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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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Wireless sensorized insoles setup (a) and new gait instance user interface (b).
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f8-sensors-15-16589: Wireless sensorized insoles setup (a) and new gait instance user interface (b).

Mentions: To verify the gait phase detection algorithm's accuracy, the system was tested on five male subjects. All candidates presented a healthy gait and had an age range of 33 ± 2. Prior to the gait data capture, the subjects were asked to perform some trials of walking with the ambulatory system attached as in Figure 8a. For each subject, three separate trials were stored, each of them consisting of 10 gait cycles for each foot. The first and the last gait cycles in the trials were discarded. As we said in Section 3.3, the start and the end of the walking forward sequence do not properly represent the subject's gait, due to poor stability (in the case of the beginning) and gait deceleration (at the end of the walking sequence).


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

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

Wireless sensorized insoles setup (a) and new gait instance user interface (b).
© Copyright Policy
Related In: Results  -  Collection

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

f8-sensors-15-16589: Wireless sensorized insoles setup (a) and new gait instance user interface (b).
Mentions: To verify the gait phase detection algorithm's accuracy, the system was tested on five male subjects. All candidates presented a healthy gait and had an age range of 33 ± 2. Prior to the gait data capture, the subjects were asked to perform some trials of walking with the ambulatory system attached as in Figure 8a. For each subject, three separate trials were stored, each of them consisting of 10 gait cycles for each foot. The first and the last gait cycles in the trials were discarded. As we said in Section 3.3, the start and the end of the walking forward sequence do not properly represent the subject's gait, due to poor stability (in the case of the beginning) and gait deceleration (at the end of the walking sequence).

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