IMU Data-Driven and PCA-Based Approach to Establish Quantifiable and Practically Applicable Measures for V2 Technique Elements in Cross-Country Skiing.

Quantifying movement coordination in cross-country (XC) skiing, specifically the technique with its elemental forms, is challenging. Particularly, this applies when trying to establish a bidirectional transfer between scientific theory and practical experts' knowledge as expressed, for example, in ski instruction curricula. The objective of this study was to translate 14 curricula-informed distinct elements of the V2 ski-skating technique (horizontal and vertical posture, lateral tilt, head position, upper body rotation, arm swing, shoulder abduction, elbow flexion, hand and leg distance, plantar flexion, ski set-down, leg push-off, and gliding phase) into plausible, valid and applicable measures to make the technique training process more quantifiable and scientifically grounded. Inertial measurement unit (IMU) data of 10 highly experienced XC skiers who demonstrated the technique elements by two extreme forms each (e.g., anterior versus posterior positioning for the horizontal posture) were recorded. Element-specific principal component analyses (PCAs)-driven by the variance produced by the technique extremes-resulted in movement components that express quantifiable measures of the underlying technique elements. Ten measures were found to be sensitive in distinguishing between the inputted extreme variations using statistical parametric mapping (SPM), whereas for four elements the SPM did not detect differences (lateral tilt, plantar flexion, ski set-down, and leg push-off). Applicability of the established technique measures was determined based on quantifying individual techniques through them. The study introduces a novel approach to quantitatively assess V2 ski-skating technique, which might help to enhance technique feedback and bridge the communication gap that often exists between practitioners and scientists.

Improving the Jump Shots of U12 Junior Basketball Players by Implementing a Combined Program of Plyometric and Coordination Exercises Using MyVert Technology.

The aim of this study was to investigate the impact of the implementation of an experimental program with combined plyometric and coordination exercises for a time interval of 6 months aimed at improving the jump shots of U12 junior players through the use of information technologies. One hundred seventeen female basketball players, aged between 10 and 12 years (U12), participated in this study. The study subjects were divided into two groups: the experimental group (EG), with 60 (51.3%) subjects, and the control group (CG), with 57 subjects (48.7%). The 6-month experiment program implemented in the experimental group included exercises that combined coordination exercises with plyometric exercises in the execution of throwing skills and skills specific to the basketball game by using the MyVert portable smart sensor. This study included an initial test and a final test, in which three motor tests adapted to the specifics of the basketball game were applied in order to evaluate jump shots: a throw-after-step test, a standing shot test and a shot-after-dribbling test. Only the results of the experimental group showed statistically significant progress (p < 0.05) between the final and initial testing in all three motor tests for the following parameters: maximum jump height (cm), average jump height (cm), power (watts/kg) and successful shots (no). The gains of the control group were not statistically significant in any test. It should be noted that the number of throws scored in the basket of the experimental group increased significantly, a fact highlighted by the very large size of Cohen's value > 3 in all the tests of this study. The results of the experimental group as a result of the implementation of the experimental training program using MyVert technology were superior to the results of the control group. The practical implications of the present study will contribute to the optimization of the athletes' training methodology in order to improve the physical and technical levels in relation to the peculiarities of age and training level.

Sensitivity of principal component analysis outcomes to data pre-processing conditions when quantifying trial-to-trial variability in whole-body kinematics.

This study investigated whether modes of variance in trial-to-trial whole-body kinematic variability identified by principal component analysis (PCA) were consistent across data pre-processing conditions generated from a common dataset. Comparisons made included 1) when trajectory data was expressed in a global vs. local reference frame; 2) when the number of landmarks used to represent whole-body motion differed, and; 3) whether input trajectory data were normalized to participant stature. Varying data pre-processing conditions prior to PCA does not bias the total variance identified. However, it can influence how modes of variance are dispersed across PCs, which in turn, can influence interpretation.

Computational kinematics of dance: distinguishing hip hop genres.

Dance plays a vital role in human societies across time and culture, with different communities having invented different systems for artistic expression through movement (genres). Differences between genres can be described by experts in words and movements, but these descriptions can only be appreciated by people with certain background abilities. Existing dance notation schemes could be applied to describe genre-differences, however they fall substantially short of being able to capture the important details of movement across a wide spectrum of genres. Our knowledge and practice around dance would benefit from a general, quantitative and human-understandable method of characterizing meaningful differences between aspects of any dance style; a computational kinematics of dance. Here we introduce and apply a novel system for encoding bodily movement as 17 macroscopic, interpretable features, such as expandedness of the body or the frequency of sharp movements. We use this encoding to analyze Hip Hop Dance genres, in part by building a low-cost machine-learning classifier that distinguishes genre with high accuracy. Our study relies on an open dataset (AIST++) of pose-sequences from dancers instructed to perform one of ten Hip Hop genres, such as Breakdance, Popping, or Krump. For comparison we evaluate moderately experienced human observers at discerning these sequence's genres from movements alone (38% where chance = 10%). The performance of a baseline, Ridge classifier model was fair (48%) and that of the model resulting from our automated machine learning pipeline was strong (76%). This indicates that the selected features represent important dimensions of movement for the expression of the attitudes, stories, and aesthetic values manifested in these dance forms. Our study offers a new window into significant relations of similarity and difference between the genres studied. Given the rich, complex, and culturally shaped nature of these genres, the interpretability of our features, and the lightweight techniques used, our approach has significant potential for generalization to other movement domains and movement-related applications.

Dual Tasking Affects the Outcomes of Instrumented Timed up and Go, Sit-to-Stand, Balance, and 10-Meter Walk Tests in Stroke Survivors.

Stroke can impair mobility, with deficits more pronounced while simultaneously performing multiple activities. In this study, common clinical tests were instrumented with wearable motion sensors to study motor-cognitive interference effects in stroke survivors (SS). A total of 21 SS and 20 healthy controls performed the Timed Up and Go (TUG), Sit-to-Stand (STS), balance, and 10-Meter Walk (10MWT) tests under single and dual-task (counting backward) conditions. Calculated measures included total time and gait measures for TUG, STS, and 10MWT. Balance tests for both open and closed eyes conditions were assessed using sway, measured using the linear acceleration of the thorax, pelvis, and thighs. SS exhibited poorer performance with slower TUG (16.15 s vs. 13.34 s, single-task p < 0.001), greater sway in the eyes open balance test (0.1 m/s2 vs. 0.08 m/s2, p = 0.035), and slower 10MWT (12.94 s vs. 10.98 s p = 0.01) compared to the controls. Dual tasking increased the TUG time (~14%, p < 0.001), balance thorax sway (~64%, p < 0.001), and 10MWT time (~17%, p < 0.001) in the SS group. Interaction effects were minimal, suggesting similar dual-task costs. The findings demonstrate exaggerated mobility deficits in SS during dual-task clinical testing. Dual-task assessments may be more effective in revealing impairments. Integrating cognitive challenges into evaluation can optimize the identification of fall risks and personalize interventions targeting identified cognitive-motor limitations post stroke.

MocapMe: DeepLabCut-Enhanced Neural Network for Enhanced Markerless Stability in Sit-to-Stand Motion Capture.

This study examined the efficacy of an optimized DeepLabCut (DLC) model in motion capture, with a particular focus on the sit-to-stand (STS) movement, which is crucial for assessing the functional capacity in elderly and postoperative patients. This research uniquely compared the performance of this optimized DLC model, which was trained using 'filtered' estimates from the widely used OpenPose (OP) model, thereby emphasizing computational effectiveness, motion-tracking precision, and enhanced stability in data capture. Utilizing a combination of smartphone-captured videos and specifically curated datasets, our methodological approach included data preparation, keypoint annotation, and extensive model training, with an emphasis on the flow of the optimized model. The findings demonstrate the superiority of the optimized DLC model in various aspects. It exhibited not only higher computational efficiency, with reduced processing times, but also greater precision and consistency in motion tracking thanks to the stability brought about by the meticulous selection of the OP data. This precision is vital for developing accurate biomechanical models for clinical interventions. Moreover, this study revealed that the optimized DLC maintained higher average confidence levels across datasets, indicating more reliable and accurate detection capabilities compared with standalone OP. The clinical relevance of these findings is profound. The optimized DLC model's efficiency and enhanced point estimation stability make it an invaluable tool in rehabilitation monitoring and patient assessments, potentially streamlining clinical workflows. This study suggests future research directions, including integrating the optimized DLC model with virtual reality environments for enhanced patient engagement and leveraging its improved data quality for predictive analytics in healthcare. Overall, the optimized DLC model emerged as a transformative tool for biomechanical analysis and physical rehabilitation, promising to enhance the quality of patient care and healthcare delivery efficiency.

A Protocol for Comprehensive Analysis of Gait in Individuals with Incomplete Spinal Cord Injury.

This is a protocol for comprehensive analysis of gait and affecting factors in individuals with incomplete paraplegia due to spinal cord injury (SCI). A SCI is a devastating event affecting both sensory and motor functions. Due to better care, the SCI population is changing, with a greater proportion retaining impaired ambulatory function. Optimizing ambulatory function after SCI remains challenging. To investigate factors influencing optimal ambulation, a multi-professional research project was grounded with expertise from clinical rehabilitation, neurophysiology, and biomechanical engineering from Karolinska Institutet, the Spinalis Unit at Aleris Rehab Station (Sweden's largest center for specialized neurorehabilitation), and the Promobilia MoveAbility Lab at KTH Royal Institute of Technology. Ambulatory adults with paraplegia will be consecutively invited to participate. Muscle strength, sensitivity, and spasticity will be assessed, and energy expenditure, 3D movements, and muscle function (EMG) during gait and submaximal contractions will be analyzed. Innovative computational modeling and data-driven analyses will be performed, including the identification of clusters of similar movement patterns among the heterogeneous population and analyses that study the link between complex sensorimotor function and movement performance. These results may help optimize ambulatory function for persons with SCI and decrease the risk of secondary conditions during gait with a life-long perspective.

Characteristic Changes of the Stance-Phase Plantar Pressure Curve When Walking Uphill and Downhill: Cross-Sectional Study.

BACKGROUND: Monitoring of gait patterns by insoles is popular to study behavior and activity in the daily life of people and throughout the rehabilitation process of patients. Live data analyses may improve personalized prevention and treatment regimens, as well as rehabilitation. The M-shaped plantar pressure curve during the stance phase is mainly defined by the loading and unloading slope, 2 maxima, 1 minimum, as well as the force during defined periods. When monitoring gait continuously, walking uphill or downhill could affect this curve in characteristic ways. OBJECTIVE: For walking on a slope, typical changes in the stance phase curve measured by insoles were hypothesized. METHODS: In total, 40 healthy participants of both sexes were fitted with individually calibrated insoles with 16 pressure sensors each and a recording frequency of 100 Hz. Participants walked on a treadmill at 4 km/h for 1 minute in each of the following slopes: -20%, -15%, -10%, -5%, 0%, 5%, 10%, 15%, and 20%. Raw data were exported for analyses. A custom-developed data platform was used for data processing and parameter calculation, including step detection, data transformation, and normalization for time by natural cubic spline interpolation and force (proportion of body weight). To identify the time-axis positions of the desired maxima and minimum among the available extremum candidates in each step, a Gaussian filter was applied (σ=3, kernel size 7). Inconclusive extremum candidates were further processed by screening for time plausibility, maximum or minimum pool filtering, and monotony. Several parameters that describe the curve trajectory were computed for each step. The normal distribution of data was tested by the Kolmogorov-Smirnov and Shapiro-Wilk tests. RESULTS: Data were normally distributed. An analysis of variance with the gait parameters as dependent and slope as independent variables revealed significant changes related to the slope for the following parameters of the stance phase curve: the mean force during loading and unloading, the 2 maxima and the minimum, as well as the loading and unloading slope (all P<.001). A simultaneous increase in the loading slope, the first maximum and the mean loading force combined with a decrease in the mean unloading force, the second maximum, and the unloading slope is characteristic for downhill walking. The opposite represents uphill walking. The minimum had its peak at horizontal walking and values dropped when walking uphill and downhill alike. It is therefore not a suitable parameter to distinguish between uphill and downhill walking. CONCLUSIONS: While patient-related factors, such as anthropometrics, injury, or disease shape the stance phase curve on a longer-term scale, walking on slopes leads to temporary and characteristic short-term changes in the curve trajectory.

Upper Limbs Muscle Co-Contraction Changes Correlate With The Physical Motor Impairments in CMT.

Background: Subjects with Charcot-Marie-Tooth (CMT) disease show hands impairment which is a relevant problem affecting the quality of life. This symptom is related to muscle weakness and reduced motor coordination of the upper limb. However, most studies focus on lower limb impairment, therefore the investigation of upper limb disability is necessary to identify biomarkers able to monitor disease-specific features and to tailor rehabilitation. Objective: This study aimed at characterizing upper limb muscle co-contraction using the co-contraction index (CCI) in CMT population. Methods: Upper limb kinematic and electromyography (EMG) data were collected from fourteen CMT subjects (6-CMT1A and 8-CMT1X) during motor tasks typical of daily living activities. Rudolph's CCI was used to quantify muscle co-contraction of four muscle pairs acting on shoulder, elbow and wrist. All CMT subjects underwent clinical examination. Thirteen healthy subjects served as the normative reference (HC). Results: CMT1X and CMT1A showed a significant reduction in CCI for distal and proximal muscle pairs compared to HC. Furthermore, CMT1A showed greater values of CCI compared to CMT1X mainly for the axial and axial-to-proximal muscle pairs. Movement speed and smoothness were not altered compared to HC. In addition, EMG metrics showed moderate-to-strong significant correlations with clinical outcomes. Conclusions: CCI was able to quantify disease-specific deficits with respect to the normative reference, highlighting motor control alterations even before motor output impairment. CCI was also sensitive in detecting CMT subtypes-based differences and adopted compensatory strategies. Our findings suggest that CCI can be an outcome measure for CMT disease monitoring and interventional studies.

The relationship between manual ability, dystonia and choreoathetosis severity and upper limb movement patterns during reaching and grasping in children and young adults with dyskinetic cerebral palsy.

INTRODUCTION: Impaired upper limb movements are a key feature in dyskinetic cerebral palsy (CP). However, information on how specific movement patterns relate to manual ability, performance and underlying movement disorders is lacking. Insight in these associations may contribute to targeted upper limb management in dyskinetic CP. This study aimed to explore associations between deviant upper limb movement patterns and (1) manual ability, (2) severity of dystonia/choreoathetosis, and (3) movement time/trajectory deviation during reaching and grasping. PARTICIPANTS/METHODS: Participants underwent three-dimensional upper limb analysis during reaching forwards (RF), reaching sideways (RS) and reach-and-grasp vertical (RGV) as well as clinical assessment. Canonical correlation and regression analysis with statistical parametric mapping were used to explore associations between clinical/performance parameters and movement patterns (mean and variability). RESULTS: Thirty individuals with dyskinetic CP participated (mean age 16±5 y; 20 girls). Lower manual ability was related to higher variability in wrist flexion/extension during RF and RS early in the reaching cycle (p < 0.05). Higher dystonia severity was associated with higher mean wrist flexion (40-82 % of the reaching cycle; p = 0.004) and higher variability in wrist flexion/extension (31-75 %; p < 0.001) and deviation (2-14 %; p = 0.007/60-73 %; p = 0.006) during RF. Choreoathetosis severity was associated with higher elbow pro/supination variability (12-19 %; p = 0.009) during RGV. Trajectory deviation was associated with wrist and elbow movement variability (p < 0.05). CONCLUSION: Current novel analysis of upper limb movement patterns and respective timings allows to detect joint angles and periods in the movement cycle wherein associations with clinical parameters occur. These associations are not present at each joint level, nor during the full movement cycle. This knowledge should be considered for individualized treatment strategies.

Reliability of a qualitative movement assessment tool during a single-leg triple hop landing.

OBJECTIVE: Assess inter- and intra-rater reliability of the Qualitative Analysis of Single Leg Squat (QASLS) during a single-leg triple hop landing in subjects following anterior cruciate ligament reconstruction (ACLR). Explore if differences in reliability existed between novice and experienced clinicians. Determine if QASLS scores differed between the surgical and nonsurgical limbs. DESIGN: Repeated Measures. PARTICIPANTS: 20 subjects ≥6 months post-ACLR. METHODS: Subjects were recorded performing a single-leg triple hop bilaterally. Videos were independently rated by five raters (2 physical therapists and 3 physical therapy students). Intraclass correlation coefficient (ICC) was calculated to measure reliability of the QASLS on the surgical limb. Wilcoxon signed-rank test was utilized to assess if differences in QASLS scores existed between limbs. RESULTS: The cumulative inter-rater reliability was moderate (ICC (2,1): 0.703) and the cumulative intra-rater reliability was good (ICC (3,1): 0.857). Little difference was found between experienced and novice raters for inter- and intra-rater reliability. There was no statistically significant difference in QASLS scores between limbs (P = 0.64). CONCLUSION: The QASLS tool offers moderate inter- and good intra-rater reliability for evaluating movement quality during a single-leg triple hop landing, irrespective of rater experience. Additionally, there was no observed difference in QASLS scores between surgical and nonsurgical limbs.

A 2D video-based assessment is associated with 3D biomechanical contributors to dynamic knee valgus in the coronal plane.

Introduction: Adolescent athletes involved in sports that involve cutting and landing maneuvers have an increased risk of anterior cruciate ligament (ACL) tears, highlighting the importance of identifying risky movement patterns such as dynamic knee valgus (DKV). Qualitative movement screenings have explored two-dimensional (2D) scoring criteria for DKV, however, there remains limited data on the validity of these screening tools. Determining a 2D scoring criterion for DKV that closely aligns with three-dimensional (3D) biomechanical measures will allow for the identification of poor knee position in adolescent athletes on a broad scale. The purpose of this study was to establish a 2D scoring criterion that corresponds to 3D biomechanical measures of DKV. Methods: A total of 41 adolescent female club volleyball athletes performed a three-task movement screen consisting of a single-leg squat (SLS), single-leg drop landing (SLDL), and double-leg vertical jump (DLVJ). A single rater scored 2D videos of each task using four criteria for poor knee position. A motion capture system was used to calculate 3D joint angles, including pelvic obliquity, hip adduction, knee abduction, ankle eversion, and foot progression angle. Receiver operating characteristic curves were created for each 2D scoring criterion to determine cut points for the presence of movement faults, and areas under the curve (AUC) were computed to describe the accuracy of each 2D criterion compared to 3D biomechanical data. Results: 3D measures indicated knee abduction angles between 2.4°-4.6° (SD 4.1°-4.3°) at the time point when the center of the knee joint was most medial during the three tasks. AUCs were between 0.62 and 0.93 across scoring items. The MEDIAL scoring item, defined as the knee joint positioned inside the medial border of the shoe, demonstrated the greatest association to components of DKV, with AUCs ranging from 0.67 to 0.93. Conclusion: The MEDIAL scoring criterion demonstrated the best performance in distinguishing components of DKV, specifically pelvic obliquity, hip adduction, ankle eversion, and foot progression. Along with the previously published scoring definitions for trunk-specific risk factors, the authors suggest that the MEDIAL criterion may be the most indicative of DKV, given an association with 3D biomechanical risk factors.

Post-Stroke Functional Changes: In-Depth Analysis of Clinical Tests and Motor-Cognitive Dual-Tasking Using Wearable Sensors.

Clinical tests like Timed Up and Go (TUG) facilitate the assessment of post-stroke mobility, but they lack detailed measures. In this study, 21 stroke survivors and 20 control participants underwent TUG, sit-to-stand (STS), and the 10 Meter Walk Test (10MWT). Tests incorporated single tasks (STs) and motor-cognitive dual-task (DTs) involving reverse counting from 200 in decrements of 10. Eight wearable motion sensors were placed on feet, shanks, thighs, sacrum, and sternum to record kinematic data. These data were analyzed to investigate the effects of stroke and DT conditions on the extracted features across segmented portions of the tests. The findings showed that stroke survivors (SS) took 23% longer for total TUG (p < 0.001), with 31% longer turn time (p = 0.035). TUG time increased by 20% (p < 0.001) from STs to DTs. In DTs, turning time increased by 31% (p = 0.005). Specifically, SS showed 20% lower trunk angular velocity in sit-to-stand (p = 0.003), 21% longer 10-Meter Walk time (p = 0.010), and 18% slower gait speed (p = 0.012). As expected, turning was especially challenging and worsened with divided attention. The outcomes of our study demonstrate the benefits of instrumented clinical tests and DTs in effectively identifying motor deficits post-stroke across sitting, standing, walking, and turning activities, thereby indicating that quantitative motion analysis can optimize rehabilitation procedures.

Assessment of Spinal and Pelvic Kinematics Using Inertial Measurement Units in Clinical Subgroups of Persistent Non-Specific Low Back Pain.

Inertial measurement units (IMUs) offer a portable and quantitative solution for clinical movement analysis. However, their application in non-specific low back pain (NSLBP) remains underexplored. This study compared the spine and pelvis kinematics obtained from IMUs between individuals with and without NSLBP and across clinical subgroups of NSLBP. A total of 81 participants with NSLBP with flexion (FP; n = 38) and extension (EP; n = 43) motor control impairment and 26 controls (No-NSLBP) completed 10 repetitions of spine movements (flexion, extension, lateral flexion). IMUs were placed on the sacrum, fourth and second lumbar vertebrae, and seventh cervical vertebra to measure inclination at the pelvis, lower (LLx) and upper (ULx) lumbar spine, and lower cervical spine (LCx), respectively. At each location, the range of movement (ROM) was quantified as the range of IMU orientation in the primary plane of movement. The ROM was compared between NSLBP and No-NSLBP using unpaired t-tests and across FP-NSLBP, EP-NSLBP, and No-NSLBP subgroups using one-way ANOVA. Individuals with NSLBP exhibited a smaller ROM at the ULx (p = 0.005), LLx (p = 0.003) and LCx (p = 0.01) during forward flexion, smaller ROM at the LLx during extension (p = 0.03), and a smaller ROM at the pelvis during lateral flexion (p = 0.003). Those in the EP-NSLBP group had smaller ROM than those in the No-NSLBP group at LLx during forward flexion (Bonferroni-corrected p = 0.005), extension (p = 0.013), and lateral flexion (p = 0.038), and a smaller ROM at the pelvis during lateral flexion (p = 0.005). Those in the FP-NSLBP subgroup had smaller ROM than those in the No-NSLBP group at the ULx during forward flexion (p = 0.024). IMUs detected variations in kinematics at the trunk, lumbar spine, and pelvis among individuals with and without NSLBP and across clinical NSLBP subgroups during flexion, extension, and lateral flexion. These findings consistently point to reduced ROM in NSLBP. The identified subgroup differences highlight the potential of IMU for assessing spinal and pelvic kinematics in these clinically verified subgroups of NSLBP.

Eye movement analysis for real-world settings using segmented linear regression.

Eye movement analysis is critical to studying human brain phenomena such as perception, cognition, and behavior. However, under uncontrolled real-world settings, the recorded gaze coordinates (commonly used to track eye movements) are typically noisy and make it difficult to track change in the state of each phenomenon precisely, primarily because the expected change is usually a slower transient process. This paper proposes an approach, Improved Naive Segmented linear regression (INSLR), which approximates the gaze coordinates with a piecewise linear function (PLF) referred to as a hypothesis. INSLR improves the existing NSLR approach by employing a hypotheses clustering algorithm, which redefines the final hypothesis estimation in two steps: (1) At each time-stamp, measure the likelihood of each hypothesis in the candidate list of hypotheses by using the least square fit score and its distance from the k-means of the hypotheses in the list. (2) Filter hypothesis based on a pre-defined threshold. We demonstrate the significance of the INSLR method in addressing the challenges of uncontrolled real-world settings such as gaze denoising and minimizing gaze prediction errors from cost-effective devices like webcams. Experiment results show INSLR consistently outperforms the baseline NSLR in denoising noisy signals from three eye movement datasets and minimizes the error in gaze prediction from a low precision device for 71.1% samples. Furthermore, this improvement in denoising quality is further validated by the improved accuracy of the oculomotor event classifier called NSLR-HMM and enhanced sensitivity in detecting variations in attention induced by distractor during online lecture.

Mining Bodily Cues to Deception.

A significant body of research has investigated potential correlates of deception and bodily behavior. The vast majority of these studies consider discrete, subjectively coded bodily movements such as specific hand or head gestures. Such studies fail to consider quantitative aspects of body movement such as the precise movement direction, magnitude and timing. In this paper, we employ an innovative data mining approach to systematically study bodily correlates of deception. We re-analyze motion capture data from a previously published deception study, and experiment with different data coding options. We report how deception detection rates are affected by variables such as body part, the coding of the pose and movement, the length of the observation, and the amount of measurement noise. Our results demonstrate the feasibility of a data mining approach, with detection rates above 65%, significantly outperforming human judgement (52.80%). Owing to the systematic analysis, our analyses allow for an understanding of the importance of various coding factor. Moreover, we can reconcile seemingly discrepant findings in previous research. Our approach highlights the merits of data-driven research to support the validation and development of deception theory.

Gait Features in Different Environments Contributing to Participation in Outdoor Activities in Old Age (GaitAge): Protocol for an Observational Cross-Sectional Study.

BACKGROUND: The ability to walk is a key issue for independent old age. Optimizing older peoples' opportunities for an autonomous and active life and reducing health disparities requires a better understanding of how to support independent mobility in older people. With increasing age, changes in gait parameters such as step length and cadence are common and have been shown to increase the risk of mobility decline. However, gait assessments are typically based on laboratory measures, even though walking in a laboratory environment may be significantly different from walking in outdoor environments. OBJECTIVE: This project will study alterations in biomechanical features of gait by comparing walking on a treadmill in a laboratory, level outdoor, and hilly outdoor environments. In addition, we will study the possible contribution of changes in gait between these environments to outdoor mobility among older people. METHODS: Participants of the study were recruited through senior organizations of Central Finland and the University of the Third Age, Jyväskylä. Inclusion criteria were community-dwelling, aged 70 years and older, able to walk at least 1 km without assistive devices, able to communicate, and living in central Finland. Exclusion criteria were the use of mobility devices, severe sensory deficit (vision and hearing), memory impairment (Mini-Mental State Examination ≤23), and neurological conditions (eg, stroke, Parkinson disease, and multiple sclerosis). The study protocol included 2 research visits. First, indoor measurements were conducted, including interviews (participation, health, and demographics), physical performance tests (short physical performance battery and Timed Up and Go), and motion analysis on a treadmill in the laboratory (3D Vicon and next-generation inertial measurement units [NGIMUs]). Second, outdoor walking tests were conducted, including walking on level (sports track) and hilly (uphill and downhill) terrain, while movement was monitored via NGIMUs, pressure insoles, heart rate, and video data. RESULTS: A total of 40 people (n=26, 65% women; mean age 76.3, SD 5.45 years) met the inclusion criteria and took part in the study. Data collection took place between May and September 2022. The first result is expected to be published in the spring of 2024. CONCLUSIONS: This multidisciplinary study will provide new scientific knowledge about how gait biomechanics are altered in varied environments, and how this influences opportunities to participate in outdoor activities for older people. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/52898.

A Crowd Movement Analysis Method Based on Radar Particle Flow.

Crowd movement analysis (CMA) is a key technology in the field of public safety. This technology provides reference for identifying potential hazards in public places by analyzing crowd aggregation and dispersion behavior. Traditional video processing techniques are susceptible to factors such as environmental lighting and depth of field when analyzing crowd movements, so cannot accurately locate the source of events. Radar, on the other hand, offers all-weather distance and angle measurements, effectively compensating for the shortcomings of video surveillance. This paper proposes a crowd motion analysis method based on radar particle flow (RPF). Firstly, radar particle flow is extracted from adjacent frames of millimeter-wave radar point sets by utilizing the optical flow method. Then, a new concept of micro-source is defined to describe whether any two RPF vectors originated from or reach the same location. Finally, in each local area, the internal micro-sources are counted to form a local diffusion potential, which characterizes the movement state of the crowd. The proposed algorithm is validated in real scenarios. By analyzing and processing radar data on aggregation, dispersion, and normal movements, the algorithm is able to effectively identify these movements with an accuracy rate of no less than 88%.

Navigating the Court: A Comparison of Distance Demands between Sexes in Grand Slam Tennis.

This study analysed critical movement demands of tennis match-play to better inform contemporary approaches to athlete preparation and training. HawkEye data from matches during the 2021 and 2022 Australian Open were utilised. Distance was aggregated for movement cycles, points, games, sets, and matches, alongside total shots played. Data was collated for males (best-of-five sets) and females (best-of-three sets) allowing comparisons both within and between sexes. Overall, no differences within sexes were evident for total distance, however males traversed further per match than females (MDE = 809 ± 139m, ES = 0.86). Female players travelled further in their deciding (third) sets compared to set 1 (ES = 0.28) and while this effect wasn't as discernible for males, the deciding (fifth) set showed some evidence of elevated distance requirements and variability. Between sexes, only female set 3 was different to male set 3 (ES = 0.29). Female and male tiebreak games (i.e. game 13) required players travel further distance compared to other games (ES = ~1.45). Between sex differences were observed for tiebreak games compared to games 1 to 12 (female ES = 1.36 and male ES = 1.53). Players from both sexes generally covered similar distances during points and movement cycles, with between-shot distances of 4.2m-4.5m, notably longer than previous reports. Further, total shots and total match distance (r > 0.97; p < 0.01) shared similar linear relationships. These results highlight that the between shot or movement cycle demands of professional hard court tennis are substantially higher than described in the literature (Roetert et al., 2003). The findings also reveal competitiveness as a key influence on set level distance demands during professional tennis match-play, a consideration in player preparation programs.