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)
To clinically diagnose and postoperatively monitor the younger or more demanding orthopaedic patients it becomes increasingly important to measure function beyond the capacity of classic scores suffering from subjectivity, pain dominance and ceiling effects. This study investigates whether a stair climbing test with
Summary. Upper extremity activity was similar in patients and healthy subjects, showing no significant asymmetry between arms within subjects. Further improvements (e.g. thresholds, filters, inclinometer function) are needed to show the clinical value of AM for patients suffering shoulder complaints. Introduction. Activity monitoring is becoming a popular outcome tool especially in orthopaedics. The suitability of a single 3D acceleration-based activity monitor (AM) for patients with lower-extremity problems has been shown. However less is known about its feasibility to monitor upper-extremity activity. Insight into the amount and intensity of upper-extremity activity of the affected and non-affected arm (asymmetry) may be of added value for diagnostics, therapy choice and evaluating treatment effects. This study investigates the feasibility of a single AM to evaluate (asymmetry in) upper-extremity activity in daily life. Methods. Upper-extremity activity was measured in 12 patients with subacromial impingent syndrome (59±12yr) and 10 healthy subjects (29±11yrs). Subjects wore a single 3D
Summary. A single 3D
An objective technological solution for tracking adherence to at-home shoulder physiotherapy is important for improving patient engagement and rehabilitation outcomes, but remains a significant challenge. The aim of this research was to evaluate performance of machine-learning (ML) methodologies for detecting and classifying inertial data collected during in-clinic and at-home shoulder physiotherapy exercise. A smartwatch was used to collect inertial data from 42 patients performing shoulder physiotherapy exercises for rotator cuff injuries in both in-clinic and at-home settings. A two-stage ML approach was used to detect out-of-distribution (OOD) data (to remove non-exercise data) and subsequently for classification of exercises. We evaluated the performance impact of grouping exercises by motion type, inclusion of non-exercise data for algorithm training, and a patient-specific approach to exercise classification. Algorithm performance was evaluated using both in-clinic and at-home data.Aims
Methods
Recently, some smart media devices including portable
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
The iASSIST system is a portable,
Introduction. Diagnosis of osteoarthritis relies primarily on image-based analyses. X-ray, CT, and MRI can be used to evaluate various features associated with OA including joint space narrowing, deformity, articular cartilage integrity, and other joint parameters. While effective, these exams are costly, may expose the patient to ionizing radiation, and are often conducted under passive, non-weightbearing conditions. A supplemental form of analysis utilizing vibroarthrographic (VAG) signals provides an alternative that is safer and more cost-effective for the patient. The objective of this study is to correlate the kinematic patterns of normal, diseased (pre-operative), and implanted (post-operative) hip subjects to their VAG signals that were collected and to more specifically, determine if a correlation exists between femoral head center displacement and vibration signal features. Methods. Of the 28 hips that were evaluated, 10 were normal, 10 were diseased, and 8 were implanted. To collect the VAG signal from each subject, two uniaxial
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/m. 2. (range: 21.4–36.8), were included. A single
In this work, we propose a new quantitative way of evaluating acute compartment syndrome (ACS) by dynamic mechanical assessment of soft tissue changes. First, we have developed an animal model of ACS to replicate the physiological changes during the condition. Secondly, we have developed a mechanical assessment tool for quantitative pre-clinical assessment of ACS. Our hand-held indentation device provides an accurate method for investigations into the local dynamic mechanical properties of soft tissue and for in-situ non-invasive assessment and monitoring of ACS. Our compartment syndrome model was developed on the cranial tibial and the peroneus tertius muscles of a pig's leg (postmortem). The compartment syndrome pressure values were obtained by injecting blood from the bone through the muscle. To enable ACS assessment by a hand-held indentation device we combined three main components: a load cell, a linear actuator and a 3-axis
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
Falls in adults are a major problem and can lead to injuries and death. In order to better understand falls and successful recoveries, identifying kinematics, kinetics, and muscle forces during recovery from loss of balance is crucial. To obtain reactive gait patterns, participants must be subjected to unexpected perturbations such as trips and slips. Previous researchers have reported kinetics recovery data following stumbling; however, the muscle force recovery patterns remain unknown. To better target exercises to reduce the risk of falls, we must first understand which muscles, their magnitude, and their coordination patterns, play a role in a successful recovery from a trip and a slip. Additionally, knowing the successful patterns of lower limb function can help with the diagnosis of faulty movements. A total of 20 healthy adults in their twenties with similar athletic backgrounds were perturbed on a split-belt treadmill using Computer-Assisted Rehabilitation Environment (Motkforce Link) at a preset speed of 1.1m/s. Two kinds of perturbations were administered: slip and trip. Slips were simulated by accelerating one belt, whereas trips were simulated by decelerating one belt. Both perturbations had similar intensity and only differed in the direction. Computational modeling was used to obtain lower-limb function during the compensatory step. SPM paired t-test was used to compare differences in recovery strategies between slip and trip through magnitude and patterns of joints. There were no significant differences in joint angles post tripping vs post-slipping. Results of net joint moments showed that compensating for the loss of balance due to tripping required a higher ankle plantarflexion moment than slipping (at 22-52%; 1.2± 0.3vs0.4±0.2, p<0.001). Additionally, larger gluteus maximus (at 40-50%;8.7±3.8vs2.7±1.1N/kg, p=0.001), gluteus medius (at23~33%; 22.6±5.7vs6.8±3.6N/kg, p<0.001) were generated than post-slipping, respectively. These findings suggested that greater GMAX and GMED forces are required post-trip recovery than slip. Future analysis of trip recovery showed the importance of ankle joint in recovering from forward and backward fall. These results can be used as references in remote diagnosis of joint and muscle weakness and assessment of the risk of falls with the use of
Abstract. Objectives. The aim of this study was to investigate whether mechanical loading induced by physical activity can reduce risk of sarcopenia in middle-aged adults. Methods. This was a longitudinal study based on a subset of UK Biobank data consisting of 1,918 participants (902 men and 1,016 women, mean age 56 years) who had no sarcopenia at baseline (assessed between 2006 and 2010). The participants were assessed again after 6 years at follow-up, and were categorized into no sarcopenia, probable sarcopenia, or sarcopenia according to the definition and algorithm developed in 2018 by European Working Group on Sarcopenia in Older People (EWGSOP). Physical activity was assessed at a time between baseline and follow-up using 7-day acceleration data obtained from wrist worn
Introduction. In cementless THA the incidence of intraoperative fracture has been reported to be as high 28% [1]. To mitigate these surgical complications, investigators have explored vibro-acoustic techniques for identifying fracture [2–5]. These methods, however, must be simple, efficient, and robust as well as integrate with workflow and sterility. Early work suggests an energy-based method using inexpensive sensors can detect fracture and appears robust to variability in striking conditions [4–5]. The orthopaedic community is also considering powered impaction as another way to minimize the risk of fracture [6– 8], yet the authors are unaware of attempts to provide sensor feedback perhaps due to challenges from the noise and vibrations generated during powered impaction. Therefore, this study tests the hypothesis that vibration frequency analysis from an
Introduction. Aseptic loosening of the acetabular cup in total hip replacement (THR) remains a major problem. Current diagnostic imaging techniques are ineffective at detecting early loosening, especially for the acetabular component. The aim of this preliminary study was to assess the viability of using a vibration analysis technique to accurately detect acetabular component loosening. Methods. A simplified acetabular model was constructed using a Sawbones foam block into which an acetabular cup was fitted. Different levels of loosening were simulated by the interposition of thin layer of silicon between the acetabular component and the Sawbones block. This included a simulation of a secure (stable) fixation and various combinations of cup zone loosening. A constant amplitude sinusoidal excitation with a sweep range of 100–1500 Hz was used. Output vibration from the model was measured using an
Capturing objective data of the postoperative changes in the mobility of patients is expected to generate a better understanding of the effect of postoperative treatment. Until recently, the collection of gait-related data was limited to controlled clinical environments. The emergence of accurate wearable
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
Mobility plays an important role, in particular for patients with osteoporosis and after trauma surgery, both as an outcome and as treatment. Mobility is closely linked to the patient”s quality of life and exercise is a powerful additional treatment option. In order to be able to generate an evidence base to evaluate various surgical and non-surgical treatment options, objective measurements of patient mobility and exercise over a certain time period are needed. Wearables are a promising candidate, with obvious advantages compared to questionnaires and/or PROs. However, when extracting parameters with wearables, one often faces the problem of algorithms not performing well enough for special cases like slow gait speeds or impaired gait, as they typically appear in this patient group. We plan to further extend the applicability of the actibelt system (3D
Measurements of shoulder kinematics during activities of daily living (ADL) can be used to evaluate patient function before and after treatment and help define device testing conditions. However, due to the difficulties of making 3D motion measurements outside of laboratory conditions, there are few reports of measured shoulder 3D kinematics during ADL. The purpose of this study was to demonstrate the feasibility of using wearable inertial measurement units (IMUs) to track shoulder joint angles. A nonrandom sample of 5 subjects with normal shoulders was selected based on occupation. The occupations were: dental hygienist, primary school teacher, mechanical project engineer, administrative assistant, and retail associate. Subjects wore two OPAL IMUs (APDM, Portland OR) as shown in Figure 1 on the sternum and on the upper arm for approximately 4 hours while at their workplace performing their normal work place activities and then up to 4 hours while off-work. Orientation angles from IMUs have traditionally been estimated by integrating gyroscope data and calculating inclination angles relative to gravity with