*Fiber Bragg Gratings as e-Health Enablers: An Overview for Gait Analysis Applications DOI: http://dx.doi.org/10.5772/intechopen.81136*

spatio-temporal, kinematic, kinetic, and dynamic electromyography (EMG), as shown in **Table 1** [22]. From such parameters, the ones that require a more specialized technology to be monitored outside the clinical environment, and therefore passible of being monitored in a gait e-Health architecture, are [7, 23, 24]:


The act of walking implies the movement of the whole body, and specifically, it requires a synchronized movement of each lower limb apart. Therefore, the gait pattern of an individual can be affected by a disorder in any segment of the body, like for instance, problems in the spinal cord or from a reduced knee flexion in patients with an anterior cruciate ligament reconstruction [25]. For that reason, the analysis of the gait cycle is a vital tool for the biomechanical mobility monitoring, as it can give crucial information not only about the lower limbs health condition, but also allows to infer details about other possible pathologies related to the dynamic movement of the body [26]. So, by monitoring the parameters previously listed, it is possible to assess the health conditions for the body parts involved in walking, namely the lower limbs and its joint. These parameters can be analyzed using objective and subjective techniques [7, 27, 28].

The subjective analysis is based on the observation of the patient while walking, and is generally performed in clinical environment under the supervision


**Table 1.**

*Parameters generally used for gait analysis (adapted from [22]).*

*Applications of Optical Fibers for Sensing*

*Representation of the stance and swing phases of a gait cycle.*

support and ends with the second double support period [18–20]. The double support period corresponds to the percentage of the cycle when both feet are simultaneously in contact with the floor and it describes the smooth transition between the left and the right single limbs support [18]. During the first double support, the heel strikes the floor (heel strike), marking the beginning of the gait cycle. The cycle evolves then toward the single period support, with the foot moving down toward the floor into a foot-flat position, where a stable support base is created for the rest of the body. Within the single support phase, the body is propelled over the foot, with the hip joint vertically aligned with ankle joint in the event characterized as the mid stance. From that point onward, the second double support phase starts, with the lower limb moving the body center of mass forward during the heel rise event, where the heel loses contact with the floor. The last contact of the foot with the floor is made by the big toe (hallux), at the toe off event, which also marks the end of the stance phase and the beginning of

During the swing phase, there is no contact between the plantar foot and the floor, and the limb continues its movement forward, which can be divided into three different periods: initial swing, mid swing, and terminal swing. In the initial swing, the lower limb vertical length should be reduced, for the foot to clear the floor and to accelerate forward by flexing the hip and knee, together with ankle dorsiflexion. The mid swing is characterized by the alignment of the accelerating limb with the stance limb. In this phase, the ankle and the hip joints are aligned. During the terminal swing, the limb undergoes a deceleration while it prepares for the contact with the floor, in the heel strike of the start of a new cycle [19–21]. As described, the swing phase is characterized by accelerations and decelerations of the lower limb, which require a more demanding muscular effort at the hip

Gait analysis is a systematic procedure that allows the detection of negative deviations from normal gait pattern, as well as their causes. Based on such analysis, it is possible to quantify the parameters involved in the movement of the lower limbs and retrieve the mechanisms that rule the human body movement [22]. Based on the gait cycle pattern described earlier, there are several parameters that can be physically monitored in order to assess the patient's health: anthropometric,

**26**

level [18].

**2.2 Gait parameters**

the swing phase [20].

**Figure 2.**

of a doctor or a therapist. For this analysis, the patient is asked to vary several gait-related parameters, while walking in a predetermined circuit [7]. This type of analysis is bit limited in the information that can be retrieved, nevertheless, this could be useful for an initial evaluation and posterior decision on which objective techniques should be used. In contrast to the subjective techniques, objective gait analysis is more of a quantitative evaluation of the parameters listed above. This type of analysis requires the use of different types of equipment and procedures to measure the gait parameters. These methodologies can be categorized according to the technology used, varying from the ones based on imaging, instrumented walking platforms or floor sensors, and wearable sensors [7, 24, 29].

For an e-Health architecture, the most suitable technology would be the one built using wearable sensors, which would be able to acquire the patient gait parameters, everywhere and under any conditions. Among those, FBGs can be considered as an objective technique for gait analysis (allows the quantification of parameters during gait analysis), which could be used in instrumented platforms or as wearable sensors [30]. Recently, the use of FBGs as wearable sensors for remote monitoring of patients has been reported [12, 31, 32].

### **2.3 Gait pattern monitoring: e-Health architecture**

The Internet of Things (IoT) concept is the fusion between pervasive network connectivity and the computing capability expanded to sensing devices and objects, able to acquire and exchange data autonomously. In recent years, due to the potential gains brought to the citizens' quality of life, IoT is seen as a whole platform able to bridge people and objects by integrating the smart concept into people's life, namely, smart cities, homes, wearables, and mobility [33].

Within the vast field of applications provided by IoT, e-Health stands out as one of the most influential topics on life-quality of humans, as smart and connected healthcare services have been requested more enthusiastically. e-Health is gaining too much attention mainly due to the joint effect of the increase of insufficient and ineffective healthcare services, allied to the change in population demographics and the increasing demand of such change entails. The world's population aged over 60 years is expected to reach 2 billion by 2050 [34], which implies the rise of chronic diseases that may be translated on different degrees of mobility impairments, requiring a close monitoring and a patient-centered healthcare service, where the healthcare providers and patients are pervasively connected [12, 33]. Also aligned with such demands, the market for home medical devices is set to significantly grow from \$27.8 billion in 2015 up to nearly \$44.3 billion by 2020 [35]. The increase in available e-Health solutions is a remarkable step toward improving the healthcare services, along with the autonomy of debilitated or impaired citizens. Fundamentally, e-Health can be seen as the solution to help the elders and patients with chronic illness to live an active life, without compromising their mobility or daily routine [32].

e-Health systems use remote monitoring architectures composed of sensing devices responsible to collect patients' physiological information, analyze and store such data in the cloud. The information can afterward be wirelessly sent to the healthcare professionals for a decision/action. The continuous flow of information on the patient's condition improves the provided service at a lower cost, while simultaneously enhancing the life quality of patients, who need continuous attention [33].

The patients' physiological information can be collected by networked sensors, integrated in smart wearable systems, or placed within the patients living

**29**

**Figure 3.**

*Fiber Bragg Gratings as e-Health Enablers: An Overview for Gait Analysis Applications*

environment. Considering the specific case of gait analysis, the continuous, automatic, and remote monitoring of gait of impaired or under rehabilitation citizens allows the objective assessment for preventive and proactive supervision of the pathologies, as well as to closely assist the therapies in progress [36]. In this scenario, wearable sensing architecture allows not only the evaluation of the patients in the course of daily life activities, but also provides the feedback on the recovering/rehabilitation therapy to patients and medical staff through ubiquitous connectivity. Based on that feedback, new therapeutic instructions can be given remotely to the patient, maximizing the efficiency of the provided

An e-Health architecture to monitor the gait pattern of a citizen/patient comprises three key elements: the monitoring system composed of a sensors' network (preferably wearable); a computer/analysis system to collect, analyze, and store the data; and finally, the wireless mobile gateway, responsible for data processing and wireless transmission to medical servers and decision centers [31, 32]. **Figure 3**

The first part of the considered architecture is responsible for the data sensing and consequently, for the information given to action centers. Therefore, it is crucial that the sensing network is as accurate and reliable as possible. The use of FBGs as IoT and e-Health enablers is becoming increasingly common, due to their sensing characteristics, when compared with the ones of their electronic counterparts, namely small size (in the order of micrometers), biocompatibility, multiplexing capability, immunity to electromagnetic interference, in addition to their high accuracy and sensitivity, even for applications in challenging environments [37–39]. Consequently, FBGs can be used as a reliable solution for the integration in e-Health architectures, as for monitoring sensing systems in biomechanics and physical rehabilitation. Some examples can be mentioned, covering the detection of bone strains, mapping of gait plantar and shear pressures, measuring of pressures in orthopedic joints and angles between the body segments, as have already been successfully

In the following sections, the use of FBGs to monitor different body segments

schematizes the typical architecture involved in an e-Health scenario.

*DOI: http://dx.doi.org/10.5772/intechopen.81136*

healthcare services [31, 32].

reported [12, 30].

involved in gait will be explored.

*Possible architecture for a gait e-Health monitoring system.*

### *Fiber Bragg Gratings as e-Health Enablers: An Overview for Gait Analysis Applications DOI: http://dx.doi.org/10.5772/intechopen.81136*

environment. Considering the specific case of gait analysis, the continuous, automatic, and remote monitoring of gait of impaired or under rehabilitation citizens allows the objective assessment for preventive and proactive supervision of the pathologies, as well as to closely assist the therapies in progress [36]. In this scenario, wearable sensing architecture allows not only the evaluation of the patients in the course of daily life activities, but also provides the feedback on the recovering/rehabilitation therapy to patients and medical staff through ubiquitous connectivity. Based on that feedback, new therapeutic instructions can be given remotely to the patient, maximizing the efficiency of the provided healthcare services [31, 32].

An e-Health architecture to monitor the gait pattern of a citizen/patient comprises three key elements: the monitoring system composed of a sensors' network (preferably wearable); a computer/analysis system to collect, analyze, and store the data; and finally, the wireless mobile gateway, responsible for data processing and wireless transmission to medical servers and decision centers [31, 32]. **Figure 3** schematizes the typical architecture involved in an e-Health scenario.

The first part of the considered architecture is responsible for the data sensing and consequently, for the information given to action centers. Therefore, it is crucial that the sensing network is as accurate and reliable as possible. The use of FBGs as IoT and e-Health enablers is becoming increasingly common, due to their sensing characteristics, when compared with the ones of their electronic counterparts, namely small size (in the order of micrometers), biocompatibility, multiplexing capability, immunity to electromagnetic interference, in addition to their high accuracy and sensitivity, even for applications in challenging environments [37–39]. Consequently, FBGs can be used as a reliable solution for the integration in e-Health architectures, as for monitoring sensing systems in biomechanics and physical rehabilitation. Some examples can be mentioned, covering the detection of bone strains, mapping of gait plantar and shear pressures, measuring of pressures in orthopedic joints and angles between the body segments, as have already been successfully reported [12, 30].

In the following sections, the use of FBGs to monitor different body segments involved in gait will be explored.

**Figure 3.** *Possible architecture for a gait e-Health monitoring system.*

*Applications of Optical Fibers for Sensing*

of patients has been reported [12, 31, 32].

**2.3 Gait pattern monitoring: e-Health architecture**

namely, smart cities, homes, wearables, and mobility [33].

of a doctor or a therapist. For this analysis, the patient is asked to vary several gait-related parameters, while walking in a predetermined circuit [7]. This type of analysis is bit limited in the information that can be retrieved, nevertheless, this could be useful for an initial evaluation and posterior decision on which objective techniques should be used. In contrast to the subjective techniques, objective gait analysis is more of a quantitative evaluation of the parameters listed above. This type of analysis requires the use of different types of equipment and procedures to measure the gait parameters. These methodologies can be categorized according to the technology used, varying from the ones based on imaging, instrumented walk-

For an e-Health architecture, the most suitable technology would be the one built using wearable sensors, which would be able to acquire the patient gait parameters, everywhere and under any conditions. Among those, FBGs can be considered as an objective technique for gait analysis (allows the quantification of parameters during gait analysis), which could be used in instrumented platforms or as wearable sensors [30]. Recently, the use of FBGs as wearable sensors for remote monitoring

The Internet of Things (IoT) concept is the fusion between pervasive network connectivity and the computing capability expanded to sensing devices and objects, able to acquire and exchange data autonomously. In recent years, due to the potential gains brought to the citizens' quality of life, IoT is seen as a whole platform able to bridge people and objects by integrating the smart concept into people's life,

Within the vast field of applications provided by IoT, e-Health stands out as one of the most influential topics on life-quality of humans, as smart and connected healthcare services have been requested more enthusiastically. e-Health is gaining too much attention mainly due to the joint effect of the increase of insufficient and ineffective healthcare services, allied to the change in population demographics and the increasing demand of such change entails. The world's population aged over 60 years is expected to reach 2 billion by 2050 [34], which implies the rise of chronic diseases that may be translated on different degrees of mobility impairments, requiring a close monitoring and a patient-centered healthcare service, where the healthcare providers and patients are pervasively connected [12, 33]. Also aligned with such demands, the market for home medical devices is set to significantly grow from \$27.8 billion in 2015 up to nearly \$44.3 billion by 2020 [35]. The increase in available e-Health solutions is a remarkable step toward improving the healthcare services, along with the autonomy of debilitated or impaired citizens. Fundamentally, e-Health can be seen as the solution to help the elders and patients with chronic illness to live an active life, without compromising their mobility or

e-Health systems use remote monitoring architectures composed of sensing devices responsible to collect patients' physiological information, analyze and store such data in the cloud. The information can afterward be wirelessly sent to the healthcare professionals for a decision/action. The continuous flow of information on the patient's condition improves the provided service at a lower cost, while simultaneously enhancing the life quality of patients, who need continuous atten-

The patients' physiological information can be collected by networked sensors, integrated in smart wearable systems, or placed within the patients living

ing platforms or floor sensors, and wearable sensors [7, 24, 29].

**28**

tion [33].

daily routine [32].
