Date Approved

1-18-2022

Embargo Period

1-20-2023

Document Type

Thesis

Degree Name

M.S. Mechanical Engineering

Department

Mechanical Engineering

College

Henry M. Rowan College of Engineering

Advisor

Behrad Koohbor, Ph.D.

Committee Member 1

Chen Shen, Ph.D.

Keywords

Human Motion Analysis, Joints Kinematics, physiological signal acquisition and processing, Trip and Fall Prevention, Trip Detection

Subject(s)

Biomechanics; Falls (Accidents)--Prevention

Disciplines

Biomechanical Engineering

Abstract

Trips and falls are one of the most common causes for injuries among elderly. The existing trip-and-falls studies primarily focus on the proactive fall prevention approaches, while active prevention strategies remain largely unexplored. This thesis aims to provide first steps towards active trip-and-fall prevention by developing various algorithms capable of detecting trip in human walking faster than the human voluntary reactions (~200 ms). The measurements of human kinematics are used as the inputs in the algorithms. The proposed algorithms include three threshold-based detection methods, an optimized elastic time-series alignment tool called dynamic time warping (DTW) that overcomes problems of time dependency and signal phasing, and an optimized signal-frequency decomposition method known as continuous wavelet transform (CWT) that allows signal analysis in time-frequency domain. In addition, a planar covariation law (PCL), which is an intersegmental co-ordination kinematic law that relates lower limb angles during walking, is explored for trip gaits for the first time. A quantification of trip perturbation as a function of perturbation duration was developed to provide an insight about trip evolution and severity. The developed rapid trip detection algorithms integrated and combined with assistive technologies have the potential to serve as an enabling tools of future trip-and-fall prevention strategies.

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