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With advances in computing power and deep learning, image-based pose estimation has become a viable tool for quantitative motion analysis. Compared to sensor-based systems, vision-based approaches are cost-effective, portable, and easy to deploy. However, when applied to golf swings, conventional similarity measures often fail to match expert perception, as they rely on static, frame-wise posture comparisons and require strict temporal alignment. We propose a Dynamic Motion Similarity Measurement (DMSM) framework that segments a swing into seven canonical phases—address, takeaway, half, top, impact, release, and finish—and evaluates the dynamic trajectories of joint keypoints within each phase. Unlike traditional DTW- or frame-based methods, our approach integrates continuous motion trajectories and normalizes joint coordinates to account for player body scale differences. Motion data are interpolated to improve temporal resolution, and numerical integration quantifies path differences, capturing motion flow rather than isolated postures. Quantitative experiments on side-view swing datasets show that DMSM yields stronger discrimination between same- and different-player pairs (phase-averaged separation: 0.092 vs. 0.090 for the DTW + cosine baseline) and achieves a clear biomechanical distinction in spine-angle trajectories (Δ = 38.68). Statistical analysis (paired t-test) confirmed that the improvement was significant (p < 0.05), and coach evaluations supported perceptual alignment. Although DMSM introduces a minor computational overhead (≈169 ms), it delivers more reliable phase-wise feedback and biomechanically interpretable motion analysis. This framework offers a practical foundation for AI-based golf swing analysis and real-time feedback systems in sports training, demonstrating improved perceptual consistency, biomechanical interpretability, and computational feasibility.

In this paper, an effective motion comparison method based on segmented multi-joint line graphs combined with the SIFT feature matching method is proposed. Firstly, the multi-joint 3D motion data are captured using the Kinect. Secondly, 3D motion data are normalized and distortion data are removed. Therefore, a 2D line graph can be obtained. Next, SIFT features of the 2D motion line graph are extracted. Finally, the line graphs are divided into several regions and then the comparison results can be calculated based on SIFT matching ratios between the tutor’s local line graph and the trainee’s local line graph. The experimental results show that the proposed method not only can easily deal with the several challenge problems in motion analysis, e.g., the problem of different rhythm of motions, the problem of a large amount of data, but also can provide detailed error correction cues.

This paper investigates the lexical choices made by speakers of Ladin in describing the opening scene of Mayer’s (1969) Frog, where are you? in Ladin and in the other languages they learnt later in life (Italian, German and English). The focus of the investigation is on motion lexicalisation, which varies across languages in terms of preferred encoding patterns (Talmy 1985, 2000; Wälchli 2001). Relative frequencies are calculated for the variants occurring in the different languages, before turning to a qualitative discourse-analytic approach, which forms the core of the analysis. The results are discussed with reference to the fields of typology and cross-linguistic research. The analysed texts bring to the fore the necessity of distinguishing between national and regional idioms and the potential value of drawing cross-linguistic issues to the explicit attention of learners. Possible implications are considered with reference to the novel framework of Applied Language Typology (Filipović 2018).

This paper presents comparison of two torque control methods for walking assist applied to hip exoskeleton. One method is based on motion sequences comparison and Finite State Machine (FSM) and another method is based phase oscillator. Both methods can recognize walking intention (hip joint flexion and extension) well through simulation results. The human-robot dynamic system was modelled and adaptive torque control is analysed and simulated for walking assist hip exoskeleton torque control. With parameters tuned properly through simulation, volunteers experiment was conducted and results shows that both methods can help improve walking speed and stride well. Motion comparison method performs better during static state (suddenly stop during walking) while phase oscillator method can 100% recognize walking intention and provide compatible torque assist. An average walking speed improvement of 20.16% is detected by 10m ground level walking tests with hip exoskeleton compared to freely walking.

Sensors with a higher sampling rate produce higher-quality data. However, for more extended periods of data acquisition, as in the continuous monitoring of patients, the handling of the generated big data becomes increasingly complicated. This study aimed to determine the validity and reliability of low-sampling-frequency accelerometer (SENS) measurements in patients with knee osteoarthritis. Data were collected simultaneously using SENS and a previously validated sensor (Xsens) during two repetitions of overground walking. The processed acceleration signals were compared with respect to different coordinate axes to determine the test–retest reliability and the agreement between the two systems in the time and frequency domains. In total, 44 participants were included. With respect to different axes, the interclass correlation coefficient for the repeatability of SENS measurements was [0.93–0.96]. The concordance correlation coefficients for the two systems’ agreement were [0.81–0.91] in the time domain and [0.43–0.99] in the frequency domain. The absolute biases estimated by the Bland–Altman method were [0.0005–0.008] in the time domain and [0–0.008] in the frequency domain. Low-sampling-frequency accelerometers can provide relatively valid data for measuring the gait accelerations in patients with knee osteoarthritis and can be used in the future for remote patient monitoring.

A new measure of motion similarity has been proposed. The formulation of this measure is presented and its logical basis is described. Unlike in most of other methods, the measure enables easy determination of the instantaneous synergies of the motion of body parts. To demonstrate how to use the measure, the data describing human movement is used. The movement is recorded using a professional motion capture system. Two different cases of non-periodic movements are discussed: stepping forward and backward, and returning to a stable posture after an unexpected thrust to the side (hands free or tied). This choice enables the identification of synergies in slow dynamics (stepping) and in fast dynamics (push recovery). The trajectories of motion similarity measures are obtained for point masses of the human body. The interpretation of these trajectories in relation to motion events is discussed. In addition, ordinary motion trajectories and footprints are shown in order to better illustrate the specificity of the discussed examples. The article ends with a discussion and conclusions.

The orientation of a magneto and inertial measurement unit (MIMU) is estimated by means of sensor fusion algorithms (SFAs) thus enabling human motion tracking. However, despite several SFAs implementations proposed over the last decades, there is still a lack of consensus about the best performing SFAs and their accuracy. As suggested by recent literature, the filter parameters play a central role in determining the orientation errors. The aim of this work is to analyze the accuracy of ten SFAs while running under the best possible conditions (i.e., their parameter values are set using the orientation reference) in nine experimental scenarios including three rotation rates and three commercial products. The main finding is that parameter values must be specific for each SFA according to the experimental scenario to avoid errors comparable to those obtained when the default parameter values are used. Overall, when optimally tuned, no statistically significant differences are observed among the different SFAs in all tested experimental scenarios and the absolute errors are included between 3.8 deg and 7.1 deg. Increasing the rotation rate generally leads to a significant performance worsening. Errors are also influenced by the MIMU commercial model. SFA MATLAB implementations have been made available online.

A few reports on scapular motion during shoulder joint extension exist. Understanding the normal motion of shoulder joint extension may be useful in evaluating and treating patients with diminished or minimal shoulder joint extension. Therefore, this study aimed to identify scapular motion during shoulder joint extension movement in a sitting position. Shoulder joint extension movement in the sitting position were measured in 22 healthy adults (age, 25.8 ± 2.7 years). Shoulder joint extension, scapular upward rotation, anterior tilt, external rotation angles, and the acromion position were investigated using a three-dimensional motion analyzer. The difference from each value of 10° to 50° shoulder joint extension to each value of 0° shoulder joint extension were checked. The results were compared using multiple comparison method. In most participants, the scapula tilted posteriorly up to 30° of the shoulder joint extension and anteriorly after 30°. Scapular upward and external rotation continued to increase with shoulder extension. Furthermore, the acromion was displaced upward and backward. Thus, scapular posterior tilt is necessary for shoulder joint extension during the initial movement, followed by anterior tilt. The acromion may have been displaced posteriorly because of clavicular retraction, causing the scapula to tilt posteriorly. After 30° of shoulder joint extension, the scapular anterior tilt may have prevailed over the scapular posterior tilt.

The study of perceptual decision making has made significant progress owing to major contributions from two experimental paradigms: the sequential vibrotactile frequency comparison task for the somatosensory domain requiring working memory, and the random-dot motion task in the visual domain requiring evidence accumulation over time. On the one hand, electrophysiological recordings in nonhuman primates and humans have identified changes in firing rates and power modulations of beta band oscillations with the vibrotactile frequencies held in working memory, as well as with the mental operation required for decision making. On the other hand, firing rates and centro-parietal potentials were found to increase to a fixed level at the time of responding during the random-dot motion task, possibly reflecting an underlying evidence accumulation mechanism until a decision threshold is met. Here, to bridge these two paradigms, we presented two visual random-dot motion stimuli in a sequential comparison task while recording EEG from human volunteers. We identified a modulation of prefrontal beta band power that scaled with the level of dot motion coherence of the first stimulus during a short retention interval. Furthermore, beta power in premotor areas was modulated by participants’ choices approximately 700 ms before responses were given via button press. At the same time, dot motion patches of the second stimulus evoked a pattern of broadband centro-parietal signal build-up till responses were made, whose peak varied with trial difficulty. Hence, we show that known modulations of beta power during working memory and decision making extend from the vibrotactile to the visual domain and provide support for the notion of evidence accumulation as an unconfined decision-making mechanism generalizing over distinct decision types. •Comparing amount of motion between two sequentially presented random dot stimuli.•Prefrontal beta power encodes to-be-remembered amount of motion during retention.•Premotor beta power indexes participants’ choices before responding.•Centro-parietal positivity tracks decision, but not evidence for single percepts.•Centro-parietal positivity does not hit a fixed bound when responding.

Correct use of multi-temporal Interferometric Synthetic Aperture Radar (InSAR) datasets to complement geodetic surveying for geo-hazard applications requires rigorous assessment of their precision and accuracy. Published inter-comparisons are mostly limited to ground displacement estimates obtained from different algorithms belonging to the same family of InSAR approaches, either Persistent Scatterer Interferometry (PSI) or Small BAseline Subset (SBAS); and accuracy assessments are mainly focused on vertical displacements or based on few Global Navigation Satellite System (GNSS) or geodetic leveling points. To fill this demonstration gap, two years of Sentinel-1 SAR ascending and descending mode data are processed with both PSI and SBAS consolidated algorithms to extract vertical and horizontal displacement velocity datasets, whose accuracy is then assessed against a wealth of contextual geodetic data. These include permanent GNSS records, static GNSS benchmark repositioning, and geodetic leveling monitoring data that the National Institute of Statistics, Geography, and Informatics (INEGI) of Mexico collected in 2014−2016 in the Aguascalientes Valley, where structurally-controlled land subsidence exhibits fast vertical rates (up to −150 mm/year) and a non-negligible east-west component (up to ±30 mm/year). Despite the temporal constraint of the data selected, the PSI-SBAS inter-comparison reveals standard deviation of 6 mm/year and 4 mm/year for the vertical and east-west rate differences, respectively, thus reassuring about the similarity between the two types of InSAR outputs. Accuracy assessment shows that the standard deviations in vertical velocity differences are 9−10 mm/year against GNSS benchmarks, and 8 mm/year against leveling data. Relative errors are below 20% for any locations subsiding faster than −15 mm/year. Differences in east-west velocity estimates against GNSS are on average −0.1 mm/year for PSI and +0.2 mm/year for SBAS, with standard deviations of 8 mm/year. When discrepancies are found between InSAR and geodetic data, these mostly occur at benchmarks located in proximity to the main normal faults, thus falling within the same SBAS ground pixel or closer to the same PSI target, regardless of whether they are in the footwall or hanging wall of the fault. Establishing new benchmarks at higher distances from the fault traces or exploiting higher resolution SAR scenes and/or InSAR datasets may improve the detection of the benchmarks and thus consolidate the statistics of the InSAR accuracy assessments.