Assessment of Waveform Similarity in Clinical Gait Data: The Linear Fit Method | Zendy

Marco Iosa | Zendy; Andrea Cereatti | Zendy; Andrea Merlo | Zendy; Isabella Campanini | Zendy; Stefano Paolucci | Zendy; Aurelio Cappozzo | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Assessment of Waveform Similarity in Clinical Gait Data: The Linear Fit Method

Author(s) -

Marco Iosa,

Andrea Cereatti,

Andrea Merlo,

Isabella Campanini,

Stefano Paolucci,

Aurelio Cappozzo

Publication year - 2014

Publication title -

biomed research international

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.772

H-Index - 126

eISSN - 2314-6141

pISSN - 2314-6133

DOI - 10.1155/2014/214156

Subject(s) - waveform , similarity (geometry) , normalization (sociology) , pattern recognition (psychology) , computer science , gait , gait analysis , reliability (semiconductor) , kinematics , sensitivity (control systems) , artificial intelligence , offset (computer science) , data mining , statistics , mathematics , physical medicine and rehabilitation , medicine , engineering , power (physics) , physics , classical mechanics , quantum mechanics , electronic engineering , sociology , anthropology , image (mathematics) , programming language , telecommunications , radar

The assessment of waveform similarity is a crucial issue in gait analysis for the comparison of kinematic or kinetic patterns with reference data. A typical scenario is in fact the comparison of a patient's gait pattern with a relevant physiological pattern. This study aims to propose and validate a simple method for the assessment of waveform similarity in terms of shape, amplitude, and offset. The method relies on the interpretation of these three parameters, obtained through a linear fit applied to the two data sets under comparison plotted one against the other after time normalization. The validity of this linear fit method was tested in terms of appropriateness (comparing real gait data of 34 patients with cerebrovascular accident with those of 15 healthy subjects), reliability, sensitivity, and specificity (applying a cluster analysis on the real data). Results showed for this method good appropriateness, 94.1% of sensitivity, 93.3% of specificity, and good reliability. The LFM resulted in a simple method suitable for analysing the waveform similarity in clinical gait analysis.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research