Introduction. Optimized tibial tray rotation during a total knee replacement (TKR) is critical for tibiofemoral congruency through full range of motion, as it affects soft tissue tension, stability and patellar tracking. Surgeons commonly reference the tibial tubercle, or the “floating tibial tray,” while testing the knee in flexion and extension. Utilization of embedded sensors may enable the surgeon to more accurately assess tibiofemoral contact points during surgery. Methods. The malrotation of the tibiofemoral congruency when utilizing the mid to medial 1/3 of the tibial tubercle for tibial rotation was evaluated in 50 posterior cruciate ligament-retaining TKRs performed by an experienced, high-volume surgeon. Sensors were embedded in the tibial trials; the rotation of the tibial tray was defined, and the femoral contact points in each compartment were captured. The surgical procedure was performed to size and then appropriately rotate the tibial tray. The anterior medial tray was pinned to control anterior-posterior and medio-lateral displacement, and allow internal and external rotation of the tray. With the capsule closed and patella reduced, the knee was reduced with trial implants. The femoral contact points and medial-lateral soft tissue tension were documented. Patellar tracking and changes in soft tissue tension were also documented. Results. In 60% (n = 30/50) of cases, further external rotation (average 5 degrees) was required. No further rotation was required in 10% (n = 5/50), and 30% (n = 15/50) required further internal rotation for optimized congruency. Patellar tracking and changes in soft tissue tension based on rotation showed parallel center of load in medio-lateral compartments and equalized intercompartment pressures resulting in optimized balance of the knee. Conclusions. Utilizing the tibial tubercle for optimized tibial tray rotation and femoral congruency was only adequate in 10% of cases. The use of sensors to define the femoral contact points on the tibia enabled the surgeon to adjust the tibial tray to optimize tibiofemoral congruency. Mal-rotated trays negatively affected soft tissue tension and patellar tracking

Introduction. Knowledge of knee kinetics and kinematics contributes to our understanding of the patho-mechanics of knee pathology and rehabilitation and a mobile system for use in the clinic is desirable. We set out to assess validity and reliability of ambulatory Inertial Motion Unit (IMU) Sensors (Pegasus¯) against an established optoelectronic system (CODA¯). Pegasus¯ uses inertial sensors placed on subjects' thighs and lower leg segments to directly measure orientation of these segments with respect to gravity. CODA¯) models the position of joint centres based on tracked positions of optical markers placed on a subject, providing 3D kinematics of the subject's hips, knees and ankles in all three planes. Methods. Intra observer reliability of the Pegasus¯ system was tested on 6 volunteers (4 male; 2 female) with no previous lower limb or knee pathology. IMU's were placed on the long axis of the lateral aspects of both thighs and lower leg segments. A test re-test protocol was used with sagittal data angle collected around a standard circuit. Inter-observer reliability was tested by placement of IMU's by 5 different testers on a single volunteer. To test validity, we collected simultaneous sagittal knee angle data from Pegasus¯ and CODA¯ in two subjects. The presence of IMU's did not compromise positioning of optical markers. Results. Analysis of triplicate measurements showed that intra-observer error is +/− 5°. Inter-observer difference in measurements varied from 3° to 20° absolute values. Positional error of the Pegasus¯ IMU's was significant in comparison to CODA¯, with absolute offsets in knee angles typically of 10° to 25°. Range of motion differences between the two systems calculated as root mean square (rms) difference of the zero meaned signals were 3.8°-4.8°. Conclusion. The Pegasus¯ system is useful in ambulatory measurement of the range of knee motion in the sagittal plane. In the current configuration there was poor intra and inter-observer reliability possibly related to positional error using the Pegasus¯ system and may be due to fixation method, operator factors, body shape and variability of clothing. Recommendations have been made to the manufacturer

Wearable inertial sensors can detect abnormal gait associated with knee or hip osteoarthritis (OA). However, few studies have compared sensor-derived gait parameters between patients with hip and knee OA or evaluated the efficacy of sensors suitable for remote monitoring in distinguishing between the two. Hence, our study seeks to examine the differences in accelerations captured by low-frequency wearable sensors in patients with knee and hip OA and classify their gait patterns.

We included patients with unilateral hip and knee OA. Gait analysis was conducted using an accelerometer ipsilateral with the affected joint on the lateral distal thighs. Statistical parametric mapping (SPM) was used to compare acceleration signals. The k-Nearest Neighbor (k-NN) algorithm was trained on 80% of the signals' Fourier coefficients and validated on the remaining 20% using 10-fold cross-validation to classify the gait patterns into hip and knee OA.

We included 42 hip OA patients (19 females, age 70 [63–78], BMI of 28.3 [24.8–30.9]) and 59 knee OA patients (31 females, age 68 [62–74], BMI of 29.7 [26.3–32.6]). The SPM results indicated that one cluster (12–20%) along the vertical axis had accelerations exceeding the critical threshold of 2.956 (p=0.024). For the anteroposterior axis, three clusters were observed exceeding the threshold of 3.031 at 5–19% (p = 0.0001), 39–54% (p=0.00005), and 88–96% (p = 0.01). Regarding the mediolateral axis, four clusters were identified exceeding the threshold of 2.875 at 0–9% (p = 0.02), 14–20% (p=0.04), 28–68% (p < 0.00001), and 84–100% (p = 0.004). The k-NN model achieved an AUC of 0.79, an accuracy of 80%, and a precision of 85%.

In conclusion, the Fourier coefficients of the signals recorded by wearable sensors can effectively discriminate the gait patterns of knee and hip OA. In addition, the most remarkable differences in the time domain were observed along the mediolateral axis.

Physiotherapy is a critical element in successful conservative management of low back pain (LBP). The aim of this study was to develop and evaluate a system with wearable inertial sensors to objectively detect sitting postures and performance of unsupervised exercises containing movement in multiple planes (flexion, extension, rotation).

A set of 8 inertial sensors were placed on 19 healthy adult subjects. Data was acquired as they performed 7 McKenzie low-back exercises and 3 sitting posture positions. This data was used to train two models (Random Forest (RF) and XGBoost (XGB)) using engineered time series features. In addition, a convolutional neural network (CNN) was trained directly on the time series data. A feature importance analysis was performed to identify sensor locations and channels that contributed most to the models. Finally, a subset of sensor locations and channels was included in a hyperparameter grid search to identify the optimal sensor configuration and the best performing algorithm(s) for exercise classification. Models were evaluated using F1-score in a 10-fold cross validation approach.

The optimal hardware configuration was identified as a 3-sensor setup using lower back, left thigh, and right ankle sensors with acceleration, gyroscope, and magnetometer channels. The XBG model achieved the highest exercise (F1=0.94±0.03) and posture (F1=0.90±0.11) classification scores. The CNN achieved similar results with the same sensor locations, using only the accelerometer and gyroscope channels for exercise classification (F1=0.94±0.02) and the accelerometer channel alone for posture classification (F1=0.91±0.03).

This study demonstrates the potential of a 3-sensor lower body wearable solution (e.g. smart pants) that can identify proper sitting postures and exercises in multiple planes, suitable for low back pain. This technology has the potential to improve the effectiveness of LBP rehabilitation by facilitating quantitative feedback, early problem diagnosis, and possible remote monitoring.

Patient-reported outcome measures (PROMs) have failed to highlight differences in function or outcome when comparing knee replacement designs and implantation techniques. Ankle-worn inertial measurement units (IMUs) can be used to remotely measure and monitor the bi-lateral impact load of patients, augmenting traditional PROMs with objective data. The aim of this study was to compare IMU-based impact loads with PROMs in patients who had undergone conventional total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), and robotic-assisted TKA (RA-TKA).

77 patients undergoing primary knee arthroplasty (29 RA-TKA, 37 TKA, and 11 UKA) for osteoarthritis were prospectively enrolled. Remote patient monitoring was performed pre-operatively, then weekly from post-operative weeks two to six using ankle-worn IMUs and PROMs. IMU-based outcomes included: cumulative impact load, bone stimulus, and impact load asymmetry. PROMs scores included: Oxford Knee Score (OKS), EuroQol Five-dimension with EuroQol visual analogue scale, and the Forgotten Joint Score.

On average, patients showed improved impact load asymmetry by 67% (p=0.001), bone stimulus by 41% (p<0.001), and cumulative impact load by 121% (p=0.035) between post-operative week two and six. Differences in IMU-based outcomes were observed in the initial six weeks post-operatively between surgical procedures. The mean change scores for OKS were 7.5 (RA-TKA), 11.4 (TKA), and 11.2 (UKA) over the early post-operative period (p=0.144). Improvements in OKS were consistent with IMU outcomes in the RA-TKA group, however, conventional TKA and UKA groups did not reflect the same trend in improvement as OKS, demonstrating a functional decline.

Our data illustrate that PROMs do not necessarily align with patient function, with some patients reporting good PROMs, yet show a decline in cumulative impact load or load asymmetry. These data also provide evidence for a difference in the functional outcome of TKA and UKA patients that might be overlooked by using PROMs alone.

Thermal sensors have been used in bracing research as self-reported diaries are inaccurate. Little is known about new low-profile sensors, optimal location within a brace, locational thermal micro-climate and effect of brace lining. Our objective is to Determine an optimal temperature threshold for sensor-measured and true wear time agreement. Identify optimal sensor location. Assess all factors to determine the best sensor option for the Bracing AdoleScent Idiopathic Scoliosis (BASIS) multicentre RCT.

Seven Orthotimer and five iButton (DS1925L) sensors were synchronised to record temperature at five-minute intervals. Three healthy participants donned a rigid spinal brace, embedded with both sensors across four anatomical locations (abdomen/axilla/lateral-gluteal/sacral). Universal-coordinated-time wear protocols were performed in/out-doors. Intraclass correlation coefficient (ICC) assessed sensor-measured and true wear time agreement at thresholds 15–36oC.

Optimal thresholds, determined by largest ICC estimate: Orthotimer: Abdomen=26oC, axilla=27oC, lateral-gluteal=24.5oC, sacral=22.5oC. iButton: Abdomen=26oC, axilla=27oC, lateral-gluteal=23.5oC, sacral=23.5oC. Warm-up time and error at optimal thresholds increased for moulded sensors covered with 6mm lining.

Location: anterior abdominal wall. Excellent reliability and higher optimal thresholds, less likely to be exceeded by ambient temperature; not a pressure area. Sensor: iButton, longer battery life and larger memory than Orthotimer; allows recording at 10 min intervals for life of brace. Orthotimer only able to record every 30 mins, increasing error between true and measured wear time; Orthotimer needs 6-monthly data download. Threshold: 26oC is optimal threshold to balance warm-up and cool-down times for accurately measuring wear time. Sensor should not be covered by lining foam as this significantly prolongs warm-up time.

Introduction

The ability to walk over various surfaces such as cobblestones, slopes or stairs is a very patient centric and clinically meaningful mobility outcome. Current wearable sensors only measure step counts or walking speed regardless of such context relevant for assessing gait function. This study aims to improve deep learning (DL) models to classify surfaces of walking by altering and comparing model features and sensor configurations.

Introduction

Transosseous flexion-distraction injuries of the spine typically require surgical intervention by stabilizing the fractured vertebra during healing with a pedicle-screw-rod constructs. As healing is taking place the load shifts from the implant back to the spine. Monitoring the load-induced deflection of the rods over time would allow quantifiable postoperative assessment of healing progress without the need for radiation exposure or frequent hospital visits. This approach, previously demonstrated to be effective in assessing fracture healing in long bones and monitoring posterolateral spinal fusion in sheep, is now being investigated for its potential in evaluating lumbar vertebra transosseous fracture healing.

Gait measurements can vary due to various intrinsic and extrinsic factors, and this variability becomes more pronounced using inertial sensors in a free-living environment. Therefore, identifying and quantifying the sources of variability is essential to ensure measurement reliability and maintain data quality.

This study aimed to determine the variability of daily accelerations recorded by an inertial sensor in a group of healthy individuals. Ten participants, four males and six females, with a mean age of 50 years (range: 29–61) and BMI of 26.9 kg/m² (range: 21.4–36.8), were included. A single accelerometer continuously recorded lower limb accelerations over two weeks. We extracted and analyzed the accelerations of three consecutive strides within walking bouts if the time difference between the bouts was more than two hours. Multivariate mixed-effects modeling was performed on both the discretized acceleration waveforms at 101 points (0–100) and the harmonics of the signals in the frequency domain to determine the variance components for different subjects, days, bouts, and steps as the random effect variables. Intraclass correlation coefficients (ICCs) were calculated for between-day, between-bout, and between-step comparisons.

The results showed that the ICCs for the between-day, between-bout, and between-step comparisons were 0.73, 0.82, 0.99 for the vertical axis; 0.64, 0.75, 0.99 for the anteroposterior axis; and 0.55, 0.96, 0.97 for the mediolateral axis. For the signal harmonics, the respective ICCs were 0.98, 0.98, 0.99 for the vertical axis; 0.54, 0.93, 0.98 for the anteroposterior axis; and 0.69, 0.78, 0.95 for the mediolateral axis.

Overall, this study demonstrated that accelerations recorded continuously for multiple days in a free-living environment exhibit high variability, mainly between days, and some variability arising from differences between walking bouts during different times within days. However, reliable and repeatable gait measurements can be obtained by identifying and quantifying the sources of variability.

Introduction

Wearable sensors are promising tools for fast clinical gait evaluations in individuals with osteoarthritis (OA) of the knee and hip. However, gait assessments with wearable sensor are often limited to relatively simple straight-ahead walking paradigms. Parameters reflecting more complex and relevant aspects of gait, including dual-tasking, turning, and compensatory upper body motion are often overlooked in literature. The aim of this study was to investigate turning, dual-task performance, and upper body motion in individuals with knee or hip OA in addition to spatiotemporal gait parameters, taking shared covariance between gait parameters into account.

Participation in a physical therapy program is considered one of the greatest predictors for successful conservative management of common shoulder disorders, however, adherence to standard exercise protocols is often poor (around 50%) and typically worse for unsupervised home exercise programs. Currently, there are limited tools available for objective measurement of adherence and performance of shoulder rehabilitation in the home setting. The goal of this study was to develop and evaluate the potential for performing home shoulder physiotherapy monitoring using a commercial smartwatch. We hypothesize that shoulder physiotherapy exercises can be classified by analyzing the temporal sequence of inertial sensor outputs from a smartwatch worn on the extremity performing the exercise.

Twenty healthy adult subjects with no prior shoulder disorders performed seven exercises from a standard evidence-based rotator cuff physiotherapy protocol: pendulum, abduction, forward elevation, internal/external rotation and trapezius extension with a resistance band, and a weighted bent-over row. Each participant performed 20 repetitions of each exercise bilaterally under the supervision of an orthopaedic surgeon, while 6-axis inertial sensor data was collected at 50 Hz from an Apple Watch. Using the scikit-learn and keras platforms, four supervised learning algorithms were trained to classify the exercises: k-nearest neighbour (k-NN), random forest (RF), support vector machine classifier (SVC), and a deep convolutional recurrent neural network (CRNN). Algorithm performance was evaluated using 5-fold cross-validation stratified first temporally and then by subject.

Categorical classification accuracy was above 94% for all algorithms on the temporally stratified cross validation, with the best performance achieved by the CRNN algorithm (99.4± 0.2%). The subject stratified cross validation, which evaluated classifier performance on unseen subjects, yielded lower accuracies scores again with CRNN performing best (88.9 ± 1.6%).

This proof-of concept study demonstrates the feasibility of a smartwatch device and machine learning approach to more easily monitor and assess the at-home adherence of shoulder physiotherapy exercise protocols. Future work will focus on translation of this technology to the clinical setting and evaluating exercise classification in shoulder disorder populations.

Immune response in periprosthetic joint infection (PJI) is diverse. Resident macrophage and/or wandering monocyte are superb guardians to sense microbial attacks, take invaders and alarm the danger. Neutrophils are refined but momentary fighters to kill microbes with projectile weapons as well as predation. The swift action is usually effective at the forefront to prevent expansion of infectious foci. However, such characteristics often evokes overshooting via self-defeating of pus, thus leading to crucial soft tissue damage in the acute phase. Intervention of monocyte/macrophages follow and act as wise organizers. In addition, stromal fibroblasts also act in front for host defence. They equip innate immune sensors (TLRs, NLRs), which can sense dangers and trigger off inflammatory response, but also is usually self-regulated. These sensors not only interact each other, but also have possible contribution to selective autophagy (xenophagy and lysophagy) in PJI. In this presentation, overview of pathology in PJI will be summarized with a special attention to innate immune sensors (TLRs and NLRs), and selective autophagy.

3D measurement of joint angles so far has only been possible using marker-based movement analysis, and therefore has not been applied in (larger scale) clinical practice (performance test) and even less so in the free field (activity monitoring). 3D joint angles could provide useful additional information in assessing the risk of anterior cruciate ligament injury using a vertical drop jump or in assessing knee range of motion after total knee arthroplasty. We developed a tool to measure dynamic 3D joint angles using 6 inertial sensors, attached to left and right shank, thigh and pelvis. The same sensors have been used for activity identification in a previous study. To validate the setup in a pilot study, we measured 3D knee and hip angles using the sensors and a Vicon movement lab simultaneously in 3 subjects. Subjects performed drop jumps, squats and ran on the spot. The mean error between Vicon and sensor measurement for the maximum joint angles was 3, 7 and 8 degrees for knee flexion, ad/abduction and rotation respectively, and 9, 7 and 10 degrees for hip flexion, ad/abduction and rotation respectively. No calibration movements were required. A major part of the inaccuracy was caused by soft tissue effects and can partly be resolved by improved sensor attachment. These pilot results show that it is feasible to measure 3D joint angles continuously using unobtrusive light-weight sensors. No movement lab is necessary and therefore the measurements can be done in a free field setting, e.g. at home or during training at a sport club. A more extensive validation study will be performed in the near future.

Objectives

Implant loosening is the most common reason for revision of total or partial knee replacement, but the patient complains of pain-not a loose implant. It would be a useful diagnostic tool to interrogate the implant to ascertain whether it remains well fixed or not, thus either confirming or eliminating this mode of failure. For such technology to be adopted by manufacturers, it must be extremely low cost and simple to build into an implant. We aim to develop a sensor that meets these requirements and, when embedded in an implant, can provide information on its fixation to the underlying bone. We have previously proven that, through impedance analysis of passive piezoelectric sensors, it is possible for such sensors to determine the cured state of cement with good correlation (0.7) to a surgeon's judgement (Darton et al, 2014). In this study we now look at how the impedance trances of the sensors can be interpreted to distinguish between tibial trays that are securely cemented in sawbone blocks and those with no cement in loose fitting sawbone blocks.

The Pivot-shift phenomenon (PS) is known to be one of the essential signs of functional insufficiency of the anterior cruciate ligament (ACL). To evaluate the dynamic knee laxity is very important to accurately diagnose ACL injury, to assess surgical reconstructive techniques, and to evaluate treatment approaches. However, the pivot-shift test remains a subjective clinical examination difficult to quantify. The aim of the present study is to validate the use of an innovative non-invasive device based on the use of an inertial sensor to quantify PS test. The validation was based on comparison with data acquired by a surgical navigation system.

The surgeon intraoperatively performed the PS tests on 15 patients just before fixing the graft required for the ACL reconstruction. A single accelerometer and a navigation system simultaneously acquired the joint kinematics. An additional optical tracker set to the accelerometer has allowed to quantify the movement of the sensor. The tibial anteroposterior acceleration obtained with the navigation system was compared with the acceleration acquired by the accelerometer. It is therefore estimated the presence of any artifacts due to the soft tissue as the test-retest repositioning error in the positioning of the sensor. It was also examined, the repeatability of the acceleration parameters necessary for the diagnosis of a possible ACL lesion and the waveform of the output signal obtained during the test. Finally it has been evaluated the correlation between the two acceleration measurements obtained by the two sensors.

The RMS (root mean square) of the error of test-retest positioning has reported a good value of 5.5 ± 2.9 mm. While the amounts related to the presence of soft tissue artifacts was equal to 4.9 ± 2.6 mm. It was also given a good intra-tester repeatability (Cronbach's alpha = 0.86). The inter-patient similarity analysis showed a high correlation in the acceleration waveform of 0.88 ± 0.14. Finally the measurements obtained between the two systems showed a good correlation (rs = 0.72, p<0.05).

This study showed good reliability of the proposed scheme and a good correlation with the results of the navigation system. The proposed device is therefore to be considered a valid method for evaluating dynamic joint laxity.

Introduction & Aims

The traditional method of soft-tissue balancing during TKA is subjective in nature, and stiffness and instability are common indications for revision, suggesting that TKA balancing by subjective assessment is suboptimal. This study examines the intraoperative mediolateral loads measured with a nanosensor-enabled tibial insert trial and the sequential balancing steps used to achieve quantitative balance. Data obtained from a prospective multicenter study was assessed to determine the effect of targeted ligament release on intra-articular loading, and to understand which types of releases are necessary to achieve quantified ligament balance.

Whilst home-based exercise rehabilitation plays a key role in determining patient outcomes following orthopaedic intervention (e.g. total knee replacement), it is very challenging for clinicians to objectively monitor patient progress, attribute functional improvement (or lack of) to adherence/non-adherence and ultimately prescribe personalised interventions. This research aimed to identify whether 4 knee rehabilitation exercises could be objectively distinguished from each other using lower body inertial measurement units (IMUs) and principle components analysis (PCA) in the hope to facilitate objective home monitoring of exercise rehabilitation.

5 healthy participants performed 4 repetitions of 4 exercises (knee flexion in sitting, knee extension, single leg step down and sit to stand) whilst wearing lower body IMU sensors (Xsens, Holland; sampling at 60 Hz). Anthropometric measurements and a static calibration were combined to create the biomechanical model, with 3D hip, knee and ankle angles computed using the Euler sequence ZXY. PCA was performed on time normalised (101 points) 3D joint angle data which reduced all joint angle waveforms into new uncorrelated PCs via an orthogonal transformation. Scatterplots of PC1 versus PC2 were used to visually inspect for clustering between the PC values for the 4 exercises. A one-way ANOVA was performed on the first 3 PC values for the 9 variables under analysis. Games-Howell post hoc tests identified variables that were significantly different between exercises.

All exercises were clearly distinguishable using the PC scatterplot representing hip flexion-extension waveforms. ANOVA results revealed that PC1 for the knee flexion angle waveform was the only PC value statistically different across all exercises.

Findings demonstrate clear potential to objectively distinguish between different knee rehabilitation exercises using IMU sensors and PCA. Flexion-extension angles at the hip and knee appear most suited for accurate separation, which will be further investigated on patient data and additional exercises.

Smart instrumentation targets optimal joint hardware installation. Intelligent implants target the chronic assessment of joint health and hardware condition.

Intelligent implants would facilitate the collection of data, closing the loop to drive best surgical practice, joint system design, and the improvement of outcomes. Intelligent devices could assist post-op in managing pain and promoting recovery. Intelligent implants could offer opportunity for early detection and less invasive intervention should problems arise acutely, or even long after implant.

While the development of smart instrumentation is tactically important, the development of intelligent implants is vital to the improvement of outcomes, and should be central to the strategic vision for orthopedic technology development.

One out of every five total knee arthroplasty (TKA) recipients is unhappy with the outcome of their surgery. As the number of TKA candidates continues to increase, so, too, will the dissatisfied patient population. These statistics should not be acceptable to the surgeons, hospitals, and patients implicated in this elective procedure. There are many contributing factors to patient dissatisfaction, paramount among them being post-operative levels of functionality and pain. Therefore, in an attempt to increase function and decrease pain levels through soft-tissue management, sensor-assisted TKA outcomes were compared with manual TKA outcomes.

One hundred and fourteen primary TKA patients were evaluated: 57 sensor-assisted TKA patients; 57 manual TKA patients. All procedures were performed by the same surgeon. In order to reduce confounding variables, all patients were matched for: age, gender distribution, BMI, marital status, smoking proclivity, pre-operative ROM, pre-operative alignment, and employment status. Outcomes scores were captured pre-operatively, and at the 6-month interval, including Knee Society Score metrics and the Oxford score, as well as 6-month ROM. The sensor device used in this analysis is inserted into the tibial component, during the trialing, and displays loading values in the medial and lateral compartments (lbf.), and also displays the medial and lateral center of load location. In the sensor-assisted TKA group, balance was achieved for all patients, as previously described in literature.

There was a statistically significant rate of improvement, for all outcomes measures, in the sensor-assisted TKA group when compared with the manual group (Figure 1). In addition to rate of improvement, there was also a significant trend towards a significance in ROM in the sensor-assisted group, as a stand-alone dependent variable (P = 0.002).

By the 6-month follow-up interval, patients in receipt of a sensor-assisted TKA reported greater improvement in function and less pain than the patients in the manual TKA group. This data suggests that soft-tissue balance may contribute to faster recovery, as reported by the patient. Because pain and function play an integral role in patient satisfaction, further follow-up might yield higher satisfaction in the sensor-assisted patient group, which is consistent with previously published observations.

Ligament balancing can be difficult to perfect in total knee arthoplasty (TKA), where current surgical practice is subjective and highly dependent on the individual surgeon. Proper ligament balancing contributes to postoperative stability, prosthetic alignment, and proprioception. Conversely, imbalance is linked to increased wear rates of the polyethylene component within the implant and, in turn, early surgical revision. With the end goal of quantification of joint compartmental pressures, pressure sensor arrays have been designed to quantify contact stresses within the knee during TKA.

Flexible, capacitive pressure sensors are designed as simple parallel plates, enabling a robust solid state design. Modification of cleanroom microfabrication processes enable realization of these arrays on polyimide (common in microdevices), and polyethylene (common in joint replacements). Readout circuitry implements an Analog Devices capacitance to digital chip and output is compared to direct LCR meter data. Testing verifies the highly linear response of the sensors with applied normal loads corresponding to pressure magnitudes present in passive (intraoperative) knee flexion. Spatial resolution of the arrays is 0.5 mm, with a critical dimension of 25 micrometers, allowing the magnitude and location of forces to be accurately recorded.

The MEMS pressure sensors are mounted on a tibial trial, with the body of the trial housing all circuitry. The sensors are read sequentially, and the data undergoes analog to digital conversion prior to wireless data transmission at 2.4 GHz. An Instron machine is used for compressive loading for laboratory calibration and testing. This paper outlines device fabrication, readout circuit implementation, and preliminary results.