only convenient for clinical diagnosis and treatment use but also can continuously monitor gait changes in nonclinical settings, thus providing seamless gait analysis from clinical to real-world settings. However, although wearable technologies are regarded as solutions to create a more effective, convenient, and economical gait analysis technology, the potential of gait analysis has not been fully exploited thus far. There is still a great deal to do for its pervasive use. In future work, more gait parameters will be closely examined in the spatiotemporal domain, to conduct a thorough examination of person's pathological gait. Furthermore, reasonable indexes will be explored to evaluate the gait performance as fully as possible, and some nonlinear analysis techniques will be utilized to provide insight into the neuromuscular control processes that govern human locomotion.

*DOI: http://dx.doi.org/10.5772/intechopen.86837*

*Applications of MEMS Gyroscope for Human Gait Analysis DOI: http://dx.doi.org/10.5772/intechopen.86837*

**Figure 22.**

*Gyroscopes - Principles and Applications*

**Figure 19.**

**Figure 20.**

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**Figure 21.**

*and (d) Fourth week.*

estimated foot angles of extension-flexion movement are shown in **Figure 21**, in which each region of maximum foot dorsiflexion angles is marked by an ellipse. For a better comparison, the angles over successive gait cycles are segmented and

*Aligned extension-flexion angles of foot during rehabilitation. (a) First week, (b) Second week, (c) Third week* 

*Durations of the gait phases over multiple trials. (a) Healthy subject and (b) Hemiparesis patient.*

*Pie charts of the gait phases. (a) Healthy subject and (b) Hemiparesis patient.*

aligned along the time axis with the same starting point.

*Averaged percentages of gait phases during rehabilitation. (a) First week, (b) Second week, (c) Third week and (d) Fourth week.*

As expected, the dorsiflexion range of the affected foot increased gradually as the treatment proceeded. If not, healthcare professionals may need to modify their treatment plans. Meanwhile, the gait symmetry of patient was also improved, as shown in **Figure 22** in terms of the percentages of the gait cycles spent in each concerned phase.

The conclusions drawn from **Figures 21** and **22** are consistent with that drawn in Sections 6.1 and 6.2. Based on the quantified spatiotemporal gait parameters that are provided by gyroscope-based foot-mounted gait analysis system, gait performance can be characterized to assess the rehabilitation process of patients with gait abnormalities, which is useful for deciding appropriate medical intervention.

### **7. Conclusions**

Quantification of human gait via wearable inertial sensors has been attracting increasing interests in recent years, ranging from aiding pathologic diagnosis, choosing appropriate therapy, evaluating treatment efficacy, and assessing rehabilitation outcomes to monitoring gait degradation, predicting fall risks, and preventing elderly falls. This chapter demonstrated that gait analysis system constructed of foot-mounted MEMS gyroscopes could provide a promising way for estimating spatiotemporal gait parameters and has various potential uses in future research and clinical applications. Such systems are not only convenient for clinical diagnosis and treatment use but also can continuously monitor gait changes in nonclinical settings, thus providing seamless gait analysis from clinical to real-world settings.

However, although wearable technologies are regarded as solutions to create a more effective, convenient, and economical gait analysis technology, the potential of gait analysis has not been fully exploited thus far. There is still a great deal to do for its pervasive use. In future work, more gait parameters will be closely examined in the spatiotemporal domain, to conduct a thorough examination of person's pathological gait. Furthermore, reasonable indexes will be explored to evaluate the gait performance as fully as possible, and some nonlinear analysis techniques will be utilized to provide insight into the neuromuscular control processes that govern human locomotion.
