Development of non-destructive vibration method for classification of bone fracture severity

Accurate classification of bone fracture severity is critical in orthopedic evaluation. Radiation-based methods such as X-ray, CT, and MRI provide anatomical detail but lack the ability to classify fracture severity. This study presents a non-invasive, vibration-based approach to assessing fracture severity by analyzing dynamic response characteristics. Five goat (metacarpal) bone specimens were examined, including one unfractured bone that served as a reference bone and four with induced fractures in the lateral, longitudinal, and two oblique orientations. Controlled impact excitation was applied using an impact hammer, and acceleration responses were measured using acceleration sensors. Frequency response function (FRF), coherence, and phase shift were calculated using Fast Fourier Transform (FFT) algorithms. Resonance frequency and FRF magnitude served as primary indicators of stiffness loss and damping changes caused by fractures. The reference bone had a resonance frequency of 376 Hz and an FRF magnitude of 12.96 g/N, which was considered the reference parameter. The lateral fracture showed the most severe response, with a 17.98% increase in resonant frequency and a 491% increase in FRF magnitude, indicating significant stiffness redistribution and low damping. Longitudinal and oblique fractures resulted in large resonant frequency reductions of up to 94.4%. The experimental results were obtained using Fast Fourier Transform (FFT) algorithms and Euler-Bernoulli ray theory. These results suggest that vibration analysis is a reliable, quantitative, and non-destructive tool for classifying the severity of bone fractures.

Authors

Institutions

Publication Details

Journal
Applied Computer Science
Published
2026-03-31
DOI
https://doi.org/10.35784/acs_8034
Primary Topic
Bone health and osteoporosis research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development of non-destructive vibration method for classification of bone fracture severity

Nikunj Rachchh, Jignesh Jani
Applied Computer Science
Bone health and osteoporosis research
article

Development of non-destructive vibration method for classification of bone fracture severity

Nikunj Rachchh, Jignesh Jani
article en

Abstract

Accurate classification of bone fracture severity is critical in orthopedic evaluation. Radiation-based methods such as X-ray, CT, and MRI provide anatomical detail but lack the ability to classify fracture severity. This study presents a non-invasive, vibration-based approach to assessing fracture severity by analyzing dynamic response characteristics. Five goat (metacarpal) bone specimens were examined, including one unfractured bone that served as a reference bone and four with induced fractures in the lateral, longitudinal, and two oblique orientations. Controlled impact excitation was applied using an impact hammer, and acceleration responses were measured using acceleration sensors. Frequency response function (FRF), coherence, and phase shift were calculated using Fast Fourier Transform (FFT) algorithms. Resonance frequency and FRF magnitude served as primary indicators of stiffness loss and damping changes caused by fractures. The reference bone had a resonance frequency of 376 Hz and an FRF magnitude of 12.96 g/N, which was considered the reference parameter. The lateral fracture showed the most severe response, with a 17.98% increase in resonant frequency and a 491% increase in FRF magnitude, indicating significant stiffness redistribution and low damping. Longitudinal and oblique fractures resulted in large resonant frequency reductions of up to 94.4%. The experimental results were obtained using Fast Fourier Transform (FFT) algorithms and Euler-Bernoulli ray theory. These results suggest that vibration analysis is a reliable, quantitative, and non-destructive tool for classifying the severity of bone fractures.

Applied Computer ScienceVol. 22(1)
Marwadi University (IN)
Openalex Percentile: Top 32%
Bone health and osteoporosis research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.