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.

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.

Acceptability, validity and responsiveness of inertial measurement units for assessing motor recovery after gene therapy in infants with early onset spinal muscular atrophy: a prospective cohort study.

BACKGROUND: Onasemnogene abeparvovec gene replacement therapy (GT) has changed the prognosis of patients with spinal muscular atrophy (SMA) with variable outcome regarding motor development in symptomatic patients. This pilot study evaluates acceptability, validity and clinical relevance of Inertial Measurement Units (IMU) to monitor spontaneous movement recovery in early onset SMA patients after GT. METHODS: Clinical assessments including CHOPINTEND score (the gold standard motor score for infants with SMA) and IMU measurements were performed before (M0) and repeatedly after GT. Inertial data was recorded during a 25-min spontaneous movement task, the child lying on the back, without (10 min) and with a playset (15 min) wearing IMUs. Two commonly used parameters, norm acceleration 95th centile (||A||_95) and counts per minute (||A||_CPM) were computed for each wrist, elbow and foot sensors. RESULTS: 23 SMA-patients were included (mean age at diagnosis 8 months [min 2, max 20], 19 SMA type 1, three type 2 and one presymptomatic) and 104 IMU-measurements were performed, all well accepted by families and 84/104 with a good child participation (evaluated with Brazelton scale). ||A||_95 and ||A||_CPM showed high internal consistency (without versus with a playset) with interclass correlation coefficient for the wrist sensors of 0.88 and 0.85 respectively and for the foot sensors of 0.93 and 0.91 respectively. ||A||_95 and ||A||_CPM were strongly correlated with CHOPINTEND (r for wrist sensors 0.74 and 0.67 respectively and for foot sensors 0.61 and 0.68 respectively, p-values < 0.001). ||A||_95 for the foot, the wrist, the elbow sensors and ||A||_CPM for the foot, the wrist, the elbow sensors increased significantly between baseline and the 12 months follow-up visit (respective p-values: 0.004, < 0.001, < 0.001, 0.006, < 0.001, < 0.001). CONCLUSION: IMUs were well accepted, consistent, concurrently valid, responsive and associated with unaided sitting acquisition especially for the elbow sensors. This study is the first reporting a large set of inertial sensor derived data after GT in SMA patients and paves the way for IMU-based follow-up of SMA patients after treatment.

Statistical evaluation of tongue capability with visual feedback.

BACKGROUND: Analysis of tongue movement would benefit from a reference showcasing healthy tongue capability. We aimed to develop a reference of tongue capability and evaluated the role of visual feedback on the expression of movement. METHODS: Using a wireless tracking intraoral wearable device, we composed probability distributions of the tongue tip as subjects were asked to explore the entire sensing surface area. Half of the 32 subjects received live visual feedback of the location of the center of the tongue tip contact. RESULTS: We observed that the visual feedback group was 51.0% more consistent with each other in the position domain, explored 21.5% more sensing surface area, and was 50.7% more uniformly distributed. We found less consistent results when we evaluated velocity and acceleration. CONCLUSION: Visual feedback best established a healthy capability reference which can be used for designing new interfaces, quantifying tongue ability, developing new diagnostic and rehabilitation techniques, and studying underlying mechanisms of tongue motor control.

Differences in Trunk Acceleration-Derived Gait Indexes in Stroke Subjects with and without Stroke-Induced Immunosuppression.

Background: Stroke-induced immunosuppression (SII) represents a negative rehabilitative prognostic factor associated with poor motor performance at discharge from a neurorehabilitation unit (NRB). This study aims to evaluate the association between SII and gait impairment at NRB admission. Methods: Forty-six stroke patients (65.4 ± 15.8 years, 28 males) and 42 healthy subjects (HS), matched for age, sex, and gait speed, underwent gait analysis using an inertial measurement unit at the lumbar level. Stroke patients were divided into two groups: (i) the SII group was defined using a neutrophil-to-lymphocyte ratio ≥ 5, and (ii) the immunocompetent (IC) group. Harmonic ratio (HR) and short-term largest Lyapunov's exponent (sLLE) were calculated as measures of gait symmetry and stability, respectively. Results: Out of 46 patients, 14 (30.4%) had SII. HR was higher in HS when compared to SII and IC groups (p < 0.01). HR values were lower in SII when compared to IC subjects (p < 0.01). sLLE was lower in HS when compared to SII and IC groups in the vertical and medio-lateral planes (p ≤ 0.01 for all comparisons). sLLE in the medio-lateral plane was higher in SII when compared to IC subjects (p = 0.04). Conclusions: SII individuals are characterized by a pronounced asymmetric gait and a more impaired dynamic gait stability. Our findings underline the importance of devising tailored rehabilitation programs in patients with SII. Further studies are needed to assess the long-term outcomes and the role of other clinical features on gait pattern.

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.

Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease.

Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.

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.

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%.

GaitKeeper: An AI-Enabled Mobile Technology to Standardize and Measure Gait Speed.

Gait speed is increasingly recognized as an important health indicator. However, gait analysis in clinical settings often encounters inconsistencies due to methodological variability and resource constraints. To address these challenges, GaitKeeper uses artificial intelligence (AI) and augmented reality (AR) to standardize gait speed assessments. In laboratory conditions, GaitKeeper demonstrates close alignment with the Vicon system and, in clinical environments, it strongly correlates with the Gaitrite system. The integration of a cloud-based processing platform and robust data security positions GaitKeeper as an accurate, cost-effective, and user-friendly tool for gait assessment in diverse clinical settings.

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.

The influence of visual elements in Chinese food labels on consumers' visual perception: insights from an eye-tracking study.

To investigate the impact of four visual elements, namely text, colour, image, and shape, on the visual perception of Chinese consumers when observing nutrition labels, as well as to enhance their attention towards nutritional information, this study examines the visual effects of nutrition labels incorporating these elements through eye movement experiments, questionnaire surveys, subjective evaluations, and other research methods. The aim is to determine the optimal design solution. The results revealed that participants displayed the highest level of attention towards the round x image group, followed by the colour group. Thus, exceptional image design and a suitable colour scheme can significantly enhance consumers' attention during browsing. This study offers valuable references and guidance for the redesign of food nutrition labels, while also presenting research insights for the application of visual perception in other domains.

TAT-HUM: Trajectory analysis toolkit for human movements in Python.

Human movement trajectories can reveal useful insights regarding the underlying mechanisms of human behaviors. Extracting information from movement trajectories, however, can be challenging because of their complex and dynamic nature. The current paper presents a Python toolkit developed to help users analyze and extract meaningful information from the trajectories of discrete rapid aiming movements executed by humans. This toolkit uses various open-source Python libraries, such as NumPy and SciPy, and offers a collection of common functionalities to analyze movement trajectory data. To ensure flexibility and ease of use, the toolkit offers two approaches: an automated approach that processes raw data and generates relevant measures automatically, and a manual approach that allows users to selectively use different functions based on their specific needs. A behavioral experiment based on the spatial cueing paradigm was conducted to illustrate how one can use this toolkit in practice. Readers are encouraged to access the publicly available data and relevant analysis scripts as an opportunity to learn about kinematic analysis for human movements.

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.

Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion: Drawing Insights From Psychology, Engineering, and the Arts, This Article Provides a Comprehensive Overview of the Field of Emotion Analysis in Visual Media and Discusses the Latest Research, Systems, Challenges, Ethical Implications, and Potential Impact of Artificial Emotional Intelligence on Society.

The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding of human behavior that was once thought impossible. While recent advancements in deep learning have transformed the field of computer vision, automated understanding of evoked or expressed emotions in visual media remains in its infancy. This foundering stems from the absence of a universally accepted definition of "emotion," coupled with the inherently subjective nature of emotions and their intricate nuances. In this article, we provide a comprehensive, multidisciplinary overview of the field of emotion analysis in visual media, drawing on insights from psychology, engineering, and the arts. We begin by exploring the psychological foundations of emotion and the computational principles that underpin the understanding of emotions from images and videos. We then review the latest research and systems within the field, accentuating the most promising approaches. We also discuss the current technological challenges and limitations of emotion analysis, underscoring the necessity for continued investigation and innovation. We contend that this represents a "Holy Grail" research problem in computing and delineate pivotal directions for future inquiry. Finally, we examine the ethical ramifications of emotion-understanding technologies and contemplate their potential societal impacts. Overall, this article endeavors to equip readers with a deeper understanding of the domain of emotion analysis in visual media and to inspire further research and development in this captivating and rapidly evolving field.

Criterion validity of neural networks to assess lower limb motion during cycling.

The use of marker-less methods to automatically obtain kinematics of movement is expanding but validity to high-velocity tasks such as cycling with the presence of the bicycle on the field of view is needed when standard video footage is obtained. The purpose of this study was to assess if pre-trained neural networks are valid for calculations of lower limb joint kinematics during cycling. Motion of twenty-six cyclists pedalling on a cycle trainer was captured by a video camera capturing frames from the sagittal plane whilst reflective markers were attached to their lower limb. The marker-tracking method was compared to two established deep learning-based approaches (Microsoft Research Asia-MSRA and OpenPose) to estimate hip, knee and ankle joint angles. Poor to moderate agreement was found for both methods, with OpenPose differing from the criterion by 4-8° for the hip and knee joints. Larger errors were observed for the ankle joint (15-22°) but no significant differences between methods throughout the crank cycle when assessed using Statistical Parametric Mapping were observed for any of the joints. OpenPose presented stronger agreement with marker-tracking (criterion) than the MSRA for the hip and knee joints but resulted in poor agreement for the ankle joint.

A Wireless Rowing Measurement System for Improving the Rowing Performance of Athletes.

The rowing technique is a key factor in the overall rowing performance. Nowadays the athletes' performance is so advanced that even small differences in technique can have an impact on sport competitions. To further improve the athletes' performance, individualized rowing is necessary. This can be achieved by intelligent measurement technology that provides direct feedback. To address this issue, we developed a novel wireless rowing measurement system (WiRMS) that acquires rowing movement and measures muscle activity using electromyography (EMG). Our measurement system is able to measure several parameters simultaneously: the rowing forces, the pressure distribution on the scull, the oar angles, the seat displacement and the boat acceleration. WiRMS was evaluated in a proof-of-concept study with seven experienced athletes performing a training on water. Evaluation results showed that WiRMS is able to assess the rower's performance by recording the rower's movement and force applied to the scull. We found significant correlations (p < 0.001) between stroke rate and drive-to-recovery ratio. By incorporating EMG data, a precise temporal assignment of the activated muscles and their contribution to the rowing motion was possible. Furthermore, we were able to show that the rower applies the force to the scull mainly with the index and middle fingers.

Effects of Upper Limb Robot-Assisted Rehabilitation Compared with Conventional Therapy in Patients with Stroke: Preliminary Results on a Daily Task Assessed Using Motion Analysis.

Robotic rehabilitation of the upper limb has demonstrated promising results in terms of the improvement of arm function in post-stroke patients. The current literature suggests that robot-assisted therapy (RAT) is comparable to traditional approaches when clinical scales are used as outcome measures. Instead, the effects of RAT on the capacity to execute a daily life task with the affected upper limb are unknown, as measured using kinematic indices. Through kinematic analysis of a drinking task, we examined the improvement in upper limb performance between patients following a robotic or conventional 30-session rehabilitation intervention. In particular, we analyzed data from nineteen patients with subacute stroke (less than six months following stroke), nine of whom treated with a set of four robotic and sensor-based devices and ten with a traditional approach. According to our findings, the patients increased their movement efficiency and smoothness regardless of the rehabilitative approach. After the treatment (either robotic or conventional), no differences were found in terms of movement accuracy, planning, speed, or spatial posture. This research seems to demonstrate that the two investigated approaches have a comparable impact and may give insight into the design of rehabilitation therapy.

Integrated UWB/MIMU Sensor System for Position Estimation towards an Accurate Analysis of Human Movement: A Technical Review.

Integrated Ultra-wideband (UWB) and Magnetic Inertial Measurement Unit (MIMU) sensor systems have been gaining popularity for pedestrian tracking and indoor localization applications, mainly due to their complementary error characteristics that can be exploited to achieve higher accuracies via a data fusion approach. These integrated sensor systems have the potential for improving the ambulatory 3D analysis of human movement (estimating 3D kinematics of body segments and joints) over systems using only on-body MIMUs. For this, high accuracy is required in the estimation of the relative positions of all on-body integrated UWB/MIMU sensor modules. So far, these integrated UWB/MIMU sensors have not been reported to have been applied for full-body ambulatory 3D analysis of human movement. Also, no review articles have been found that have analyzed and summarized the methods integrating UWB and MIMU sensors for on-body applications. Therefore, a comprehensive analysis of this technology is essential to identify its potential for application in 3D analysis of human movement. This article thus aims to provide such a comprehensive analysis through a structured technical review of the methods integrating UWB and MIMU sensors for accurate position estimation in the context of the application for 3D analysis of human movement. The methods used for integration are all summarized along with the accuracies that are reported in the reviewed articles. In addition, the gaps that are required to be addressed for making this system applicable for the 3D analysis of human movement are discussed.