Impact of a Semi-Rigid Knee Orthotic Intervention on Pain, Physical Activity, and Functional Capacity in Patients with Medial Knee Osteoarthritis.

Background: The effectiveness of knee orthoses as part of conservative treatment for patients with medial knee osteoarthritis has not been fully explored. The purpose of this study was to evaluate the effects of a novel semi-rigid knee orthosis on pain, physical activity, and functional capacity. Methods: Pain levels, physical activity, and functional capacity were assessed in 24 participants experiencing symptomatic medial knee osteoarthritis one week before (i.e., pretest) initiating a six-week orthosis intervention and again during the final week of the intervention (i.e., post-test). Results: Night pain, pain during walking, pain during stair climbing, and pain during sitting consistently decreased by 41% to 48% while wearing the knee orthosis. Device-based measured physical activity showed a 20.2-min increase in vigorous physical activity during the post-test, while light and moderate physical activity did not show significant changes. After six weeks of orthosis application, there was a 5% increased distance for the six-minute walk test, and participants reported fewer limitations both in everyday and athletic activities, as well as an enhanced quality of life. Conclusions: These findings highlight the potential effectiveness of a semi-rigid knee orthosis to enhancing functional capacity and quality of life. More extensive and longer clinical trials are needed to improve confidence in these findings and understand their impact on disease progression.

Rollator usage lets young individuals switch movement strategies in sit-to-stand and stand-to-sit tasks.

The transitions between sitting and standing have a high physical and coordination demand, frequently causing falls in older individuals. Rollators, or four-wheeled walkers, are often prescribed to reduce lower-limb load and to improve balance but have been found a fall risk. This study investigated how rollator support affects sit-to-stand and stand-to-sit movements. Twenty young participants stood up and sat down under three handle support conditions (unassisted, light touch, and full support). As increasing task demands may affect coordination, a challenging floor condition (balance pads) was included. Full-body kinematics and ground reaction forces were recorded, reduced in dimensionality by principal component analyses, and clustered by k-means into movement strategies. Rollator support caused the participants to switch strategies, especially when their balance was challenged, but did not lead to support-specific strategies, i.e., clusters that only comprise light touch or full support trials. Three strategies for sit-to-stand were found: forward leaning, hybrid, and vertical rise; two in the challenging condition (exaggerated forward and forward leaning). For stand-to-sit, three strategies were found: backward lowering, hybrid, and vertical lowering; two in the challenging condition (exaggerated forward and forward leaning). Hence, young individuals adjust their strategy selection to different conditions. Future studies may apply this methodology to older individuals to recommend safe strategies and ultimately reduce falls.

Effects of Hip Bracing on Gait Biomechanics, Pain and Function in Subjects With Mild to Moderate Hip Osteoarthritis.

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.

Identification of Patients with Similar Gait Compensating Strategies Due to Unilateral Hip Osteoarthritis and the Effect of Total Hip Replacement: A Secondary Analysis.

Despite good clinical functional outcome, deficits in gait biomechanics exist 2 years after total hip replacement surgery. The aims of this research were (1) to group patients showing similar gait adaptations to hip osteoarthritis and (2) to investigate the effect of the surgical treatment on gait kinematics and external joint moments. In a secondary analysis, gait data of 51 patients with unilateral hip osteoarthritis were analyzed. A k-means cluster analysis was performed on scores derived via a principal component analysis of the gait kinematics. Preoperative and postoperative datasets were statistically tested between clusters and 46 healthy controls. The first three principal components incorporated hip flexion/extension, pelvic tilt, foot progression angle and thorax tilt. Two clusters were discriminated best by the peak hip extension during terminal stance. Both clusters deviated from healthy controls in spatio-temporal, kinematic and kinetic parameters. The cluster with less hip extension deviated significantly more. The clusters improved postoperatively but differences to healthy controls were still present one year after surgery. A poor preoperative gait pattern in patients with unilateral hip osteoarthritis is associated with worse gait kinematics after total hip replacement. Further research should focus on the identification of patients who can benefit from an adapted or individualized rehabilitation program.

Modularity in Motor Control: Similarities in Kinematic Synergies Across Varying Locomotion Tasks.

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.

Automated gait event detection for a variety of locomotion tasks using a novel gyroscope-based algorithm.

BACKGROUND: The robust identification of initial contact (IC) and toe-off (TO) events is a vital task in mobile sensor-based gait analysis. Shank attached gyroscopes in combination with suitable algorithms for data processing can robustly and accurately complete this task for gait event detection. However, little research has considered gait detection algorithms that are applicable to different locomotion tasks. RESEARCH QUESTION: Does a gait event detection algorithm for various locomotion tasks provide comparable estimation accuracies as existing task-specific algorithms? METHODS: Thirteen males, equipped with a gyroscope attached to the right shank, volunteered to perform nine different locomotion tasks consisting of linear movements and movements with a change of direction. A rule-based algorithm for IC and TO events was developed based on the shank sagittal plane angular velocity. The algorithm was evaluated against events determined by vertical ground reaction force. Absolute mean error (AME), relative absolute mean error (RAME) and Bland-Altman analysis was used to assess its accuracy. RESULTS: The average AME and RAME were 11 ±â€¯3 ms and 3.07 ±â€¯1.33 %, respectively, for IC and 29 ±â€¯11 ms and 7.27 ±â€¯2.92 %, respectively, for TO. Alterations of the walking movement, such as turns and types of running, slightly reduced the accuracy of IC and TO detection. In comparison to previous methods, increased or comparable accuracies for both IC and TO detection are shown. SIGNIFICANCE: The study shows that the proposed algorithm is capable of detecting gait events for a variety of locomotion tasks by means of a single gyroscope located on the shank. In consequence, the algorithm can be applied to activities, which consist of various movements (e.g., soccer). Ultimately, this extends the use of mobile sensor-based gait analysis.

A Machine Learning and Wearable Sensor Based Approach to Estimate External Knee Flexion and Adduction Moments During Various Locomotion Tasks.

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.

Estimation of Knee Joint Forces in Sport Movements Using Wearable Sensors and Machine Learning.

Knee joint forces (KJF) are biomechanical measures used to infer the load on knee joint structures. The purpose of this study is to develop an artificial neural network (ANN) that estimates KJF during sport movements, based on data obtained by wearable sensors. Thirteen participants were equipped with two inertial measurement units (IMUs) located on the right leg. Participants performed a variety of movements, including linear motions, changes of direction, and jumps. Biomechanical modelling was carried out to determine KJF. An ANN was trained to model the association between the IMU signals and the KJF time series. The ANN-predicted KJF yielded correlation coefficients that ranged from 0.60 to 0.94 (vertical KJF), 0.64 to 0.90 (anterior-posterior KJF) and 0.25 to 0.60 (medial-lateral KJF). The vertical KJF for moderate running showed the highest correlation (0.94 ± 0.33). The summed vertical KJF and peak vertical KJF differed between calculated and predicted KJF across all movements by an average of 5.7% ± 5.9% and 17.0% ± 13.6%, respectively. The vertical and anterior-posterior KJF values showed good agreement between ANN-predicted outcomes and reference KJF across most movements. This study supports the use of wearable sensors in combination with ANN for estimating joint reactions in sports applications.

Towards a wearable monitoring tool for in-field ice hockey skating performance analysis.

The capturing of movements by means of wearable sensors has become increasingly popular in order to obtain sport performance measures during training or competition. The purpose of the current study was to investigate the feasibility of using body worn accelerometers to identify previous highlighted performance related biomechanical changes in terms of substantial differences across skill levels and skating phases. Twenty-two ice hockey players of different caliber were equipped with two 3D accelerometers, located on the skate and the waist, as they performed 30 m forward skating sprints on an ice rink. Two measures of the temporal stride characteristics (contact time and stride time) and one measure of the propulsive power (stride propulsion) of a skating stride were calculated and checked for discriminating effects across (i) skill levels and (ii) sprint phases as well as for their (iii) strength of association with the sprint performance (total sprint time). High caliber players showed an increased stride propulsion (+22%, P < 0.05) and shorter contact time (-5%, P < 0.05). All three analysed variables highlighted substantial biomechanical differences between the accelerative and constant velocity phases (P < 0.05). Stride propulsion of acceleration strides primarily correlated to total sprint time (r = -0.57, P < 0.05). The results demonstrate the potential of accelerometers to assess skating technique elements such as contact time or elements characterizing the propulsive power such as center of mass acceleration, to gauge skating performance. Thus, the findings of this study might contribute to establishing wearable sensors for in-field ice hockey skating performance analysis.

A Novel Approach to Determine Strides, Ice Contact, and Swing Phases During Ice Hockey Skating Using a Single Accelerometer.

This study presents a new approach for automated identification of ice hockey skating strides and a method to detect ice contact and swing phases of individual strides by quantifying vibrations in 3D acceleration data during the blade-ice interaction. The strides of a 30-m forward sprinting task, performed by 6 ice hockey players, were evaluated using a 3D accelerometer fixed to a hockey skate. Synchronized plantar pressure data were recorded as reference data. To determine the accuracy of the new method on a range of forward stride patterns for temporal skating events, estimated contact times and stride times for a sequence of 5 consecutive strides was validated. Bland-Altman limits of agreement (95%) between accelerometer and plantar pressure derived data were less than 0.019 s. Mean differences between the 2 capture methods were shown to be less than 1 ms for contact and stride time. These results demonstrate the validity of the novel approach to determine strides, ice contact, and swing phases during ice hockey skating. This technology is accurate, simple, effective, and allows for in-field ice hockey testing.