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An Ambulatory System for Gait Monitoring Based on Wireless Sensorized Insoles.

González I, Fontecha J, Hervás R, Bravo J - Sensors (Basel) (2015)

Bottom Line: The system can be used in gait analysis mobile applications, and it is designed for real-time demarcation of gait phases.Additionally, to provide a solution that is insensitive to perturbations caused by non-walking activities, a probabilistic classifier is employed to discriminate walking forward from other low-level activities, such as turning, walking backwards, lateral walking, etc.The combination of these two algorithms constitutes the first approach towards a continuous gait assessment system, by means of the avoidance of non-walking influences.

View Article: PubMed Central - PubMed

Affiliation: MAmI Research Lab, University of Castilla-La Mancha, Esc. Sup. de Informática, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain. ivan.gdiaz@uclm.es.

ABSTRACT
A new gait phase detection system for continuous monitoring based on wireless sensorized insoles is presented. The system can be used in gait analysis mobile applications, and it is designed for real-time demarcation of gait phases. The system employs pressure sensors to assess the force exerted by each foot during walking. A fuzzy rule-based inference algorithm is implemented on a smartphone and used to detect each of the gait phases based on the sensor signals. Additionally, to provide a solution that is insensitive to perturbations caused by non-walking activities, a probabilistic classifier is employed to discriminate walking forward from other low-level activities, such as turning, walking backwards, lateral walking, etc. The combination of these two algorithms constitutes the first approach towards a continuous gait assessment system, by means of the avoidance of non-walking influences.

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Detected gait subphases (a). FSR and foot pressure patterns (b).
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f3-sensors-15-16589: Detected gait subphases (a). FSR and foot pressure patterns (b).

Mentions: The human gait cycle can be divided into two phases: stance and swing [1]. However, the complexity of human walking involves different kinetics and kinematics events within these phases. Therefore, a more refined classification is required to model the human gait cycle properly. Perry [1] distinguishes eight different subphases in her model of the human gait cycle. In our solution, derived from Perry's model, we segment five subphases (Figure 3a). The stance subphases (LR, MSt, TSt, PSw) can be identified only by examining the foot pressure patterns using the FSRs. However, our pressure sensors are useless for identifying Perry's proposed swing subphases (initial swing (ISw), mid-swing (MSw) and terminal swing (TSw). In our case, only the swing phase (Sw) as a whole can be detected when no pressure is exerted on any FSR. To detect the three swing subphases, a procedure involving two gyroscopes to estimate the knee angle (as in [30]) could be implemented; however, the device would become more intrusive.


An Ambulatory System for Gait Monitoring Based on Wireless Sensorized Insoles.

González I, Fontecha J, Hervás R, Bravo J - Sensors (Basel) (2015)

Detected gait subphases (a). FSR and foot pressure patterns (b).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541895&req=5

f3-sensors-15-16589: Detected gait subphases (a). FSR and foot pressure patterns (b).
Mentions: The human gait cycle can be divided into two phases: stance and swing [1]. However, the complexity of human walking involves different kinetics and kinematics events within these phases. Therefore, a more refined classification is required to model the human gait cycle properly. Perry [1] distinguishes eight different subphases in her model of the human gait cycle. In our solution, derived from Perry's model, we segment five subphases (Figure 3a). The stance subphases (LR, MSt, TSt, PSw) can be identified only by examining the foot pressure patterns using the FSRs. However, our pressure sensors are useless for identifying Perry's proposed swing subphases (initial swing (ISw), mid-swing (MSw) and terminal swing (TSw). In our case, only the swing phase (Sw) as a whole can be detected when no pressure is exerted on any FSR. To detect the three swing subphases, a procedure involving two gyroscopes to estimate the knee angle (as in [30]) could be implemented; however, the device would become more intrusive.

Bottom Line: The system can be used in gait analysis mobile applications, and it is designed for real-time demarcation of gait phases.Additionally, to provide a solution that is insensitive to perturbations caused by non-walking activities, a probabilistic classifier is employed to discriminate walking forward from other low-level activities, such as turning, walking backwards, lateral walking, etc.The combination of these two algorithms constitutes the first approach towards a continuous gait assessment system, by means of the avoidance of non-walking influences.

View Article: PubMed Central - PubMed

Affiliation: MAmI Research Lab, University of Castilla-La Mancha, Esc. Sup. de Informática, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain. ivan.gdiaz@uclm.es.

ABSTRACT
A new gait phase detection system for continuous monitoring based on wireless sensorized insoles is presented. The system can be used in gait analysis mobile applications, and it is designed for real-time demarcation of gait phases. The system employs pressure sensors to assess the force exerted by each foot during walking. A fuzzy rule-based inference algorithm is implemented on a smartphone and used to detect each of the gait phases based on the sensor signals. Additionally, to provide a solution that is insensitive to perturbations caused by non-walking activities, a probabilistic classifier is employed to discriminate walking forward from other low-level activities, such as turning, walking backwards, lateral walking, etc. The combination of these two algorithms constitutes the first approach towards a continuous gait assessment system, by means of the avoidance of non-walking influences.

Show MeSH