Joint motion quality in vibroacoustic signal analysis for patients with patellofemoral joint disorders

Abstract

BackgroundChondromalacia, lateral patellar compression syndrome and osteoarthritis are common patellofemoral joint disorders leading to functional and/or structural disturbances in articular surfaces. The objective of the study was to evaluate their impact on joint motion quality via the vibroacoustic signal generated during joint movement analysis.MethodsSeventy-three patients (30 with chondromalacia, 21 with lateral patellar compression syndrome, and 22 with osteoarthritis) and 32 healthy controls were tested during flexion/extension knee motion for vibroacoustic signals using an acceleration sensor. Estimated parameters: variation of mean square (VMS), difference between mean of four maximum and mean of four minimum values (R4), power spectral density for frequency of 50–250 Hz (P1) and 250–450 Hz (P2) were analyzed.ResultsVibroacoustic signals recorded for particular disorders were characterized by significantly higher values of parameters in comparison to the control group. Moreover, differences were found among the various types of patellofemoral joint disturbances. Chondromalacia and osteoarthritis groups showed differences in all parameters examined. In addition, osteoarthritis patients exhibited differences in VMS, P1 and P2 values in comparison to lateral patellar compression syndrome patients. However, only the value of R4 was found to differ between knees with lateral patellar compression syndrome and those with chondromalacia.ConclusionOur results suggest that particular disorders are characterized by specific vibroacoustic patterns of waveforms as well as values of analyzed parameters.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2474-15-426) contains supplementary material, which is available to authorized users.

Joint Motion Quality in Chondromalacia Progression Assessed by Vibroacoustic Signal Analysis

“…VAG is a technique based on accurate recording of vibroacoustic signals generated by relative movement of articular cartilage surfaces. It is known that cartilage pathology may change vibroacoustic signals, and differences between normal and abnormal joints are observed [14‐17]. This phenomenon also is detected for CMP, which generates the most prominent vibrations [14,18].…”

“…Hence, the authors suggest a role for more sensitive and responsive diagnostic techniques in the assessment of mild and moderate disorders. One of these methods for direct analysis of crepitations could be vibroarthrography (VAG), which seems to support physical examination for objective assessment of quality of joint motion [13,14].…”

Evaluating physical function and activity in the elderly patient using wearable motion sensors

“…34,35 In addition, wearable motion sensors have also been used to assess joint stability tests such as assessment of ACL insufficiency by measuring rotational rate and peak accelerations with an IMU applied in the Pivot Shift test. 36 Furthermore, IMUs have been applied to measure shoulder joint function before and after orthopaedic interventions.…”

Vibroarthrographic analysis of patellofemoral joint arthrokinematics during squats with increasing external loads

“…Both variability (VMS) and amplitude (R4) of representative signals are characterized by low values. Especially the low values of the R4 parameter that indicate a lack of repetitive large peaks, whose presence often corresponds with macroscopic joint cartilage abnormalities or signi cant maltracking of articular surfaces [18,37]. In turn, low spectral activity analyzed by summing spectral power of the VAG signal in two bands: 50-250 Hz (P1) and 250-450 Hz (P2) seems to be related with intact integrity of the super cial layer of hyaline cartilage and an e cient lubrication mechanism [36,44].…”

“…The short-time spectra were obtained by computing the discrete Fourier transform of segments, 150 samples each, Hanning window, and 100 samples overlap of each segment. The spectral activity was analyzed by summing spectral power of the VAG signal in two bands: 50-250 Hz (P1) and 250-450 Hz (P2) [18,[36][37][38][39]. The performed ANOVA revealed that there was a signi cant main effect of the applied load on values of each analyzed parameter, similar in both lower limbs.…”