Reliability of motion phase identification for long-track speed skating using inertial measurement units.

Background: Precise identification of motion phases in long-track speed skating is critical to characterize and optimize performance. This study aimed to estimate the intra- and inter-rater reliability of movement phase identification using inertial measurement units (IMUs) in long-track speed skating. Methods: We analyzed 15 skaters using IMUs attached to specific body locations during a 500m skate, focusing on the stance phase, and identifying three movement events: Onset, Edge-flip, and Push-off. Reliability was assessed using intraclass correlation coefficients (ICC) and Bland-Altman analysis. Results: Results showed high intra- and inter-rater reliability (ICC [1,1]: 0.86 to 0.99; ICC [2,1]: 0.81 to 0.99) across all events. Absolute error ranged from 0.56 to 6.15 ms and from 0.92 to 26.29 ms for intra- and inter-rater reliability, respectively. Minimally detectable change (MDC) ranged from 17.56 to 62.22 ms and from 33.23 to 131.25 ms for intra- and inter-rater reliability, respectively. Discussion: Despite some additive and proportional errors, the overall error range was within acceptable limits, indicating negligible systematic errors. The measurement error range was small, demonstrating the accuracy of IMUs. IMUs demonstrate high reliability in movement phase identification during speed skating, endorsing their application in sports science for enhanced kinematic studies and training.

Application of stretchable strain sensors and an inertial measurement unit for simulative tension analysis of the calcaneofibular ligament in formalin-fixed cadavers.

BACKGROUND: The calcaneofibular ligament, a component of the lateral ligament complex of the ankle joint, plays an essential role in ankle-joint stability. To understand the mechanism of sprain-induced calcaneofibular ligament injury, the effect of ankle positions on calcaneofibular ligament tension needs to be ascertained. METHODS: We propose a convenient method that combines stretchable strain sensors and an inertial measurement unit for simulative tension analysis of the calcaneofibular ligament in formalin-fixed cadavers. The stretchable strain sensor was pre-stretched approximately 1.3 times and, then set along the direction of the calcaneofibular ligament; a capacitance value from the sensor was used as a parameter to reflect the tension generated. Accurate three-axial inertial measurement unit-based monitoring of joint angles was undertaken for ten cadaveric ankles in measurements at 10° intervals from 30° plantarflexion to 20° dorsiflexion, followed by the investigation of additional effects with 10° inversion and eversion. FINDINGS: Two-way repeated-measures ANOVA revealed a significant interactive effect for plantar/dorsiflexion × inversion/eversion and main effects for plantar/dorsiflexion and inversion/eversion. Post hoc pairwise analysis confirmed that 20° dorsiflexion or 10° inversion induces tension, whereas 10° eversion causes relaxation. Moreover, a promotional interactive effect by 20° dorsiflexion and 10° inversion and an offsetting effect by 10° eversion to 20° dorsiflexion were revealed. The measured values showed high levels of reliability and reproducibility (intraclass correlation coefficient [1,1] = 0.89). INTERPRETATION: These results appropriately demonstrate the tensile action of calcaneofibular ligament. The novel approach investigated herein potentially opens new avenues for precise ligament-function evaluation.

Association between movement speed and instability catch kinematics and the differences between individuals with and without chronic low back pain.

Studies reported the existence of instability catch (IC) during trunk flexion in patients with chronic low back pain (CLBP). However, different movement speeds can cause different neuromuscular demands resulting in altered kinematic patterns. In addition, kinematic characterization corresponding to clinical observation of IC is still limited. Therefore, this study aimed to determine (1) the association between movement speed and kinematic parameters representing IC during trunk flexion and (2) the differences in kinematic parameters between individuals with and without CLBP. Fifteen no low back pain (NoLBP) and 15 CLBP individuals were recruited. Inertial measurement units (IMU) were attached to T3, L1, and S2 spinous processes. Participants performed active trunk flexion while IMU data were simultaneously collected. Total trunk, lumbar, and pelvic mean angular velocity (T_MV, L_MV, and P_MV), as well as number of zero-crossings, peak-to-peak, and area of sudden deceleration and acceleration (Num, P2P, and Area), were derived. Pearson's correlation tests were used to determine the association between T_MV and L_MV, P_MV, Num, P2P, and Area. An ANCOVA was performed to determine the difference in kinematic parameters between groups using movement speed as a covariate. Significant associations (P < 0.05) were found between movement speed and other kinematic parameters, except for Area. Results showed that L_MV significantly differed from the P_MV (P = 0.002) in the CLBP group, while a significant between-group difference (P = 0.037) was found in the P_MV. Additionally, significant between-group differences (P < 0.05) in P2P and Area were observed. The associations between movement speed and kinematic parameters suggest that movement speed changes can alter kinematic patterns. Therefore, clinicians may challenge lumbopelvic neuromuscular control by modifying movement speed to elicit greater change in kinematic patterns. In addition, the NoLBP group used shared lumbar and pelvic contributions, while the CLBP group used less pelvic contribution. Finally, P2P and Area appeared to offer the greatest sensitivity to differentiate between the groups. Overall, these findings may enhance the understanding of the mechanism underlying IC in CLBP.

Balance Assessment Using a Handheld Smartphone with Principal Component Analysis for Anatomical Calibration.

Most balance assessment studies using inertial measurement units (IMUs) in smartphones use a body strap and assume the alignment of the smartphone with the anatomical axes. To replace the need for a body strap, we have used an anatomical alignment method that employs a calibration maneuver and Principal Component Analysis (PCA) so that the smartphone can be held by the user in a comfortable position. The objectives of this study were to determine if correlations existed between angular velocity scores derived from a handheld smartphone with PCA functional alignment vs. a smartphone placed in a strap with assumed alignment, and to analyze acceleration score differences across balance poses of increasing difficulty. The handheld and body strap smartphones exhibited moderately to strongly correlated angular velocity scores in the calibration maneuver (r = 0.487-0.983, p < 0.001). Additionally, the handheld smartphone with PCA functional calibration successfully detected significant variance between pose type scores for anteroposterior, mediolateral, and superoinferior acceleration data (p < 0.001).

The Cumulative Impacts of Fatigue during Overload Training Can Be Tracked Using Field-Based Monitoring of Running Stride Interval Correlations.

Integrating running gait coordination assessment into athlete monitoring systems could provide unique insight into training tolerance and fatigue-related gait alterations. This study investigated the impact of an overload training intervention and recovery on running gait coordination assessed by field-based self-testing. Fifteen trained distance runners were recruited to perform 1-week of light training (baseline), 2 weeks of heavy training (high intensity, duration, and frequency) designed to overload participants, and a 10-day light taper to allow recovery and adaptation. Field-based running assessments using ankle accelerometry and online short recovery and stress scale (SRSS) surveys were completed daily. Running performance was assessed after each training phase using a maximal effort multi-stage running test-to-exhaustion (RTE). Gait coordination was assessed using detrended fluctuation analysis (DFA) of a stride interval time series. Two participants withdrew during baseline training due to changed personal circumstances. Four participants withdrew during heavy training due to injury. The remaining nine participants completed heavy training and were included in the final analysis. Heavy training reduced DFA values (standardised mean difference (SMD) = -1.44 ± 0.90; p = 0.004), recovery (SMD = -1.83 ± 0.82; p less than 0.001), performance (SMD = -0.36 ± 0.32; p = 0.03), and increased stress (SMD = 1.78 ± 0.94; p = 0.001) compared to baseline. DFA values (p = 0.73), recovery (p = 0.77), and stress (p = 0.73) returned to baseline levels after tapering while performance trended towards improvement from baseline (SMD = 0.28 ± 0.37; p = 0.13). Reduced DFA values were associated with reduced performance (r2 = 0.55) and recovery (r2 = 0.55) and increased stress (r2 = 0.62). Field-based testing of running gait coordination is a promising method of monitoring training tolerance in running athletes during overload training.

The Utility of Calibrating Wearable Sensors before Quantifying Infant Leg Movements.

While interest in using wearable sensors to measure infant leg movement is increasing, attention should be paid to the characteristics of the sensors. Specifically, offset error in the measurement of gravitational acceleration (g) is common among commercially available sensors. In this brief report, we demonstrate how we measured the offset and other errors in three different off-the-shelf wearable sensors available to professionals and how they affected a threshold-based movement detection algorithm for the quantification of infant leg movement. We describe how to calibrate and correct for these offsets and how conducting this improves the reproducibility of results across sensors.

A Novel Method and System Implementation for Precise Estimation of Single-Axis Rotational Angles.

Accurately estimating single-axis rotational angle changes is crucial in many high-tech domains. However, traditional angle measurement techniques are often constrained by sensor limitations and environmental interferences, resulting in significant deficiencies in precision and stability. Moreover, current methodologies typically rely on fixed-axis rotation models, leading to substantial discrepancies between measured and actual angles due to axis misalignment. To address these issues, this paper proposes an innovative method for single-axis rotational angle estimation. It introduces a calibration technique for installation errors between inertial measurement units and the overall measurement system, effectively translating dynamic rotational inertial outputs to system enclosure outputs. Subsequently, the method employs triaxial accelerometers combined with zero-velocity detection technology to estimate the rotation axis position. Finally, it delves into analyzing the relationship between quaternion and axis-angle, aimed at reducing noise interference for precise rotational angle estimation. Based on this proposed methodology, a Low-Cost, a High Accuracy Measurement System (HAMS) integrating sensor fusion was designed and implemented. Experimental results demonstrate static measurement errors below ±0.15° and dynamic measurement errors below ±0.5° within a ±180° range.

Validity of Valor Inertial Measurement Unit for Upper and Lower Extremity Joint Angles.

Inertial measurement units (IMU) are increasingly utilized to capture biomechanical measures such as joint kinematics outside traditional biomechanics laboratories. These wearable sensors have been proven to help clinicians and engineers monitor rehabilitation progress, improve prosthesis development, and record human performance in a variety of settings. The Valor IMU aims to offer a portable motion capture alternative to provide reliable and accurate joint kinematics when compared to industry gold standard optical motion capture cameras. However, IMUs can have disturbances in their measurements caused by magnetic fields, drift, and inappropriate calibration routines. Therefore, the purpose of this investigation is to validate the joint angles captured by the Valor IMU in comparison to an optical motion capture system across a variety of movements. Our findings showed mean absolute differences between Valor IMU and Vicon motion capture across all subjects' joint angles. The tasks ranged from 1.81 degrees to 17.46 degrees, the root mean squared errors ranged from 1.89 degrees to 16.62 degrees, and interclass correlation coefficient agreements ranged from 0.57 to 0.99. The results in the current paper further promote the usage of the IMU system outside traditional biomechanical laboratories. Future examinations of this IMU should include smaller, modular IMUs with non-slip Velcro bands and further validation regarding transverse plane joint kinematics such as joint internal/external rotations.

Design and Modelling of MEMS Vibrating Internal Ring Gyroscopes for Harsh Environments.

This paper presents a design, model, and comparative analysis of two internal MEMS vibrating ring gyroscopes for harsh environmental conditions. The proposed design investigates the symmetric structure of the vibrating ring gyroscopes that operate at the identical shape of wine glass mode resonance frequencies for both driving and sensing purposes. This approach improves the gyroscope's sensitivity and precision in rotational motion. The analysis starts with an investigation of the dynamic behaviour of the vibrating ring gyroscope with the detailed derivation of motion equations. The design geometry, meshing technology, and simulation results were comprehensively evaluated on two internal vibrating ring gyroscopes. The two designs are distinguished by their support spring configurations and internal ring structures. Design I consists of eight semicircular support springs and Design II consists of sixteen semicircular support springs. These designs were modelled and analyzed using finite element analysis (FEA) in Ansys 2023 R1 software. This paper further evaluates static and dynamic performance, emphasizing mode matching and temperature stability. The results reveal that Design II, with additional support springs, offers better mode matching, higher resonance frequencies, and better thermal stability compared to Design I. Additionally, electrostatic, modal, and harmonic analyses highlight the gyroscope's behaviour under varying DC voltages and environmental conditions. Furthermore, this study investigates the impact of temperature fluctuations on performance, demonstrating the robustness of the designs within a temperature range from -100 °C to 100 °C. These research findings suggest that the internal vibrating ring gyroscopes are highly suitable for harsh conditions such as high temperature and space applications.

Multivariable model for gait pattern differentiation in elderly patients with hip and knee osteoarthritis: A wearable sensor approach.

Background: Hip and knee osteoarthritis (OA) patients demonstrate distinct gait patterns, yet detecting subtle abnormalities with wearable sensors remains uncertain. This study aimed to assess a predictive model's efficacy in distinguishing between hip and knee OA gait patterns using accelerometer data. Method: Participants with hip or knee OA underwent overground walking assessments, recording lower limb accelerations for subsequent time and frequency domain analyses. Logistic regression with regularization identified associations between frequency domain features of acceleration signals and OA, and k-nearest neighbor classification distinguished knee and hip OA based on selected acceleration signal features. Findings: We included 57 knee OA patients (30 females, median age 68 [range 49-89], median BMI 29.7 [range 21.0-45.9]) and 42 hip OA patients (19 females, median age 70 [range 47-89], median BMI 28.3 [range 20.4-37.2]). No significant difference could be found in the time domain's averaged shape of acceleration signals. However, in the frequency domain, five selected features showed a diagnostic ability to differentiate between knee and hip OA. Using these features, a model achieved a 77 % accuracy in classifying gait cycles into hip or knee OA groups, with average precision, recall, and F1 score of 77 %, 76 %, and 78 %, respectively. Interpretation: The study demonstrates the effectiveness of wearable sensors in differentiating gait patterns between individuals with hip and knee OA, specifically in the frequency domain. The results highlights the promising potential of wearable sensors and advanced signal processing techniques for objective assessment of OA in clinical settings.

Application of IMU/GPS Integrated Navigation System Based on Adaptive Unscented Kalman Filter Algorithm in 3D Positioning of Forest Rescue Personnel.

Utilizing reliable and accurate positioning and navigation systems is crucial for saving the lives of rescue personnel and accelerating rescue operations. However, Global Navigation Satellite Systems (GNSSs), such as GPS, may not provide stable signals in dense forests. Therefore, integrating multiple sensors like GPS and Inertial Measurement Units (IMUs) becomes essential to enhance the availability and accuracy of positioning systems. To accurately estimate rescuers' positions, this paper employs the Adaptive Unscented Kalman Filter (AUKF) algorithm with measurement noise variance matrix adaptation, integrating IMU and GPS data alongside barometric altitude measurements for precise three-dimensional positioning in complex environments. The AUKF enhances estimation robustness through the adaptive adjustment of the measurement noise variance matrix, particularly excelling when GPS signals are interrupted. This study conducted tests on two-dimensional and three-dimensional road scenarios in forest environments, confirming that the AUKF-algorithm-based integrated navigation system outperforms the traditional Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Adaptive Extended Kalman Filter (AEKF) in emergency rescue applications. The tests further evaluated the system's navigation performance on rugged roads and during GPS signal interruptions. The results demonstrate that the system achieves higher positioning accuracy on rugged forest roads, notably reducing errors by 18.32% in the north direction, 8.51% in the up direction, and 3.85% in the east direction compared to the EKF. Furthermore, the system exhibits good adaptability during GPS signal interruptions, ensuring continuous and accurate personnel positioning during rescue operations.

A robust multimodal detection system: physical exercise monitoring in long-term care environments.

Introduction: Falls are a major cause of accidents that can lead to serious injuries, especially among geriatric populations worldwide. Ensuring constant supervision in hospitals or smart environments while maintaining comfort and privacy is practically impossible. Therefore, fall detection has become a significant area of research, particularly with the use of multimodal sensors. The lack of efficient techniques for automatic fall detection hampers the creation of effective preventative tools capable of identifying falls during physical exercise in long-term care environments. The primary goal of this article is to examine the benefits of using multimodal sensors to enhance the precision of fall detection systems. Methods: The proposed paper combines time-frequency features of inertial sensors with skeleton-based modeling of depth sensors to extract features. These multimodal sensors are then integrated using a fusion technique. Optimization and a modified K-Ary classifier are subsequently applied to the resultant fused data. Results: The suggested model achieved an accuracy of 97.97% on the UP-Fall Detection dataset and 97.89% on the UR-Fall Detection dataset. Discussion: This indicates that the proposed model outperforms state-of-the-art classification results. Additionally, the proposed model can be utilized as an IoT-based solution, effectively promoting the development of tools to prevent fall-related injuries.

Sensing and Control Strategies Used in FES Systems Aimed at Assistance and Rehabilitation of Foot Drop: A Systematic Literature Review.

Functional electrical stimulation (FES) is a rehabilitation and assistive technique used for stroke survivors. FES systems mainly consist of sensors, a control algorithm, and a stimulation unit. However, there is a critical need to reassess sensing and control techniques in FES systems to enhance their efficiency. This SLR was carried out following the PRISMA 2020 statement. Four databases (PubMed, Scopus, Web of Science, Wiley Online Library) from 2010 to 2024 were searched using terms related to sensing and control strategies in FES systems. A total of 322 articles were chosen in the first stage, while only 60 of them remained after the final filtering stage. This systematic review mainly focused on sensor techniques and control strategies to deliver FES. The most commonly used sensors reported were inertial measurement units (IMUs), 45% (27); biopotential electrodes, 36.7% (22); vision-based systems, 18.3% (11); and switches, 18.3% (11). The control strategy most reported is closed-loop; however, most of the current commercial FES systems employ open-loop strategies due to their simplicity. Three main factors were identified that should be considered when choosing a sensor for gait-oriented FES systems: wearability, accuracy, and affordability. We believe that the combination of computer vision systems with artificial intelligence-based control algorithms can contribute to the development of minimally invasive and personalized FES systems for the gait rehabilitation of patients with FDS.

Kinematic changes in dairy cows with induced hindlimb lameness: transferring methodology from the field of equine biomechanics.

Lameness is a common issue on dairy farms, with serious implications for economy and animal welfare. Affected animals may be overlooked until their condition becomes severe. Thus, improved lameness detection methods are needed. In this study, we describe kinematic changes in dairy cows with induced, mild to moderate hindlimb lameness in detail using a "whole-body approach". Thereby, we aimed to identify explicable features to discriminate between lame and non-lame animals for use in future automated surveillance systems. For this purpose, we induced a mild to moderate and fully reversible hindlimb lameness in 16 dairy cows. We obtained 41 straight-line walk measurements (containing > 3 000 stride cycles) using 11 inertial measurement units attached to predefined locations on the cows' upper body and limbs. One baseline and ≥ 1 induction measurement(s) were obtained from each cow. Thirty-one spatial and temporal parameters related to limb movement and inter-limb coordination, upper body vertical displacement symmetry and range of motion (ROMz), as well as pelvic pitch and roll, were calculated on a stride-by-stride basis. For upper body locations, vertical within-stride movement asymmetry was investigated both by calculating within-stride differences between local extrema, and by a signal decomposition approach. For each parameter, the baseline condition was compared with induction condition in linear mixed-effect models, while accounting for stride duration. Significant difference between baseline and induction condition was seen for 23 out of 31 kinematic parameters. Lameness induction was associated with decreased maximum protraction (-5.8%) and retraction (-3.7%) angles of the distal portion of the induced/non-induced limb respectively. Diagonal and lateral dissociation of foot placement (ratio of stride duration) involving the non-induced limb decreased by 8.8 and 4.4%, while diagonal dissociation involving the induced limb increased by 7.7%. Increased within-stride vertical displacement asymmetry of the poll, neck, withers, thoracolumbar junction (back) and tubera sacrale (TS) were seen. This was most notable for the back and poll, where a 40 and 24% increase of the first harmonic amplitude (asymmetric component) and 27 and 14% decrease of the second harmonic amplitude (symmetric component) of vertical displacement were seen. ROMz increased in all these landmarks except for TS. Changes in pelvic roll main components, but not in the range of motion of either pitch or roll angle per stride, were seen. Thus, we identified several kinematic features which may be used in future surveillance systems. Further studies are needed to determine their usefulness in realistic conditions, and to implement methods on farms.

Research on Positioning and Navigation System of Greenhouse Mobile Robot Based on Multi-Sensor Fusion.

The labor shortage and rising costs in the greenhouse industry have driven the development of automation, with the core of autonomous operations being positioning and navigation technology. However, precise positioning in complex greenhouse environments and narrow aisles poses challenges to localization technologies. This study proposes a multi-sensor fusion positioning and navigation robot based on ultra-wideband (UWB), an inertial measurement unit (IMU), odometry (ODOM), and a laser rangefinder (RF). The system introduces a confidence optimization algorithm based on weakening non-line-of-sight (NLOS) for UWB positioning, obtaining calibrated UWB positioning results, which are then used as a baseline to correct the positioning errors generated by the IMU and ODOM. The extended Kalman filter (EKF) algorithm is employed to fuse multi-sensor data. To validate the feasibility of the system, experiments were conducted in a Chinese solar greenhouse. The results show that the proposed NLOS confidence optimization algorithm significantly improves UWB positioning accuracy by 60.05%. At a speed of 0.1 m/s, the root mean square error (RMSE) for lateral deviation is 0.038 m and for course deviation is 4.030°. This study provides a new approach for greenhouse positioning and navigation technology, achieving precise positioning and navigation in complex commercial greenhouse environments and narrow aisles, thereby laying a foundation for the intelligent development of greenhouses.

Knee sleeves improve gait symmetry during fast walking in older adults.

Knee sleeves are commonly used to address knee-related concerns, particularly in older individuals. Although previous studies have demonstrated their efficacy in improving gait and functional outcomes in knees with pathological conditions, the effectiveness of knee sleeves for improving gait characteristics in healthy older adults remains unclear. The harmonic ratio (HR), an index for assessing gait symmetry commonly used to discriminate between individuals with different functional levels, can be used to detect alterations in gait characteristics. This study investigated the effects of knee sleeves on gait symmetry in healthy older adults. Sixteen healthy community-dwelling older adults walked barefoot with and without knee sleeves at normal and fast speeds. Gait symmetry indices (HR and improved HR [iHR]) and spatiotemporal gait parameters were compared under different conditions. A significant interaction between knee condition and walking speed was observed for mean iHR in the anteroposterior direction (p = 0.006). A significant simple main effect of knee condition was found during fast walking, with a larger iHR with knee sleeves than without (p = 0.002). In the condition without knee sleeves, the iHR was significantly lower during fast walking than during normal walking (p = 0.035). Furthermore, a significant main effect of knee condition was observed for the variability of iHR in the anteroposterior direction, with a smaller variability when walking with knee sleeves than when walking without (p = 0.006). These results suggest that knee sleeves may enhance gait symmetry along the anteroposterior direction, particularly during fast walking, where symmetry disruption is more likely than walking at a comfortable pace. A significant reduction in gait symmetry variability also suggests a stabilizing effect on gait dynamics. These findings provide the first evidence supporting the efficacy of knee sleeves for improving gait symmetry. The use of knee sleeves could be a valuable option for restoring disrupted gait symmetry during fast walking, with potential implications for reducing the risk of falls.

Achieving More with Less: A Lightweight Deep Learning Solution for Advanced Human Activity Recognition (HAR).

Human activity recognition (HAR) is a crucial task in various applications, including healthcare, fitness, and the military. Deep learning models have revolutionized HAR, however, their computational complexity, particularly those involving BiLSTMs, poses significant challenges for deployment on resource-constrained devices like smartphones. While BiLSTMs effectively capture long-term dependencies by processing inputs bidirectionally, their high parameter count and computational demands hinder practical applications in real-time HAR. This study investigates the approximation of the computationally intensive BiLSTM component in a HAR model by using a combination of alternative model components and data flipping augmentation. The proposed modifications to an existing hybrid model architecture replace the BiLSTM with standard and residual LSTM, along with convolutional networks, supplemented by data flipping augmentation to replicate the context awareness typically provided by BiLSTM networks. The results demonstrate that the residual LSTM (ResLSTM) model achieves superior performance while maintaining a lower computational complexity compared to the traditional BiLSTM model. Specifically, on the UCI-HAR dataset, the ResLSTM model attains an accuracy of 96.34% with 576,702 parameters, outperforming the BiLSTM model's accuracy of 95.22% with 849,534 parameters. On the WISDM dataset, the ResLSTM achieves an accuracy of 97.20% with 192,238 parameters, compared to the BiLSTM's 97.23% accuracy with 283,182 parameters, demonstrating a more efficient architecture with minimal performance trade-off. For the KU-HAR dataset, the ResLSTM model achieves an accuracy of 97.05% with 386,038 parameters, showing comparable performance to the BiLSTM model's 98.63% accuracy with 569,462 parameters, but with significantly fewer parameters.

Biomechanical and Physiological Variables in Dynamic and Functional Balance Control during Single-Leg Loading in Individuals with Chronic Ankle Instability: A Scoping Review.

BACKGROUND: This scoping review summarizes the tasks and outcomes in dynamic and functional balance assessments of individuals with chronic ankle instability, focusing on the physiological and biomechanical characteristics. METHOD: A comprehensive literature search was conducted in PubMed, Scopus, Web of Science, and MEDLINE databases in September 2023 and revised in April 2024. Studies evaluating dynamic and functional balance in chronic ankle instability using clinical tests, as well as biomechanical and physiological outcomes, were included. RESULTS: Out of 536 publications, 31 met the screening criteria. A history of ankle sprain was the main focus of the inclusion criteria (28 articles, 90%). The star excursion balance test, emphasizing maximum reach distance, was the most common quantitative task (12 articles, 66%). Physiological data mainly came from electromyography studies (7 articles, 23%), while biomechanical variables were often assessed through center of pressure studies using force plates (17 articles, 55%). CONCLUSIONS: The preferred quantitative clinical assessment was the star excursion balance test, focusing on normalized reach outcomes. Qualitative functional balance assessments emphasize landing activities and center of pressure displacement. Electromyography is commonly used to analyze the tibialis anterior and peroneus longus muscles. However, there is a lack of qualitative data on dynamic balance control, including morphological characteristics and the center of mass adaptation.

Serial kinematic analysis using inertial measurement units in growing dogs at risk of hip dysplasia.

In this study, 54 dogs were examined at regular intervals from 12 weeks to 15 months of age using a gait analysis system based on inertial measurement sensors. At the end of the study, the dogs were examined for hip dysplasia (HD) and elbow dysplasia (ED) under sedation and officially classified at the Dysplasia Commission in Zurich. Gait parameters which are characteristic for the gait pattern of dogs, were calculated according to recent publications. These parameters were analysed for variance throughout the entire study period and assigned to healthy dogs and those suffering from HD. The findings of the study show that dogs suffering from HD exhibit a more unsteady gait pattern, e.g. higher variance, as they grow.

Multimodal data for behavioural authentication in Internet of Things environments.

Identifying humans based on their behavioural patterns represents an attractive basis for access control as such patterns appear naturally, do not require a focused effort from the user side, and do not impose the additional burden of memorising passwords. One means of capturing behavioural patterns is through passive sensors laid out in a target environment. Thanks to the proliferation of the Internet of Things (IoT), sensing devices are already embedded in our everyday surroundings and represent a rich source of multimodal data. Nevertheless, collecting such data for authentication research purposes is challenging, as it entails management and synchronisation of a range of sensing devices, design of diverse tasks that would evoke different behaviour patterns, storage and pre-processing of data arriving from multiple sources, and the execution of long-lasting user activities. Consequently, to the best of our knowledge, no publicly available datasets suitable for behaviour-based authentication research exist. In this brief article, we describe the first multimodal dataset for behavioural authentication research collected in a sensor-enabled IoT setting. The dataset comprises of high-frequency accelerometer, gyroscope, and force sensor data collected from an office-like environment. In addition, the dataset contains 3D point clouds collected with wireless radar and electroencephalogram (EEG) readings from a wireless EEG cap worn by the study participants. Within the environment, 54 volunteers conducted 6 different tasks that were constructed to elicit different behaviours and different cognitive load levels, resulting in a total of 16 h of multimodal data. The richness of the dataset comprising 5 different sensing modalities, a variability of tasks including keyboard typing, hand gesturing, walking, and other activities, opens a range of opportunities for research in behaviour-based authentication, but also the understanding of the role of different tasks and cognitive load levels on human behaviour.