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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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Membership functions of each linguistic input variable (a). Low membership functions with different slopes (b).
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f4-sensors-15-16589: Membership functions of each linguistic input variable (a). Low membership functions with different slopes (b).

Mentions: Figure 4a shows an example of the two membership functions' final configuration in a linguistic input variable (FSRx). The red function represents the grade of membership in the low fuzzy set and the green the grade of membership in the high fuzzy set. Across the domain, the sum of memberships is always 1. It is important to note the influence of the chosen slope. As s increases (s → ∞), the membership function becomes clearer, and transitions lose smoothness, although they are faster. Figure 4b shows four sigmoids with different slopes (from 20.0 to 100.0).


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

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

Membership functions of each linguistic input variable (a). Low membership functions with different slopes (b).
© Copyright Policy
Related In: Results  -  Collection

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

f4-sensors-15-16589: Membership functions of each linguistic input variable (a). Low membership functions with different slopes (b).
Mentions: Figure 4a shows an example of the two membership functions' final configuration in a linguistic input variable (FSRx). The red function represents the grade of membership in the low fuzzy set and the green the grade of membership in the high fuzzy set. Across the domain, the sum of memberships is always 1. It is important to note the influence of the chosen slope. As s increases (s → ∞), the membership function becomes clearer, and transitions lose smoothness, although they are faster. Figure 4b shows four sigmoids with different slopes (from 20.0 to 100.0).

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