Smartphones are often equipped with inertial sensors capable of measuring individuals' physical activities. Their role in monitoring the patients' physical activities in telemedicine, however, needs to be explored. The main objective of this study was to explore the correlation between a participant's daily step counts and the daily step counts reported by their smartphone. This prospective observational study was conducted on patients undergoing lower limb orthopedic surgery and a group of non-patients. The data collection period was from 2 weeks before until four weeks after the surgery for the patients and two weeks for the non-patients. The participants' daily steps were recorded by physical activity trackers employed 24/7, and an application recorded the number of daily steps registered by the participants' smartphones. We compared the cross-correlation between the daily steps time-series taken from the smartphones and physical activity trackers in different groups of participants. We also employed mixed modeling to estimate the total number of steps. Overall, 1067 days of data were collected from 21 patients (11 females) and 10 non-patients (6 females). The cross-correlation coefficient between the smartphone and physical activity tracker was 0.70 [0.53–0.83]. The correlation in the non-patients was slightly higher than in the patients (0.74 [0.60–0.90] and 0.69 [0.52–0.81], respectively). Considering the ubiquity, convenience, and practicality of smartphones, the high correlation between the smartphones and the total daily step time-series highlights the potential usefulness of smartphones in detecting the change in the step counts in remote monitoring of the patient's physical activity

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

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

The tendency towards using inertial sensors for remote monitoring of the patients at home is increasing. One of the most important characteristics of the sensors is sampling rate. Higher sampling rate results in higher resolution of the sampled signal and lower amount of noise. However, higher sampling frequency comes with a cost. The main aim of our study was to determine the validity of measurements performed by low sampling frequency (12.5 Hz) accelerometers (SENS) in patients with knee osteoarthritis compared to standard sensor-based motion capture system (Xsens). We also determined the test-retest reliability of SENS accelerometers. Participants were patients with unilateral knee osteoarthritis. Gait analysis was performed simultaneously by using Xsens and SENS sensors during two repetitions of over-ground walking at a self-selected speed. Gait data from Xsens were used as an input for AnyBody musculoskeletal modeling software to measure the accelerations at the exact location of two defined virtual sensors in the model (VirtualSENS). After preprocessing, the signals from SENS and VirtualSENS were compared in different coordinate axes in time and frequency domains. ICC for SENS data from first and second trials were calculated to assess the repeatability of the measurements. We included 32 patients (18 females) with median age 70.1[48.1 – 85.4]. Mean height and weight of the patients were 173.2 ± 9.6 cm and 84.2 ± 14.7 kg respectively. The correlation between accelerations in time domain measured by SENS and VirtualSENS in different axes was r = 0.94 in y-axis (anteroposterior), r = 0.91 in x-axis (vertical), r = 0.83 in z-axis (mediolateral), and r = 0.89 for the magnitude vector. In frequency domain, the value and the power of fundamental frequencies (F. 0. ) of SENS and VirtualSENS signals demonstrated strong correlation (r = 0.98 and r = 0.99 respectively). The result of test-retest evaluation showed excellent repeatability for acceleration measurement by SENS sensors. ICC was between 0.89 to 0.94 for different coordinate axes. Low sampling frequency accelerometers can provide valid and reliable measurements especially for home monitoring of the patients, in which handling big data and sensors cost and battery lifetime are among important issues

Abstract. Rehabilitation exercise is critical for patients’ recovery after knee injury or post-surgery. Unfortunately, adherence to exercise is low due to a lack of positive feedback and poor self-motivation. Therefore, it is crucial to monitor their progress and provide supervision. Inertial measurement unit (IMUs) based sensing technology can provide remote patient monitoring functions. However, most current solutions only measure the range of knee motion in one degree of freedom. The current IMUs estimate the orientation-angle based on the integrated raw data, which might lack accuracy in measuring knee motion. This study aims to develop an IMU-based sensing system using the absolute measured orientation-angle to provide more accurate comprehensive monitoring by measuring the knee rotational angles. An IMU sensing system monitoring the knee joint angles, flexion/extension (FE), adduction/abduction (AA), and internal/external (IE) was developed. The accuracy and reliability of FE measurements were validated in human participants during squat exercise using measures including root mean square error (RMSE) and correlation coefficient. The RMSE of the three knee angles (FE, AA, and IE) were 0.82°, 0.26°, and 0.11°, which are acceptable for assessing knee motion. The FE measurement was validated in human participants and showed excellent accuracy (correlation coefficient of 0.99°). Further validation of AA and IE in human participants is underway. The sensing system showed the capability to estimate three knee rotation angles (FE, AA, and IE). It showed the potential to provide comprehensive continuous monitoring for knee rehabilitation exercises, which can also be used as a clinical assessment tool

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

Introduction. Measured outcomes from knee joint arthroplasty (TKA) have primarily focused on surgeon-directed criteria, such as alignment, range of motion measured in the clinic, and implant durability, rather than on functional outcomes. There is strong evidence that subjective reporting by patients fails to capture objective real-life function. 1,2. We believe that the recent emphasis on clinical outcomes desired by the patient, as well as the need to demonstrate value, requires a new approach to patient outcomes that directly monitors ambulatory activity after surgery. We have developed and tested a system that: 1) autonomously identifies patients who are not progressing well in their recovery from TKA surgery; 2) characterizes patient activity profiles; 3) automatically alerts health care providers of patients who should be seen for additional follow-up. We anticipate that such a system could decrease secondary procedures such as manipulation under anesthesia (MUA) and reduce hospital re-admission rates thereby resulting in significant cost savings to the patient, the care providers, and insurers. Methods. The components of the system include: 1) A sensor package that is mounted correctly in relation to the knee joint (Figure 1a) and is suitable for long term use; 2) An application that runs under the Android operating system to communicate with the sensor and to gather subjective information (pain, satisfaction, perceived stability etc. together with a photograph of the surgical site (Figure 1b); 3) Software to upload the data from the phone to a remote server; 4) An analysis and reporting package that generates, among other metrics, a profile describing the patient's activity throughout the day, trends in the recovery process, and alerts for abnormal findings (Figure 1c). The system was pilot tested on 12 patients (7 females) who underwent TKA. Complete days of data collection were scheduled for each patient every two weeks until 12 weeks, starting during the second week after surgery. Results. Patients tolerated the system well and datasets of up to 13 hours long were recorded. There was a considerable variation between patients in the use of the prosthetic knee joint at a given time point after surgery. At 6 weeks post-surgery, for example, some relatively inactive subjects had less than 50 excursions per hour while active subjects exhibited more than 750 excursions per hour. It was notable that, in activities of daily living, subjects rarely used the extremes of the flexion range that had been measured during post-operative clinic visits. Examples of activity recognition during free-living will be presented. Discussion. A remote knee monitoring system has been designed and successfully tested in an outpatient setting. The system has revealed discrepancies between knee function measured during clinic visits and that measured remotely during free living. Remote monitoring after orthopaedic procedures adds an important new dimension to the assessment of patient outcome. Acknowledgments. This work was supported by grants from the Washington Research Foundation and The Wallace H. Coulter Translational Partnership at the University of Washington

Introduction. Gait analysis systems have enjoyed increasing usage and have been validated to provide highly accurate assessments for range of motion. Size, cost, need for marker placement and need for complex data processing have remained limiting factors in uptake outside of what remains predominantly large research institutions. Progress and advances in deep neural networks, trained on millions of clinically labelled datasets, have allowed the development of a computer vision system which enables assessment using a handheld smartphone with no markers and accurate range of motion for knee during flexion and extension. This allows clinicians and therapists to objectively track progress without the need for complex and expensive equipment or time-consuming analysis, which was concluded to be lacking during a recent systematic review of existing applications. Method. A smartphone based computer vision system was assessed for accuracy with a gold standard comparison using a validated ‘traditional’ infra-red motion capture system which had a defined calibrated accuracy of 0.1degrees. A total of 22 subjects were assessed simultaneously using both the computer vision smartphone application and the standard motion capture system. Assessment of the handheld system was made by comparison to the motion capture system for knee flexion and extension angles through a range of motion with a simulated fixed-flexion deformity which prevented full extension to assess the accuracy of the system, repeating movements ten times. The peak extension angles and also numerous discrete angle measurements were compared between the two systems. Repeatability was assessed by comparing several sequential cycles of flexion/extension and comparison of the maximum range of motion in normal knees and in those with a simulated fixed-flexion deformity. In addition, discrete angles were also measured on both legs of three cadavers with both skin and then bone implanted fiducial markers for ground truth reliability accounting for skin movement. Data was processed quickly through an automated secure cloud system. Results. The smartphone application was found to be accurate to 1.47±1.05 degrees through a full range of motion and 1.75±1.56 degrees when only peak extension angles were compared, demonstrating excellent reliability and repeatability. The cadaveric studies despite limitations which will be discussed still showed excellent accuracy with average errors as low as 0.29 degrees for individual angles and 4.09 degrees for an average error in several measurement. Conclusion. This novel solution offers for the first time a way to objectively measure knee range of motion using a markerless handheld device and enables tracking through a range of assessments with proven accuracy and reliability even accounting for traditional issues with the previous marker based systems. Repeatability for both computer vision and motion capture have greater extrinsic than intrinsic error, particularly with marker placement - another benefit of a markerless system. Clinical applications include pre-operative assessment and post-operative follow-up, paired with surgical planning (including with robots) and remote monitoring after knee surgery, with outcomes guiding treatment and rehabilitation and leading to reduced need for manipulation under anaesthesia and greater satisfaction

Aims

To review the evidence and reach consensus on recommendations for follow-up after total hip and knee arthroplasty.

Aims

Scoliosis is a lateral curvature of the spine with associated rotation, often causing distress due to appearance. For some curves, there is good evidence to support the use of a spinal brace, worn for 20 to 24 hours a day to minimize the curve, making it as straight as possible during growth, preventing progression. Compliance can be poor due to appearance and comfort. A night-time brace, worn for eight to 12 hours, can achieve higher levels of curve correction while patients are supine, and could be preferable for patients, but evidence of efficacy is limited. This is the protocol for a randomized controlled trial of ‘full-time bracing’ versus ‘night-time bracing’ in adolescent idiopathic scoliosis (AIS).

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction.

Cite this article: Bone Joint Res 2023;12(7):447–454.

Wireless technologies applied to the medical field have grown both in prevalence and importance in the past decade. Various applications and technologies exist underneath the telemedicine umbrella including Point-of-Care systems where electrocardiographs, blood pressure, temperature, and medical image data are recorded and transmitted wirelessly, which enables remote patient monitoring from inside hospitals, personal residences, and virtually any location with access to satellite communication. Another widespread application for wireless systems in hospitals is asset tracking, typically done with RFID technology. Wireless technologies have not been widely used in computer assisted orthopaedic surgery (CAOS) because of the limitations in terms of overall 3-D accuracy. We have developed a wireless positioning system based on ultra wideband technology (UWB) which achieves mm-range 3-D dynamic accuracy and can be used for intraoperative tracking in CAOS systems. Current intraoperative tracking technologies include optical and electromagnetic tracking systems. The main limitations with these systems include the need for line-of-sight in optical systems and the limited view volume and susceptibility to metallic interference in electromagnetic tracking systems. UWB indoor positioning does not suffer from these effects. Until this point, the main limitation of UWB indoor positioning systems was its limitation in 3-D real-time dynamic accuracy (10–15 cm as opposed to the required 1–2 mm). We have developed a UWB indoor positioning system which achieves dynamic 3-D accuracy in the range of 5–6 mm for a non-coherent approach and 0.5–1 mm for a coherent approach (transmitter and receiver use the same clock signal). The integration of this tracking system with smart surgical tools opens up a plethora of exciting intraoperative applications including picking landmarks, 3-D bone and instrument registration, real-time wireless pressure sensing used for ligament balancing in TKA, and real-time A-mode ultrasound bone morphing. The UWB tracking system will be presented along with its integration into smart surgical tools and surgical navigation

Introduction. Proper total knee arthroplasty balancing relies on accurate component positioning and alignment as well as soft tissue tensioning. Technology for cutting guide alignment has evolved from the “free hand” technique in the 1970's, to traditional intra/extra medullary rods in the 1980's and 1990's, to computer navigated surgery in the 2000's, and finally to patient specific custom cutting blocks in the 2010's. The latest technique is a modification to conventional computer navigation assisted surgery using Brainlab's Dash™ TKA/THA software platform that runs as an application on an Apple IPod held by the surgeon in a sterile pouch in the operative field. The handheld IPod touch screen allows the surgeon to control all aspects of the navigation interface without needing the assistance of an observer to manually run the software. In addition, the surgeon is able to always focus on the operative field while ‘navigating’ without looking up at a remote image monitor. This study represents a prospective analysis of the first 30 U.S. TKA cases performed using the newly commercially released Dash™ software using an IPod during surgery. Methods. Thirty consecutive primary total knee arthroplasty procedures were performed using the Dash™ software (Brainlab) and an IPod touch (Apple). A cemented Genesis II (Smith Nephew) posterior stabilized implant was used in all cases. Femoral and tibial sensor arrays were placed in meta-diaphyseal regions for bone registration. We recorded the time spent to set up the arrays, time for bony registration, time to navigate the cutting guides, and the tourniquet time. After all bone cuts were completed, the tibial cut was manually measured with an intramedullary angle check instrument to assess the planned zero degree posterior slope and neutral varus/valgus coronal alignment. Final femoral and tibial component alignment and orientation was measured on standing long axis AP and lateral radiographs. Measurements from the Dash™ alignment group were compared to 30 consecutive surgeries using the author's traditional technique of intramedullary cutting block alignment (control group). Results. In the initial 6 surgeries conducted, total navigation time exceeded 20 minutes reflecting the learning curve. In the remaining computer navigation group cases, average time for array set up was 3 minutes, average time for bony registration was 3 minutes, average time for navigating the cutting guides was 12 minutes, and average tourniquet time was 53 minutes. In the control group, the average tourniquet time was 44 minutes. There was no statistically significant difference in component alignment between the two groups when measuring distal femoral valgus angle, posterior condylar offset, femoral flexion/extension angle, tibial slope angle, or tibial varus/valgus angle. Conclusions. Total knee arthroplasty using computer navigation and an IPod interface with Dash™ software is as accurate when compared to a traditional intramedullary TKA alignment technique. Only an additional average time of 9 minutes (after initial learning curve) using Dash™ navigation was needed. Further studies will compare these alignment techniques to extramedullary alignment and custom patient specific cutting block procedures