Computational intelligent gait-phase detection system to identify pathological gait

  • Senanayake C
  • Senanayake S
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An intelligent gait-phase detection algorithm based on kinematic and
kinetic parameters is presented in this paper. The gait parameters do
not vary distinctly for each gait phase; therefore, it is complex to
differentiate gait phases with respect to a threshold value. To overcome
this intricacy, the concept of fuzzy logic was applied to detect gait
phases with respect to fuzzy membership values. A real-time
data-acquisition system was developed consisting of four force-sensitive
resistors and two inertial sensors to obtain foot-pressure patterns and
knee flexion/extension angle, respectively. The detected gait phases
could be further analyzed to identify abnormality occurrences, and
hence, is applicable to determine accurate timing for feedback. The
large amount of data required for quality gait analysis necessitates the
utilization of information technology to store, manage, and extract
required information. Therefore, a software application was developed
for real-time acquisition of sensor data, data processing, database
management, and a user-friendly graphical-user interface as a tool to
simplify the task of clinicians. The experiments carried out to validate
the proposed system are presented along with the results analysis for
normal and pathological walking patterns.

Author-supplied keywords

  • Fuzzy inference system (FIS)
  • gait-phase detection
  • hardware and software codesign
  • virtual instrumentation

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