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BackgroundThe prevalence of knee osteoarthritis (KOA) in the adult population is high and patients profit from individualized therapy approaches. Just-in-time adaptive interventions (JITAIs) are upcoming digital interventions for behavior change. ObjectiveThis systematic review summarizes the features and effectiveness of existing JITAIs regarding important physiological health outcomes and derives the most promising features for the use case of KOA. MethodsThe electronic databases PubMed, Web of Science, Scopus, and EBSCO were searched using keywords related to JITAIs, physical activity (PA), sedentary behavior (SB), physical function, quality of life, pain, and stiffness. JITAIs for adults that focused on the effectiveness of at least 1 of the selected outcomes were included and synthesized qualitatively. Study quality was assessed with the Quality Assessment Tool Effective Public Health Practice Project. ResultsA total of 45 studies with mainly weak overall quality were included in this review. The studies were mostly focused on PA and SB and no study examined stiffness. The design of JITAIs varied, with a frequency of decision points from a minute to a day, device-based measured and self-reported tailoring variables, intervention options including audible or vibration prompts and tailored feedback, and decision rules from simple if-then conditions based on 1 variable to more complex algorithms including contextual variables. ConclusionsThe use of frequent decision points, device-based measured tailoring variables accompanied by user input, intervention options tailored to user preferences, and simple decision rules showed the most promising results in previous studies. This can be transferred to a JITAI for the use case of KOA by using target variables that include breaks in SB and an optimum of PA considering individual knee load for the health benefits of patients.

Hip Osteoarthritis (HOA) is a common joint disease with serious impact on the quality of life of the affected persons. Additionally, persons with HOA often show alterations in gait biomechanics. Developing effective conservative treatment strategies is of paramount importance, as joint replacement is only indicated for end-stage HOA. In contrast to knee osteoarthritis, little is known about the effectiveness of hip bracing for the management of HOA. Studies analysing mechanically unloading hip braces partly showed beneficial results. However, methodological limitations of these studies, such as small sample sizes or lack of control groups, limit the applicability of the results. Additionally, mechanically unloading braces might impose restrictions on motion and comfort and thus, might not be suitable for people with only mild or moderate symptoms. The aim of this study was to comprehensively quantify the effects of unilateral HOA as well as functional hip bracing on gait biomechanics, pain, proprioception and functional capacity in people with mild to moderate HOA. Hip and pelvis biomechanics during walking were analysed in 21 subjects with mild to moderate HOA under three bracing conditions: unbraced, immediately after brace application and after 1 week of brace usage. Additionally, pain, hip proprioception and functional capacity were assessed. A matched group of 21 healthy subjects was included as reference. Kinematic and kinetic data were collected using a 16-camera infrared motion capturing system and two force plates. Visual analogue scales, an angle reproduction test and a 6-min walking test were applied to measure pain, hip proprioception and functional capacity, respectively. Subjects with HOA walked slower, with reduced step length, sagittal hip range of motion and peak extension angle and had a reduced functional capacity. After 1 week of brace application step length, walking speed and functional capacity were significantly increased. Additionally, pain perception was significantly lower in the intervention period. These results encourage the application of functional hip braces in the management of mild to moderate HOA. However, as key parameters of HOA gait such as a reduced peak extension angle remained unchanged, the underlying mechanisms remain partly unclear and have to be considered in the future.

Kinematic synergies (kSYN) provide an approach to quantify the covariation of joint motions and to explain the mechanisms underlying human motor behavior. A low-dimensional control strategy by means of the activation of a moderate number of kSYN would simplify the performance of complex motor tasks. The purpose of this study was to examine similarities between the kSYN of varying locomotion tasks: straight-line walking, walking a 90° spin turn and walking upstairs. Task-specific kSYN were extracted from full body kinematic recordings of 13 participants by principal component analysis. The first five kSYN accounting for most of the variance within each task were selected for further analysis following previous studies. The similarities between the kSYN of the three different locomotion tasks were quantified by calculating cosine similarities (SIM), as a vector-based similarity measure ranging from 0 (no similarity) to 1 (high similarity), between absolute principal component loading vectors. A SIM between two kSYN > 0.8 was interpreted as highly similar. Two to three highly similar kSYN were identified when comparing two individual tasks with each other. One kSYN, primarily characterized by anteversion and retroversion of the arms and legs, were found to be similar in all three tasks. Additional kSYN that occurred between individual tasks reflected mainly an upwards/downwards movement of the body or a countercyclical knee flexion/extension. The results demonstrate that the three investigated locomotion tasks are characterized by kSYN and that certain kSYN repeatedly occur across the three locomotion tasks. PCA yields kSYN which are in descent order according to their amount of total variance accounted for. Referring to the placing of a kSYN within the order as priorization, we found a change in priorization of repeatedly occurring kSYN across the individual tasks. The findings support the idea that movements can be efficiently performed through a flexible combination of a lower number of control-relevant variables.

Joint moment measurements represent an objective biomechanical parameter of knee joint load in knee osteoarthritis (KOA). Wearable sensors in combination with machine learning techniques may provide solutions to develop assistive devices in KOA patients to improve disease treatment and to minimize risk of non-functional overreaching (e.g., pain). The purpose of this study was to develop an artificial neural network (ANN) that estimates external knee flexion moments (KFM) and external knee adduction moments (KAM) during various locomotion tasks, based on data obtained by two wearable sensors. Thirteen participants were instrumented with two inertial measurement units (IMUs) located on the right thigh and shank. Participants performed six different locomotion tasks consisting of linear motions and motions with a change of direction, while IMU signals as well as full body kinematics and ground reaction forces were synchronously recorded. KFM and KAM were determined using a full body biomechanical model. An ANN was trained to estimate the KFM and KAM time series using the IMU signals as input. Evaluation of the ANN was done using a leave-one-subject-out cross-validation. Concordance of the ANN-estimated KFM and reference data was categorized for five tasks (walking straight, 90° walking turn, moderate running, 90° running turn and 45° cutting maneuver) as strong (r ≥ 0.69, rRMSE ≤ 23.1) and as moderate for fast running (r = 0.65 ± 0.43, rRMSE = 25.5 ± 7.0%). For all locomotion tasks, KAM yielded a lower concordance in comparison to the KFM, ranging from weak (r ≤ 0.21, rRMSE ≥ 33.8%) in cutting and fast running to strong (r = 0.71 ± 0.26, rRMSE = 22.3 ± 8.3%) for walking straight. Smallest mean difference of classical discrete load metrics was seen for KFM impulse, 10.6 ± 47.0%. The results demonstrate the feasibility of using only two IMUs to estimate KFM and KAM to a limited extent. This methodological step facilitates further work that should aim to improve the estimation accuracy to provide valuable biofeedback systems for KOA patients. Greater accuracy of effective implementation could be achieved by a participant- or task-specific ANN modeling.

Joint kinetics play an important role in assessing the mechanical joint load, and their analysis is crucial to understand injury mechanisms and disease progression. Joint kinetics analysis (e.g., joint moments) is commonly conducted using human motion data recorded in a laboratory and biomechanical modeling. The combination of wearable sensors, such as inertial measurement units (IMU) or electromyography (EMG), and machine learning (ML) is increasingly popular to overcome laboratory-based limitations (Gurchiek et al., 2019). However, comparing different studies investigating various sensors and locomotion tasks can be challenging due to variations in ML algorithms, model evaluation techniques, and reported performance metrics (Gurchiek et al., 2019). Therefore, it is currently unclear which type of wearable sensor yields the highest joint moment prediction accuracies using ML. Additionally, comparing joint moment prediction accuracies for different locomotion tasks could provide valuable insight for developing assistive tools (Lee & Lee, 2022). The aim of this study was: (1.) to systematically compare the prediction accuracies of lower-limb joint moments prediction based on three different ML inputs (IMU, EMG, and IMU+EMG), and (2.) to analyze prediction accuracies across various locomotion tasks. Simultaneously acquired motion capture and wearable sensor data (unilaterally positioned on the right foot, thigh, shank, as well as on the torso) from 21 participants performing stair ascent and descent, ramp ascent and descent, and treadmill walking were used (Camargo et al., 2021). Convolutional neural networks (CNNs) were trained on three different inputs combining all locomotion tasks: 1. a dataset from four IMUs, 2. a dataset from eleven EMG sensors, and 3. a dataset from both sensors (the IMUs and EMG sensors). The CNN outputs were for all models the hip flexion, hip adduction, knee flexion, and ankle flexion moment time series, which were available from inverse dynamic calculations. Leave-one-subject-out cross-validations were conducted, and correlation coefficients (r), root mean square errors (RMSE) and relative root mean square errors (relRMSE) were determined to compare model performances. A similar mean lower-limb joint moment prediction accuracy based on different wearable sensors was observed (0.16 Nm/kg ≤ RMSE ≤ 0.18 Nm/kg, 13.8% ≤ relRMSE ≤ 15.1%, 0.94 ≤ r ≤ 0.95; Figure 1.A), and all correlation coefficients between predicted and reference joint moments were greater than 0.91. Different locomotion tasks revealed small differences in mean joint moment prediction accuracy (0.15 Nm/kg ≤ RMSE ≤ 0.17 Nm/kg, 12.7% ≤ relRMSE ≤ 16.3%, 0.93 ≤ r ≤ 0.96; Figure 1.B). However, predicting the hip flexion moment for stair descent revealed reduced accuracy (relRMSE = 22.3 ± 6.9 %, r = 0.78 ± 0.30). These findings demonstrate, consistent with related studies (Lee & Lee, 2022; Moghadam et al., 2023), the potential of predicting lower-limb joint moments for various locomotion tasks by combining wearable sensors and ML. Other factors, such as task diversity, sample size, ML algorithms, and model evaluation, may have a greater impact on joint moment predictions than different wearable sensor-based model inputs. The provided systematic comparison of different wearable sensors and locomotion tasks can assist the advancement of wearable measurement technology for clinical and sports biomechanics. References Camargo, J., Ramanathan, A., Flanagan, W., & Young, A. (2021). A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions. Journal of Biomechanics, 119, Article 110320. https://doi.org/10.1016/j.jbiomech.2021.110320 Gurchiek, R. D., Cheney, N., & McGinnis, R. S. (2019). Estimating biomechanical time-series with wearable sensors: A systematic review of machine learning techniques. Sensors, 19(23), Article 5227. https://doi.org/10.3390/s19235227 Lee, C. J., & Lee, J. K. (2022). Inertial motion capture-based wearable systems for estimation of joint kinetics: A systematic review. Sensors, 22(7), Article 2507. https://doi.org/10.3390/s22072507 Moghadam, S. M., Yeung, T., & Choisne, J. (2023). A comparison of machine learning models’ accuracy in predicting lower-limb joints’ kinematics, kinetics, and muscle forces from wearable sensors. Scientific Reports, 13(1), Article 5046. https://doi.org/10.1038/s41598-023-31906-z

Optical motion capture systems are the gold standard for collecting three-dimensional kinematic data in sports. However, while their use in flow channels is somewhat practical, they are difficult to employ in swimming pools, e.g. due to camera placement and the dynamic refraction of light and air bubbles. IMUs can circumvent these problems, but have so far primarily been used to calculate relatively condensed information such as distance per stroke or lap counts (Magalhaes et al., 2015). Additionally, current commercially available IMU-based systems for the collection of segment orientations and joint angles are often bulky and not waterproof (Ascenso, 2021). Therefore, the purpose of this study was to develop a novel IMU-based motion capture system for the specific requirements of swimming. Hardware State-of-the-art IMUs (ICM-20948, InvenSense Inc., San José, California, USA) in combination with microcontrollers (ESP32-S3, Espressif Systems, Shanghai, China) are used to record raw IMU signals. Each unit (i.e. IMU and microcontroller) is sealed in foil using a vacuum sealer to waterproof the electronics and remove air. The microcontrollers store the IMU raw data internally (up to 3.8 MB) during a recording and allow for a later data transmission to external systems. To do this, each microcontroller creates its own asynchronous web server, which is used to send commands to the microcontrollers and retrieve data via HTTP requests. Data processing An accurate IMU calibration is particularly important, as IMUs, such as the ICM-20948, contain systematic errors and are susceptible to noise. The calibration process used in this study is largely based on Särkkä et al. (2017). The raw sensor outputs are compared with known inputs (reference signals): net rotations between known positions, gravity and the magnetic field of the Earth. For this, the sensors are rotated into 24 different positions. Successive positions differ by 90° and the sensors remain stationary for a few seconds in each position. Using an iterative algorithm, 12 calibration parameters are calculated for each accelerometer and magnetometer and three for the gyroscope. After calibration, the data is processed using a 5 Hz low-pass Butterworth filter and the NXP fusion filter (NXP Semiconductors, 2016) to determine the orientations of the sensors relative to a global coordinate system. Sensor-to-segment calibration is achieved by averaging the rotations during a neutral standing position. The microcontrollers are synchronized by repeatedly exchanging timestamps and calculating linear regressions with RANSAC (i.e. ignoring outliers) that relate the local timestamps to a common global time. Validation and testing We present validation results for 3D shoulder, elbow and head angles for simulated front crawl swimming as well as isolated movements in the five joints using a synchronized optical motion capture system in the laboratory. In addition, findings from plausibility tests during actual swimming in a swimming pool are presented. References Ascenso, G. (2021). Development of a non-invasive motion capture system for swimming biomechanics (Doctoral dissertation). Manchester Metropolitan University, United Kingdom. NXP Semiconductors. (2016). AN5023 Sensor fusion kalman filters. https://www.nxp.com/docs/en/application-note/AN5023.pdf Magalhaes, F. A. de, Vannozzi, G., Gatta, G., & Fantozzi, S. (2015). Wearable inertial sensors in swimming motion analysis: A systematic review. Journal of Sports Sciences, 33(7), 732–745. https://doi.org/10.1080/02640414.2014.962574 Särkkä, O., Nieminen, T., Suuriniemi, S., & Kettunen, L. (2017). A multi-position calibration method for consumer-grade accelerometers, gyroscopes, and magnetometers to field conditions. IEEE Sensors Journal, 17(11), 3470–3481. https://doi.org/10.1109/JSEN.2017.2694488