**5. Conclusion and perspectives**

The chapter describes a wireless embedded system used to record information related to the gait of patients with after-effects of stroke in ecological situations.

**157**

*An Embedded Gait Analysis System for CNS Injury Patients*

It is based on the use of seven inertial sensors. Each sensor executes the orientation estimation algorithm. Even if each sensor uses its local clock and performs the measurement according to its own time base, their synchronization through reference broadcast time synchronization (RBTS) allows synchronization accuracy, synchronization speed, and the number of sensors to synchronize. Other constraints related to the use of the wireless link are the speed of transmission and energy consumption. To address these constraints, among wireless transmission technologies, we selected a proprietary protocol that allows the clock error between two sensors limited to a value less than 37.6 μs that does not accumulate over time. Thanks to the efficiency of this protocol, the sampling frequency to transmit the raw measurements and the orientation of the segments estimated by the sensors

The embedded wireless system uses a pair of range-finders to determine the gait events to split the recordings into gait cycles and calculate the temporal parameters. The method to determine the gait events, initial contact (IC) and final contact (FC), uses relative distance between feet and speed angular feet. The system also estimates the articular angles of the lower limbs. This information is the input of a piece of post-processing software that determines the temporal parameters and automatically cuts the joint angles into walking cycles. A series of experiments was conducted to evaluate the accuracy and robustness of the system. The difference between the values measured by the embedded system and the 3D-GA system shows a good robustness on the pathological path, which is

This development shows the evolution of our embedded gait analysis system. The final system provides the measured quantities comparable to those from a 3D-GA system. The algorithmic and material design takes into account the constraints of pathological walking and use in an ecological situation. The algorithms for segment orientation estimation and gait event detection are optimized for pathological walking. The new method we have proposed for determining the events of walking improves the robustness of event detection in the pathological case. In order for the system to be used in an ecological situation and to record the activity of the person in his daily life, the electronic design of the system is carried out in order to minimize the influence of the system on the walking of people and to have enough autonomy to record walking all day long. An effort has also been made

In addition, it is possible to analyze the walk in more complex or functional situations. Indeed, certain clinical tests required different movements on the patient. This is the case, for example, for the TUG (Timed Up and Go). This test requires the patient to get up from a chair, walk forward 3 m, turn around a pad, walk back, and then sit down again. This test involves several motor skills. According to the chronometric performance, the risk of falling for the patient was identified for a number of values. We also looked to turn strategies in this test for stroke patients compared to healthy subjects from the quantification of the pelvis trajectory [47]. To make these comparisons, DTW (Dynamic Time Warping) methods on the trajectory of the basin on the horizontal plane were used. These methods allow identification of patients' strategies and therefore their classification as shown in [48, 49]. It is realistic to hypothesize on walking route (straight lines, stairs, up and down slopes, etc.), identification of displacement strategies from the calculation of the DTW on joint kinematics and/or segmental accelerations to follow the evolution (improvement or degradation) of the locomotor possibilities of the patient. This promising approach

*DOI: http://dx.doi.org/10.5772/intechopen.83826*

reaches 109 Hz.

quite innovative.

to improve the ease of implementation and use.

is the main line of our current work.

*Assistive and Rehabilitation Engineering*

*Correlation (a) and Bland-Altman plot (b).*

The proposed system provides good accuracy in determining hemiparetic events but has a limitation. The method uses the relative distance between the feet as the main source of information to determine the IC event. Then, the FC is searched between two IC events. As a result, if the IC event is not correctly determined, the FC event cannot be detected. As shown in **Figure 11**, the IC events of both feet correspond to the local maximum of the relative distance signal of the feet. If the peaks corresponding to the IC are very attenuated, the risk of no longer detecting the IC event increases (**Figure 15**). This figure shows the relative distance and inclinations of the two feet. The colored areas represent the areas where the peaks on the distance signal corresponding to the IC must be observed. In green zones, significant peaks are observable; in red zones, most peaks are very attenuated or undetectable. This system cannot then be used on people that walk with a maximum distance of the feet very close to the length of the step during the double phase support. In practice, for all persons that have a minimum relative distance between feet larger

The chapter describes a wireless embedded system used to record information related to the gait of patients with after-effects of stroke in ecological situations.

**156**

**Figure 15.**

**Figure 14.**

*CI nondetectable case.*

than 20 cm, this system works properly.

**5. Conclusion and perspectives**

It is based on the use of seven inertial sensors. Each sensor executes the orientation estimation algorithm. Even if each sensor uses its local clock and performs the measurement according to its own time base, their synchronization through reference broadcast time synchronization (RBTS) allows synchronization accuracy, synchronization speed, and the number of sensors to synchronize. Other constraints related to the use of the wireless link are the speed of transmission and energy consumption. To address these constraints, among wireless transmission technologies, we selected a proprietary protocol that allows the clock error between two sensors limited to a value less than 37.6 μs that does not accumulate over time. Thanks to the efficiency of this protocol, the sampling frequency to transmit the raw measurements and the orientation of the segments estimated by the sensors reaches 109 Hz.

The embedded wireless system uses a pair of range-finders to determine the gait events to split the recordings into gait cycles and calculate the temporal parameters. The method to determine the gait events, initial contact (IC) and final contact (FC), uses relative distance between feet and speed angular feet. The system also estimates the articular angles of the lower limbs. This information is the input of a piece of post-processing software that determines the temporal parameters and automatically cuts the joint angles into walking cycles. A series of experiments was conducted to evaluate the accuracy and robustness of the system. The difference between the values measured by the embedded system and the 3D-GA system shows a good robustness on the pathological path, which is quite innovative.

This development shows the evolution of our embedded gait analysis system. The final system provides the measured quantities comparable to those from a 3D-GA system. The algorithmic and material design takes into account the constraints of pathological walking and use in an ecological situation. The algorithms for segment orientation estimation and gait event detection are optimized for pathological walking. The new method we have proposed for determining the events of walking improves the robustness of event detection in the pathological case. In order for the system to be used in an ecological situation and to record the activity of the person in his daily life, the electronic design of the system is carried out in order to minimize the influence of the system on the walking of people and to have enough autonomy to record walking all day long. An effort has also been made to improve the ease of implementation and use.

In addition, it is possible to analyze the walk in more complex or functional situations. Indeed, certain clinical tests required different movements on the patient. This is the case, for example, for the TUG (Timed Up and Go). This test requires the patient to get up from a chair, walk forward 3 m, turn around a pad, walk back, and then sit down again. This test involves several motor skills. According to the chronometric performance, the risk of falling for the patient was identified for a number of values. We also looked to turn strategies in this test for stroke patients compared to healthy subjects from the quantification of the pelvis trajectory [47]. To make these comparisons, DTW (Dynamic Time Warping) methods on the trajectory of the basin on the horizontal plane were used. These methods allow identification of patients' strategies and therefore their classification as shown in [48, 49]. It is realistic to hypothesize on walking route (straight lines, stairs, up and down slopes, etc.), identification of displacement strategies from the calculation of the DTW on joint kinematics and/or segmental accelerations to follow the evolution (improvement or degradation) of the locomotor possibilities of the patient. This promising approach is the main line of our current work.

*Assistive and Rehabilitation Engineering*
