Estimation of Cruciate Ligament Forces Via Smart Compression Garments

Belbasis, Aaron; Fuss, Franz Konstantin; Sidhu, Jesper

doi:10.1016/j.proeng.2015.07.194

Cited by 14 publications

(16 citation statements)

References 6 publications

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“…All studies reporting PCL load data during walking used modelling techniques, but with highly variable results (Table 4) [37, 44, 77, 78, 100–104]. While some investigators calculated almost no tension in the PCL (ranging from 0 to 27N) [37, 44, 104], others found relatively high maximum PCL forces (0.2–1 times body-weight; BW) [77, 78, 100–102].…”

Section: Resultsmentioning

confidence: 99%

“…While some investigators calculated almost no tension in the PCL (ranging from 0 to 27N) [37, 44, 104], others found relatively high maximum PCL forces (0.2–1 times body-weight; BW) [77, 78, 100–102]. The time-point at which the peak PCL force occurred was also inconsistent between different studies and varied from early stance to late swing phase.…”

Section: Resultsmentioning

confidence: 99%

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Loading Patterns of the Posterior Cruciate Ligament in the Healthy Knee: A Systematic Review

et al. 2016

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BackgroundThe posterior cruciate ligament (PCL) is the strongest ligament of the knee, serving as one of the major passive stabilizers of the tibio-femoral joint. However, despite a number of experimental and modelling approaches to understand the kinematics and kinetics of the ligament, the normal loading conditions of the PCL and its functional bundles are still controversially discussed.ObjectivesThis study aimed to generate science-based evidence for understanding the functional loading of the PCL, including the anterolateral and posteromedial bundles, in the healthy knee joint through systematic review and statistical analysis of the literature.Data sourcesMEDLINE, EMBASE and CENTRALEligibility criteria for selecting studiesDatabases were searched for articles containing any numerical strain or force data on the healthy PCL and its functional bundles. Studied activities were as follows: passive flexion, flexion under 100N and 134N posterior tibial load, walking, stair ascent and descent, body-weight squatting and forward lunge.MethodStatistical analysis was performed on the reported load data, which was weighted according to the number of knees tested to extract average strain and force trends of the PCL and identify deviations from the norms.ResultsFrom the 3577 articles retrieved by the initial electronic search, only 66 met all inclusion criteria. The results obtained by aggregating data reported in the eligible studies indicate that the loading patterns of the PCL vary with activity type, knee flexion angle, but importantly also the technique used for assessment. Moreover, different fibres of the PCL exhibit different strain patterns during knee flexion, with higher strain magnitudes reported in the anterolateral bundle. While during passive flexion the posteromedial bundle is either lax or very slightly elongated, it experiences higher strain levels during forward lunge and has a synergetic relationship with the anterolateral bundle. The strain patterns obtained for virtual fibres that connect the origin and insertion of the bundles in a straight line show similar trends to those of the real bundles but with different magnitudes.ConclusionThis review represents what is now the best available understanding of the biomechanics of the PCL, and may help to improve programs for injury prevention, diagnosis methods as well as reconstruction and rehabilitation techniques.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Loading Patterns of the Posterior Cruciate Ligament in the Healthy Knee: A Systematic Review

et al. 2016

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“…A threshold of 200 N was assumed as the minimum strength required for a BASHTI fixation to simulate the daily activities and passive motions applied to the knee at the time of rehabilitation. 18,28,31,32 The experiments showed that the failure mode was a strong function of the tendon diameter. It was observed that excessive TC might damage the tendon fibers, and consequently, it could result in the tendon failure.…”

Section: Failure Modementioning

confidence: 99%

Effect of Geometry on the Fixation Strength of Anterior Cruciate Ligament Reconstruction Using BASHTI Technique

et al. 2020

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The goal of this study is to investigate the effects of tendon and cannulated drill bit diameter on the strength of the bone and site hold tendon inside (BASHTI) fixation technique for an anterior cruciate ligament (ACL) reconstruction. Bovine digital tendons and Sawbones blocks were used to mimic the ACL reconstruction. Mechanical strength of the specimens was measured using a cyclic loading continued by a single cycle pullout load until failure to simulate the real postsurgical loading conditions. Finally, failure modes of specimens and ultimate failure load were recorded. The maximum possible tendon surface strain (i.e., tendon compression [TC]) for tendon diameters of 6, 7, 8, and 9 mm were 0.73, 0.8, 0.7, and 0.65, respectively. Eighty per cent of the specimens with tendon diameter of 6 mm and 20% of specimens with tendon diameter of 7 mm failed on the torn tendon. All samples with larger tendon diameters (i.e., 8 and 9 mm) failed on the fixation slippage. The maximum fixation strength according to the most suitable core bones for 6, 7, 8, and 9 mm tendons were 148 ± 47 N (core 9.5 mm), 258 ± 66 N (core 9.5 mm), 386 ± 128 N (core 8.5 mm), and 348 ± 146 N (core 8.5 mm), respectively. The mode of tendon failure was significantly influenced by the tendon diameter. Also, an increase in TC raised the fixation strength for all tendon diameters; however, tendon over compression decreased the fixation strength for the 8 mm tendon group. Finally, an empirical equation was proposed to predict BASHTI fixation strength.

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“…The most common sensors used for FMG purposes are off-the-shelf FSR (force sensing resistive) sensors, either as single sensors, several sensors ( Connan et al, 2016 ) or sensor matrix arrays ( Zhou et al, 2017 ), that are preloaded, compressed either by tight fitting garments or by elastic bands to the surface of the relevant muscles ( Lukowicz et al, 2006 ; McLaren et al, 2010 ; Zhou et al, 2017 ), Velcro bracelets ( Connan et al, 2016 ), integrated in a textile sleeve ( Ogris et al, 2007 ), equipped with mechanical preload adjustments ( Li et al, 2012 ), or placed inside a forearm orthosis ( Wininger et al, 2008 ). Belbasis and Fuss (2015) and Belbasis et al (2015a , b) used several customized piezoresistive polymer sensors sandwiched between compression garment and skin. Meyer et al (2006) applied a capacitance pressure sensor array embedded in textiles.…”

Section: Introductionmentioning

confidence: 99%

Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG

Belbasis

Fuss

2018

Front. Physiol.

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Muscle activity and fatigue performance parameters were obtained and compared between both a smart compression garment and the gold-standard, a surface electromyography (EMG) system during high-speed cycling in seven participants. The smart compression garment, based on force myography (FMG), comprised of integrated pressure sensors that were sandwiched between skin and garment, located on five thigh muscles. The muscle activity was assessed by means of crank cycle diagrams (polar plots) that displayed the muscle activity relative to the crank cycle. The fatigue was assessed by means of the median frequency of the power spectrum of the EMG signal; the fractal dimension (FD) of the EMG signal; and the FD of the pressure signal. The smart compression garment returned performance parameters (muscle activity and fatigue) comparable to the surface EMG. The major differences were that the EMG measured the electrical activity, whereas the pressure sensor measured the mechanical activity. As such, there was a phase shift between electrical and mechanical signals, with the electrical signals preceding the mechanical counterparts in most cases. This is specifically pronounced in high-speed cycling. The fatigue trend over the duration of the cycling exercise was clearly reflected in the fatigue parameters (FDs and median frequency) obtained from pressure and EMG signals. The fatigue parameter of the pressure signal (FD) showed a higher time dependency (R2 = 0.84) compared to the EMG signal. This reflects that the pressure signal puts more emphasis on the fatigue as a function of time rather than on the origin of fatigue (e.g., peripheral or central fatigue). In light of the high-speed activity results, caution should be exerted when using data obtained from EMG for biomechanical models. In contrast to EMG data, activity data obtained from FMG are considered more appropriate and accurate as an input for biomechanical modeling as they truly reflect the mechanical muscle activity. In summary, the smart compression garment based on FMG is a valid alternative to EMG-garments and provides more accurate results at high-speed activity (avoiding the electro-mechanical delay), as well as clearly measures the progress of muscle fatigue over time.

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Estimation of Cruciate Ligament Forces Via Smart Compression Garments

Cited by 14 publications

References 6 publications

Loading Patterns of the Posterior Cruciate Ligament in the Healthy Knee: A Systematic Review

Loading Patterns of the Posterior Cruciate Ligament in the Healthy Knee: A Systematic Review

Effect of Geometry on the Fixation Strength of Anterior Cruciate Ligament Reconstruction Using BASHTI Technique

Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG

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