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.

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.

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.

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.

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.

Introduction: Physical activity is a major outcome in total hip arthroplasty (THA) and discharge criterion. Increasing immediate post-op activity may accelerate discharge, enable fast track surgery and improve general rehabilitation. Preliminary evidence (O'Halloran P.D. et al. 2015) shows that feedback via motivational interviewing can result in clinically meaningful improvements of physical activity. It was the aim of this study to use wearable sensor activity monitors to provide and study the effect of biofeedback on THA patients' activity levels. It was hypothesized that biofeedback would increase in-hospital and post-discharge activity versus controls.

Methods: In this pilot study, 18 patients with osteoarthritis receiving elective primary THA followed by a rapid recovery protocol with discharge on day 3 after surgery (day 0) were randomized to the feedback group (n=9, M/F: 4:5, age 63.3 ± 5.9 years, BMI 26.9 ± 5.1) or a non-feedback control group (n=9, M/F: 0:9, age 66.9 ± 5.1 years, BMI 27.1 ± 4.0). Physical activity was measured using a wearable sensor and parameters (Time-on-Feet (ToF), steps, sit-stand-transfers (SST), mean cadence (steps/min)) were calculated using a previously validated algorithms (Matlab). For the in-hospital period data was calculated twice daily (am, ca. 8–13:00h and pm, ca. 13–20:00h) of day 1 (D1) and 2 (D2). The feedback group had parameters reported back twice (morning, lunch) using bar charts comparing visually and numerically their values (without motivational instructions) to a previously measured reference group (n=40, age 71 ±7 years, M:F 16:24) of a conventional discharge protocol (day 4/5). Activity measures continued from discharge (D3) until day 5 (D5) at home.

Results: Randomization resulted in matched groups regarding age and BMI, but not gender. The first post-op activity assessment (D1am) was identical between groups. Also thereafter similar values with no significant differences in any parameter were seen, e.g. the time-on-feet at D2PM was 59.2 ±31.7min (feedback) versus 62.9 ±39.2min (controls). Also on the day of discharge and beyond, no effect from the in-hospital feedback was measured. For both groups the course of activity recovery showed a distinct drop on day 4 following a highly active day of discharge (D3). On day 5, activity levels only recovered partially. For both groups, all quantitative activity parameters were significantly higher than the reference values used for feedback. Only cadence as a qualitative measure was the same like reference values.

Discussion: Biofeedback using activity values from a body-worn monitor did not increase in-hospital or immediate post-op home activity levels compared to a control group when using the investigated feedback protocol. In general, while the day of discharge steeply boosts patient activity, the day after at home results in an activity drop to near in-patient levels before discharge. In a fast track surgery protocol, it may be of value to avoid this drop via patient education or home physiotherapy. Biofeedback using activity monitors to increase immediate post-op activity for fast track surgery or improved recovery may only be effective when feedback goals are set higher, are personalised or have additional motivational context.

Introduction

Following primary total knee arthroplasty (TKA), patients experience pain relief and report improved physical function and activity. However, there is paucity of evidence that patients are truly more active in daily life after TKA. The aims of this study were: 1) to prospectively measure physical activity with a wearable motion sensor before and after TKA; 2) to compare patient-reported levels of physical activity with objectively assessed levels of physical activity before and after TKA; 3) to investigate whether differences in physical activity after TKA are related to levels of physical function.

Background

Shoulder pain limits range of motion (ROM) and reduces performing activities of daily living (ADL). Objective assessment of shoulder function could be of interest for diagnosing shoulder pathology or functional assessment of the shoulder after therapy.

The feasibility of 2 wearable inertial sensors for functional assessment to differentiate between healthy subjects and patients with unilateral shoulder pathology is investigated using parameters as asymmetry.

A cavovarus foot deformity was simulated in cadaver specimens by inserting metallic wedges of 15° and 30° dorsally into the first tarsometatarsal joint. Sensors in the ankle joint recorded static tibiotalar pressure distribution at physiological load. The peak pressure increased significantly from neutral alignment to the 30° cavus deformity, and the centre of force migrated medially. The anterior migration of the centre of force was significant for both the 15° (repeated measures analysis of variance (ANOVA), p = 0.021) and the 30° (repeated measures ANOVA, p = 0.007) cavus deformity. Differences in ligament laxity did not influence the peak pressure. These findings support the hypothesis that the cavovarus foot deformity causes an increase in anteromedial ankle joint pressure leading to anteromedial arthrosis in the long term, even in the absence of lateral hindfoot instability

Besides eliminating pain, restoring activity is a major goal in orthopaedic interventions including joint replacement or trauma surgery following falls in frail elderly, both treatments of highest socio-economic impact. In joint replacement and even more so in frail elderly at risk of falling, turns are assessed in clinical tests such as the TUG (Timed Get-up-and-Go), Tinetti, or SPPB so that classifying turning movements in the free field with wearable activity monitors promises clinically valuable objective diagnostic or outcome parameters. It is the aim of this study to validate a computationally simple turn detection algorithm for a leg-worn activity monitor comprising 3D gyroscopes. A previously developed and validated activity classification algorithm for thigh-worn accelerometers was extended by adding a turn detection algorithm to its decision tree structure and using the 3D gyroscope of a new 9-axis IMU (56×40×15mm, 25g, f=50Hz,). Based on published principles (El-Gohary et al. Sensors 2014), the turn detection algorithm filters the x-axis (thigh) for noise and walking (Butterworth low-pass, 2. nd. order with a cut-off at 4Hz and 4. th. order with a cut-off at 0.3Hz) before using a rotational speed threshold of 15deg/s to identify a turn and taking the bi-lateral zero-crossings as start and stop markers to integrate the turning angle. For validation, a test subject wore an activity monitor on both thighs and performed a total of 57 turns of various types (walking, on-the-spot, fast/slow), ranges (45 to 360deg) and directions (left/right) in free order while being video-taped. An independent observer annotated the video so that the algorithmic counts could be compared to n=114 turns. Video-observation was compared to the algorithmic classification in a confusion matrix and the detection accuracy (true positives) was calculated. In addition, 4-day continuous activity measures from 4 test subjects (2 healthy, 2 frail elderly) were compared. Overall, only 5/114 turns were undetected producing a 96% detection accuracy. No false positives were classified. However, when detection accuracy was calculated for turning angle intervals (45°: 30–67.5°; 90°: 67.5–135°; 180°: 135–270°; 360°: 270–450°), accuracy for all interval classifications combined dropped to 83.3% with equal values for left and right turns. For the 180° and 360°, accuracy was 100% while for the shorter 45° and 90° turns accuracy was 75% and 71% only, mainly because subsequent turns were not separated. Healthy subjects performed between 470 (office worker) and 823 (house wife) turns/day while frail elderly scored 128 (high fall risk) to 487 turns/day (low fall risk). Turns/day and steps/day were not correlated. In healthy subjects ca. 50% of turns were in the 45° category compared to only ca. 35% in frail elderly. Turn detection for a thigh-worn IMU activity monitor using a computationally simple algorithm is feasible with high general detection accuracy. The classification and separation of subsequent short turns can be further improved. In multi-day measurement, turns/day and the distribution of short and long turns seem to be a largely independent activity parameter compared to step counts and may improve objective assessment of fall risk or arthroplasty outcome

AIM. When a hip is replaced using a posterior surgical approach, some of the external rotator muscles are divided. The aim of this study was to assess if this surgery has a long term affect on hip rotation during activities of daily living. METHODS. An electromagnetic tracking system was used to assess hip movements during the following activities:-. Activity 1. Picking an object of the floor in a straight leg stance. Activity 2. Picking an object of the floor when knees are flexed. Activity 3. Sitting on a chair. Activity 4. Putting on socks, seated, with the trunk flexed forward. Activity 5. Putting on socks, seated, with the legs crossed. Activity 6. Climbing stairs. Measurements were taken from 10 subjects with bilaterally normal hips, 10 patients with a large head hip replacement, 10 patients with a resurfacing head and 10 patients with a small head hip replacement. All the hip replacement patients were at least 6 months post-op, with an asymptomatic contra-lateral native hip for comparison. Sensors were attached over the iliac crest and the mid-shaft of the lateral thigh. Data was collected as each activity was repeated 3 times. The tracker recorded hip rotation at 10 hertz, with an accuracy of 0.15 degree. RESULTS. For each of the activities the Normal, Large, Resurfacing and Small Head mean external rotations were:-. Activity 1. -2.9,-6.9,9.1,-0.7. Activity 2. -8.1,-6.5,-7.0,2.0. Activity 3. -15.5,-15.6,-11.7,-15.6. Activity 4. 2.2,-10.8,5.3,-3.4. Activity 5. 33.1,24.6,22.5,23.7. Activity 6. -14.6,-11.5,-13.3,-6.5. The only movement that required substantial external rotation was activity 5 (Socks - legs crossed). For this activity the Resurfacing and Small head hips had significantly less rotation than the normal group (P = 0.01 & 0.03, respectively). A t-test comparing the normal group with the large head group had a boarder line significance level of P=0.07. Significant differences were also found for the following comparisons:- Activity 1 -Normal Vs Resurfacing, Activity 4 - Normal Vs Large head and Activity 6 - Normal Vs Small head. Discussion. The results for activity 5 show that patients with a Resurfacing or Small head hip have significantly less external rotation than the normal group

The biomechanics of the patellofemoral joint can become disturbed during total knee replacement by alterations induced by the position and shape of the different prosthetic components. The role of the patella and femoral trochlea has been well studied. We have examined the effect of anterior or posterior positioning of the tibial component on the mechanisms of patellofemoral contact in total knee replacement. The hypothesis was that placing the tibial component more posteriorly would reduce patellofemoral contact stress while providing a more efficient lever arm during extension of the knee.

We studied five different positions of the tibial component using a six degrees of freedom dynamic knee simulator system based on the Oxford rig, while simulating an active knee squat under physiological loading conditions. The patellofemoral contact force decreased at a mean of 2.2% for every millimetre of posterior translation of the tibial component. Anterior positions of the tibial component were associated with elevation of the patellofemoral joint pressure, which was particularly marked in flexion > 90°.

From our results we believe that more posterior positioning of the tibial component in total knee replacement would be beneficial to the patellofemoral joint.

This study explored the relationship between the initial stability of the femoral component and penetration of cement into the graft bed following impaction allografting.

Impaction allografting was carried out in human cadaveric femurs. In one group the cement was pressurised conventionally but in the other it was not pressurised. Migration and micromotion of the implant were measured under simulated walking loads. The specimens were then cross-sectioned and penetration of the cement measured.

Around the distal half of the implant we found approximately 70% and 40% of contact of the cement with the endosteum in the pressure and no-pressure groups, respectively. The distal migration/micromotion, and valgus/varus migration were significantly higher in the no-pressure group than in that subjected to pressure. These motion components correlated negatively with the mean area of cement and its contact with the endosteum.

The presence of cement at the endosteum appears to play an important role in the initial stability of the implant following impaction allografting.