The use of sonification, the transformation of data into non-speech sound, holds strong potential for enhancing physical performance in sports and movement-based activities. However, literature reveals that most sonification systems have been, or need to be, custom-built to serve a specific purpose. This results in existing systems often being limited: they require some level of programming expertise, are tailored to narrow use cases, or lack compatibility with real-time data. While numerous toolkits exist that aim to simplify the creation of sonifications, there are no general-purpose toolkits specifically designed for the sports and movement domain. These barriers make it difficult for researchers working in this domain, such as sports scientists or Human-Computer Interaction (HCI) researchers, to effectively design sonification strategies. In this paper, we present a modular sonification toolkit that maps pre-recorded movement data to sound parameters across multiple sound design modules, with future support for real-time data processing under development.

In alpine skiing, the way a ski engages with the snow surface – particularly at the beginning of a turn – plays a key role in determining performance. This study introduces Edging Velocity (EV) as a novel metric to quantify how quickly the ski is tipped onto its edge during turn initiation. Building upon sensor-based motion analysis using the “Connected Boot” system, we investigated three distinct skiing techniques: race carving, moderate carving, and parallel ski steering. An expert skier performed multiple turns for each technique, and EV was computed from edge angle progression. Results show that EV was highest during race carving, followed by moderate carving, and lowest during parallel ski steering. All pairwise differences were statistically significant (p < 0.001 or p < 0.01). These findings highlight EV’s potential as a performance-relevant parameter for optimizing edge engagement. Integrated into real-time feedback systems, EV may support learning and refinement of skiing technique, particularly in the critical early phase of a turn.

Purpose
This study investigates the dynamics of iterations in Internet of Things (IoT) new product development (NPD), focusing on the interplay between data collection, information creation and value creation phases.

Design/methodology/approach
A multiple-case study approach was employed, examining five IoT NPD projects across various industries. Data were collected through 20 in-depth semi-structured interviews and analysed using qualitative methods.

Findings
The study identifies uncertainty related to data collection components as the primary driver of iteration cycles in IoT NPD. The technological readiness level (TRL) of data collection components and the concept readiness of the new IoT product significantly influence this uncertainty. The research identifies key nominally complete activities triggering iterations across the data-value chain and reasons for iterations including technological uncertainty, data quality evolution, product concept maturity, interdependencies between phases, validation requirements and external partnerships.

Research limitations/implications
The study is limited to IoT products with human data sources in specific domains. Future research could expand to other IoT sectors and employ larger sample sizes.

Practical implications
The findings suggest that project managers should focus on reducing uncertainty in data collection components to minimize unplanned iterations. Strategies include reducing the number of high-risk components and clearly defining system requirements early in the development process.

Originality/value
This study introduces an IoT NPD iteration framework, a novel theoretical model that explains how uncertainty in data collection components drives iteration cycles in IoT NPD. By mapping the reciprocal dependencies between data collection, information creation and value realization, the framework offers a structured approach to identifying the root causes of iteration and strategies for managing complexity in IoT NPD projects.

The interaction between ski and snow is crucial to skiing technique and performance, with ski deflection characteristics offering valuable insights into the complex dynamics of this process. For this purpose, a sensor ski based on PyzoFlex® technology was developed and validated in extensive investigations and tested in application-oriented field settings (Thorwartl et al. 2022; Thorwartl et al. 2023). The PyzoFlex® sensor system has been used on recreational skis, but for racing skis, it is crucial that the sensors have minimal impact on performance. Therefore, this research aims to evaluate the impact of PyzoFlex® sensor foils on the dynamic properties of skis by comparing an instrumented and non-instrumented ski through free vibration testing, following ISO 6267 standards. Key metrics assessed include the time for 30 complete vibrations (t30), natural frequency (fn), vibration period (T), and half-life (t1/2).
To investigate this, a ski (Atomic Redster G7; length: 1.82 m; radius: 19.6 m), initially in its original state, and then subsequently instrumented with the PyzoFlex® ski sensor system (Thorwartl et al. 2021) underwent free vibration testing in accordance with ISO 6267 to derive various dynamic parameters. Vibration data were collected using a capacitive sensor (HBM 25321A B12/200, Hottinger Baldwin Messtechnik, Darmstadt, Germany) which was mounted on the ski shovel, and the data were processed and visualized using LabVIEW (National Instruments, Austin, Texas, USA). By calculating both absolute differences (diff) and relative differences (% diff) between the two configurations, a detailed comparison of dynamic properties was made.
The test results revealed that the corresponding values of t1/2, T and t30 did not change by more than 1.8% (max. 0.02 s) compared to the initial non-instrumented ski (Table 1). The variation in fn is 0.07 Hz (% diff: 0.6%).
Based on the free vibration measurements, it can be concluded that the foils have minimal influence on the dynamic properties of the ski due to the flexibility and low mass of the sensors. This is in contrast to a prototype utilizing strain gauges, which significantly affected the ski’s behavior, with t30 reduced by 100% (Yoneyama et al. 2008). In the case of the PyzoFlex® ski, t30 showed a change of less than 1%. While other prototypes exist, they either did not assess such dynamic impacts or have not published results on this aspect.
This study is limited to laboratory vibration tests; effects during dynamic skiing and under varying snow conditions remain to be explored.
In summary, the integration of PyzoFlex® sensor foils does not appear to compromise ski dynamics, making them suitable for use in racing skis. The first prototype has already been developed and will soon be tested under laboratory and field conditions.

Background: The integration of machine learning and deep learning methodologies has transformed data analytics in biomechanics. However, the field faces challenges such as limited large-scale data sets, high data acquisition costs, and restricted participant access that hinder the development of robust algorithms. Additional issues include variability in sensor placement, soft tissue artifacts, and low diversity in movement patterns. These challenges make it difficult to train models that perform reliably across individuals, tasks, and settings. Data augmentation can help address these limitations, but its use in biomechanical time-series data remains insufficiently evaluated. Objective: This scoping review on data augmentation for biomechanical time-series data focuses on examining current techniques, evaluating their effectiveness, and offering recommendations for their application. Design: Four online databases (PubMed, IEEE Xplore, Scopus, and Web of Science) were used to find studies published between 2013 and 2024. Following PRISMA-ScR guidelines, two screening processes were conducted to identify relevant publications. Results: 21 publications were identified as relevant. There is no universal best practice for augmenting biomechanical time-series data; instead, methods vary based on study aims. A key issue identified is the absence of soft tissue artifacts in synthetic data, leading to discrepancies and emphasizing the need for realistic techniques. Furthermore, many studies lack proper evaluation of augmentation methods, making it difficult to understand the effects of different techniques. This understanding is crucial for assessing the impact of the augmented data set on downstream models and evaluating the quality of the data augmentation process. Conclusion: This review highlights the importance of data augmentation in addressing limited data availability and improving model generalization in biomechanics. Tailoring augmentation to data characteristics can enhance the performance and relevance of predictive models. However, understanding how different augmentation techniques impact data quality and downstream performance remains essential for developing better methods.

Endurance running’s popularity stems from its accessibility, but some people avoid it due to respiratory issues. Breathing monitoring using body area networks can help address these concerns by detecting breathing during exercise and offering real-time guiding feedback. This review outlines the challenges in developing body area networks and algorithms to enable intuitive real-time breathing guidance to enhance restful running experiences. Key findings are that existing breathing guidance systems’ feedback is mostly based on instructing breathing rates calculated on past observations. However, detecting instantaneous breathing phase and amplitude allows concurrent breathing guidance by triggering the manipulation of coinciding breathing phases, e.g. towards active prolonged exhales. Aspects of personal breathing guidance systems that are fundamental to create pleasant and restful running experiences are identified for further investigation in future research.

This study investigates performance development and the relationship between subjective and objective training assessments in female youth soccer using wearable sensor technology. The aim of this study was to assess how subjective post-training ratings (intensity and happiness) relate to high-percentile performance outputs, and to identify longitudinal trends in female youth soccer players using IMU-based wearable data. Data were collected over a 14-month period from 46 players (U17 and U20 teams) equipped with foot-mounted inertial measurement units (IMUs) during regular training sessions. Objective performance metrics, including 95th percentile of ball speed, peak speed, and absolute distance, were derived using a multi-stage machine learning pipeline, while subjective metrics (intensity and happiness) were collected via post-session Likert-scale questionnaires using an app. Using the modified Mann-Kendall test, we found 30 significant longitudinal trends, with 14 positive and 16 negative trends across key performance metrics. Peak speed showed the highest number of trends (13), followed by absolute distance (10) and ball speed (7). Correlation analyses based on the Spearman coefficient (with False Discovery Rate correction) revealed meaningful associations between subjective self-assessments and high-percentile performance metrics, with notable differences across player positions and age groups. A robustness check confirmed these patterns also hold when analyzing the 99th percentile of performance outputs. Our findings underscore the value of combining wearable sensor data with subjective evaluations for individualized, role-specific performance monitoring and training optimization in youth soccer. However, as an exploratory study with a single cohort, findings require further validation in broader populations.

Work-related stress affects 39% of Austrians, contributing to mental health issues like depression and burnout, driven by factors such as workload and lack of control.
The Relax project aims to develop a holistic stress management framework using continuous stress assessment and personalized interventions based on physiological, behavioral, emotional, and cognitive indicators.
The study combines wearable sensors (e.g., Polar Verity Sense), psychological methods, and technical strategies, with a longitudinal design to assess the app’s usability and effectiveness.
Usability was hindered by technical issues, but stress data visualization was well-received. The Micro-Model aligned well with stress estimates, while the Macro-Model faced data limitations. Methodological constraints suggested further refinement.
The study highlights the need for individualized, multimodal stress management, with future research focusing on system improvements, technical refinement, and validation through an anonymized dataset.

Background: Occupational stress is associated with detrimental consequences that are addressed by mobile health (mHealth) solutions. Previous developments of apps for occupational stress have not yet fully exploited the potential of multilevel diagnostics through the integration of wearable sensors for interventions. Personalizing mHealth approaches in terms of intervention time and content, which requires the use of artificial intelligence, is the next logical developmental step. The „Relax“ approach developed a corresponding prototype of an app-wearable system, which will be evaluated for effectiveness in terms of stress reduction and usability.

Objective: This study protocol describes an evaluation study used to test the effectiveness and usability of the Relax approach.

Methods: The evaluation study was designed as a 2-arm randomized trial with 2 phases, each with a 3-week intervention period. In both phases, employees were required to use the app to record daily stress and to wear a wearable sensor to measure heart rate variability. The app offered interventions based on algorithms, which altered the probability of their selection after learning from the data, thereby personalizing the user experience. In the second phase of the study, the sample was divided into 2 groups, varying the degree of personalization of the app. To analyze effectiveness, a 2-factorial mixed within-between design will be applied to compare the outcomes between both groups as well as in a pre-post comparison. In addition, exploratory analyses of the usability of the approach are planned.

Results: The study was conducted during the spring and summer of 2024, with a total of 46 participants enrolled, and is ready for data analysis.

Conclusions: The Relax approach, including a number of factors related to personalization that have not yet been incorporated into mHealth in current research, will provide new insights into the next steps of advanced mHealth solutions. Limitations of the study design, such as the lack of a control group and the sample representativity, have to be addressed.

This study aimed to develop and validate a machine learning method to estimate continuous 3D knee moments during running from wearable sensor data. Reference knee moments were calculated from 19 recreational runners during treadmill running at varying slopes (0 ± 5 % incline), speeds (self-selected ± 1 km/h) and in 3 types of footwear. A convolutional neural network was trained on data from 7 inertial measuring units (feet, shanks, thighs, sacrum) and a pair of pressure insoles. We assessed performance over continuous time windows (CONT) and during stance phases (PHSS) by intraclass-correlation (ICC), normalised root mean squared error (nRMSE), and statistical parametric mapping. The agreement levels in the sagittal plane were good to excellent (ICC: 0.84–0.98), with low errors (nRMSE: 0.05–0.11). However, accuracy was lower for non-sagittal estimations (frontal ICC: 0.19–0.90, nRMSE: 0.08–0.23; transverse ICC: 0.72–0.94, nRMSE: 0.07–0.17). Accuracy decreased across all planes during PHSS. The proposed approach yields similar or better accuracy compared to previous work while requiring less preprocessing. It provides a viable method for wearable-based assessment of running kinetics in near real-time. Additional data and methods to address inter-individual variability could improve its precision in assessing frontal plane injury risk factors.

While running is amongst the most popular activities for competition and leisure, an estimated 20-40% of runners may suffer from respiratory limitations. Some of these runners may benefit from breathing techniques to improve performance or alleviate respiratory discomfort. One such technique is locomotor-respiratory coupling (LRC), a frequency and phase synchronization of breath to step. Studies have demonstrated that LRC may benefit ventilatory efficiency via „step-driven flows,“ and some experts have argued it could be used for pacing exercise or increasing positive emotional states. Nevertheless, it may be difficult to perform without coaching or guidance. Here we propose RunRhythm, a custom smartphone application to deliver step-synchronized sound guidance for LRC. This concept builds on previous evidence that sound guidance can be effective and integrates features to maximize adherence and individualization. Preliminary results show that this application is a promising and efficacious method suitable for research on LRC in field exercise. Recommendations for use and further development are discussed to further develop this concept for the benefit of a wider population.

INTRODUCTION: A carving turn is defined by the ski experiencing minimal to no lateral movement relative to its path. Divergence between the ski’s orientation vector (E) and the resultant velocity vector (v), known as the angle of attack (AoA), results in skidding. Reid et al. (2020) investigated the AoA in the field using a videographic method, but this approach was laborious [1]. Up to now, only Schütz et al. (2024) have reported a sensor-based solution, but it was tested solely in the lab, without field data [2]. Therefore, this study aims to evaluate a sensor-based solution for detecting AoA in the field and distinguishing between carving, parallel ski steering, and transitions.
METHODS: Four IMUs (Xsens DOT) and a GNSS (Xsens, MTi-680G) were equipped to the ski (Atomic Redster G7, length: 1.82 m, radius: 17.3 m) with the antenna of the GNSS sensor placed directly on top of the GNSS sensor. The participant carried a backpack with a laptop to connect to the GNSS and performed 20 turns of each of the following: (i) carving, (ii) parallel ski steering, and (iii) transitions from parallel ski steering to carving. To address distortions in v data during skiing, a three-step filtering process was applied separately to each dimension: (i) Kalman smoothing, (ii) Hampel filtering for outlier removal, and (iii) Butterworth low-pass filtering. The AoA was subsequently calculated using the procedure outlined in previous research [2]. The runs were divided into individual turns using gyroscope-based turn detection in accordance with Martinez et al. (2019) [3]. The right turns (instrumented outer ski) were time-normalized, and the mean AoA ± standard error (SE) was calculated for each technique.
RESULTS/DISCUSSION: The results indicate that carving has the lowest mean AoA of 6.26°, while parallel ski steering has the highest mean AoA of 18.80°. The peak AoA for parallel ski steering is 32.47°, and the minimum AoA for carving is 4.09°. This is in line with past research, showing that the AoA increases as skidding is introduced [1]. During the transition, the maximum AoA of 22.13° is reached at approximately 20% of the turn completion, after which it decreases. This observation aligns with the video data, which clearly shows a transition from a skidding to a carving turn. In summary, each skiing technique can be associated with a distinct progression of the AoA during a turn.
CONCLUSION: Given that the AoA examines the definition of carving, the authors consider it crucial for monitoring and improving performance. Tracking AoA gives coaches and athletes insights to refine technique and make data-driven adjustments for better skiing proficiency.

Recent developments in sensor technology have made sensing units cheaper and easier to implement. These developments have made the application of “wearable technology”, or smart sporting equipment appealing to a broad audience allowing measuring metrics of motion quantity (e.g. distance, time, number of cycles) and motion quality, or how well a sport technique is performed. In the current presentation the concept of an instrumented ski boot, an instrumented alpine ski using the PyzoFlex® sensor foil technology and the determination of force components and measures of effectiveness within various cross-country skiing (XCS) techniques applying a push-off model is presented.
The Atomic connected boot consists of an IMU mounted on the shaft of the boot. With this concept macroscopic metrics like turn detection, turn count (1) and jump detection (2) were developed while on a more microscopic level a skiing style detection algorithm and carving score concept (3) was established to determine the skiing/carving quality while skiing. Ski deflection is a performance-relevant factor in alpine skiing and the segmental and temporal curvature characteristics along the ski have lately received particular attention. Recently, a PyzoFlex® ski deflection measurement prototype was introduced that demonstrates high reliability and validity in both static and dynamic situations both in the laboratory (bending machine, bending robot, vibration measures) and in the field while skiing (4).
The primary mechanical determinant of XCS performance is the propulsive force from both skis and poles represented by the force components along the skiing direction. The determination of force components, the relative contributions of upper-and-lower-body work to propulsion and the translation of resultant forces into propulsive forces (effectiveness) is of interest from a general locomotor perspective but also for coaches and athletes to choose situation dependent the appropriate skiing technique, to develop more effective techniques and to tailor the general conditioning program according to the biomechanics of the single XCS techniques. In various studies resultant forces were recorded using pressure insoles, force bindings, force systems integrated into skis and poles. Force components were determined by track integrated force plates or combinations of resultant forces with 3D kinematic measures. Here we present a model to determine force components, effectiveness, upper vs. lower body contribution based on leg and pole forces and 3D kinematics in various XCS techniques.
In all presented concepts the challenge lies in further developments for integration into non-obtrusive technologies in communication with conventional measuring devices (e.g. smart watch, training app) allowing for enhanced quality of training/competition metrics, feedback systems assisting in technique training, support for ski equipment selection and equipment customization etc.

Alpine skiing is a popular sport in many countries and holds benefits in terms of health and well-being. At the same time alpine skiing is associated with a certain risk of accidents caused, among other things, by overestimating one’s own skiing skills. Self-assessment of skiing skills is not trivial. Therefore, feedback modalities can be assistive. One feasible option to provide skiers with feedback on their skiing ability are Inertial Measurement Units (IMUs). The aim of this study was to analyse the skiing quality of recreational skiers based on IMU data, collected with the Connected Boot sensor system with a living lab approach to investigate whether the skiing quality score delivers reasonable results for recreational skiers. The system has been developed with expert skiers and so far has not been validated with recreational skiers. Therefore, we aimed to investigate whether the objective skiing quality score of the Connected Boot corresponds to the self-assessed carving ability and to analyse changes in the assessment before and after the study. In total, data from 62 participants who skied with the sensor system were analysed. At the beginning and the end of the study the participants additionally received questionnaires to assess their skiing skills. The results show that there was a strong correlation between the self-reported carving ability and the skiing quality score of the Connected Boot and that the self-reported carving ability before the study was around 1.71 points higher than the algorithm-based skiing quality score. Interestingly, the correlation was higher for female compared to male participants.