Publications

@article{nokey,

title = {Indirect Estimation of Breathing Rate from Heart Rate Monitoring System during Running},

author = {G. Prigent and K. Aminian and T. Rodrigues and J.-M. Vesin and G.P. Millet and M. Falbriard and F. Meyer},

doi = {https://doi.org/10.3390/s21165651 },

year  = {2021},

date = {2021-08-22},

urldate = {2022-08-21},

journal = {Sensors},

volume = {21},

pages = {5651},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Harbour2021,

title = {Enhanced Breathing Pattern Detection during Running Using Wearable Sensors},

author = {E. Harbour and M. Lasshofer and M. Genitrini and H. Schwameder},

doi = {10.3390/s21165606},

year  = {2021},

date = {2021-08-20},

urldate = {2021-08-20},

journal = {Sensors},

volume = {21},

number = {16},

abstract = {Breathing pattern (BP) is related to key psychophysiological and performance variables during exercise. Modern wearable sensors and data analysis techniques facilitate BP analysis during running but are lacking crucial validation steps in their deployment. Thus, we sought to evaluate a wearable garment with respiratory inductance plethysmography (RIP) sensors in combination with a custom-built algorithm versus a reference spirometry system to determine its concurrent validity in detecting flow reversals (FR) and BP. Twelve runners completed an incremental running protocol to exhaustion with synchronized spirometry and RIP sensors. An algorithm was developed to filter, segment, and enrich the RIP data for FR and BP estimation. The algorithm successfully identified over 99% of FR with an average time lag of 0.018 s (−0.067,0.104) after the reference system. Breathing rate (BR) estimation had low mean absolute percent error (MAPE = 2.74 [0.00,5.99]), but other BP components had variable accuracy. The proposed system is valid and practically useful for applications of BP assessment in the field, especially when measuring abrupt changes in BR. More studies are needed to improve BP timing estimation and utilize abdominal RIP during running.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Thorwartl2021,

title = {A Novel Sensor Foil to Measure Ski Deflections: Development and Validation of a Curvature Model},

author = {C. Thorwartl and J. Kröll and A. Tschepp and P. Schäffner and H. Holzer and T. Stöggl},

url = {https://www.mdpi.com/1424-8220/21/14/4848},

doi = {10.3390/s21144848},

year  = {2021},

date = {2021-07-16},

journal = {Sensors},

volume = {21},

number = {14},

pages = {4848},

abstract = {The ski deflection with the associated temporal and segmental curvature variation can be considered as a performance-relevant factor in alpine skiing. Although some work on recording ski deflection is available, the segmental curvature among the ski and temporal aspects have not yet been made an object of observation. Therefore, the goal of this study was to develop a novel ski demonstrator and to conceptualize and validate an empirical curvature model. Twenty-four PyzoFlex® technology-based sensor foils were attached to the upper surface of an alpine ski. A self-developed instrument simultaneously measuring sixteen sensors was used as a data acquisition device. After calibration with a standardized bending test, using an empirical curvature model, the sensors were applied to analyze the segmental curvature characteristic (m−1) of the ski in a quasi-static bending situation at five different load levels between 100 N and 230 N. The derived curvature data were compared with values obtained from a high-precision laser measurement system. For the reliability assessment, successive pairs of trials were evaluated at different load levels by calculating the change in mean (CIM), the coefficient of variation (CV) and the intraclass correlation coefficient (ICC 3.1) with a 95% confidence interval. A high reliability of CIM −1.41–0.50%, max CV 1.45%, and ICC 3.1 > 0.961 was found for the different load levels. Additionally, the criterion validity based on the Pearson correlation coefficient was R2 = 0.993 and the limits of agreement, expressed by the accuracy (systematic bias) and the precision (SD), was between +9.45 × 10−3 m−1 and −6.78 × 10−3 m−1 for all load levels. The new measuring system offers both good accuracy (1.33 × 10−3 m−1) and high precision (4.14 × 10−3 m−1). However, the results are based on quasi-static ski deformations, which means that a transfer into the field is only allowed to a limited extent since the scope of the curvature model has not yet been definitely determined. The high laboratory-related reliability and validity of our novel ski prototype featuring PyzoFlex® technology make it a potential candidate for on-snow application such as smart skiing equipment.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Snyder2021,

title = {Connected Skiing: Motion Quality Quantification in Alpine Skiing},

author = {Cory Snyder and Aaron Martínez and Rüdiger Jahnel and Jason Roe and Thomas Stöggl},

url = {https://www.mdpi.com/1424-8220/21/11/3779},

doi = {10.3390/s21113779},

year  = {2021},

date = {2021-05-29},

journal = {Sensors},

volume = {21},

number = {11},

pages = {13},

abstract = {Recent developments in sensing technology have made wearable computing smaller and cheaper. While many wearable technologies aim to quantify motion, there are few which aim to qualify motion. (2) To develop a wearable system to quantify motion quality during alpine skiing, IMUs were affixed to the ski boots of nineteen expert alpine skiers while they completed a set protocol of skiing styles, included carving and drifting in long, medium, and short radii. The IMU data were processed according to the previously published skiing activity recognition chain algorithms for turn segmentation, enrichment, and turn style classification Principal component models were learned on the time series variables edge angle, symmetry, radial force, and speed to identify the sources of variability in a subset of reference skiers. The remaining data were scored by comparing the PC score distributions of variables to the reference dataset. (3) The algorithm was able to differentiate between an expert and beginner skier, but not between an expert and a ski instructor, or a ski instructor and a beginner. (4) The scoring algorithm is a novel concept to quantify motion quality but is limited by the accuracy and relevance of the input data.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Martínez2021,

title = {A Comprehensive Comparison and Validation of Published Methods to Detect Turn Switch during Alpine Skiing},

author = {Aaron Martínez and Cory Snyder and Stephanie R. Moore and Thomas Stöggl},

url = {https://www.mdpi.com/1424-8220/21/7/2573/htm},

doi = {10.3390/s21072573 },

year  = {2021},

date = {2021-04-06},

journal = {Sensors},

volume = {21},

number = {7},

abstract = {The instant of turn switch (TS) in alpine skiing has been assessed with a variety of sensors and TS concepts. Despite many published methodologies, it is unclear which is best or how comparable they are. This study aimed to facilitate the process of choosing a TS method by evaluating the accuracy and precision of the methodologies previously used in literature and to assess the influence of the sensor type. Optoelectronic motion capture, inertial measurement units, pressure insoles, portable force plates, and electromyography signals were recorded during indoor treadmill skiing. All TS methodologies were replicated as stated in their respective publications. The method proposed by Supej assessed with optoelectronic motion capture was used as a comparison reference. TS time differences between the reference and each methodology were used to assess accuracy and precision. All the methods analyzed showed an accuracy within 0.25 s, and ten of them within 0.05 s. The precision ranged from ~0.10 s to ~0.60 s. The TS methodologies with the best performance (accuracy and precision) were Klous Video, Spörri PI (pressure insoles), Martinez CTD (connected boot), and Yamagiwa IMU (inertial measurement unit). In the future, the specific TS methodology should be chosen with respect to sensor selection, performance, and intended purpose.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Moore2020b,

title = {Foot Strike Angle Prediction and Pattern Classification Using LoadsolTM Wearable Sensors: A Comparison of Machine Learning Techniques (2020)},

author = {S. R. Moore and C Kranzinger and J. Fritz and T. Stöggl and J. Kröll and H. Schwameder},

url = {https://www.mdpi.com/1424-8220/20/23/6737},

doi = {10.3390/s20236737},

year  = {2020},

date = {2020-11-25},

journal = {Sensors},

volume = {20},

number = {23},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Neuwirth2020,

title = {Classification of Alpine Skiing Styles Using GNSS and Inertial Measurement Units (2020)},

author = {C. Neuwirth and C. Snyder and W. Kremser and R. Brunauer and H. Holzer and T. Stöggl},

url = {https://www.mdpi.com/1424-8220/20/15/4232},

doi = {10.3390/s20154232},

year  = {2020},

date = {2020-07-29},

journal = {Sensors},

volume = {20},

number = {15},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Martínez2020,

title = {Comparison of the Turn Switch Time Points Measured by Portable Force Platforms and Pressure Insoles (2020)},

author = {A. Martínez and K. Nakazato and P. Scheiber and C. Snyder and T. Stöggl},

url = {https://www.frontiersin.org/articles/10.3389/fspor.2020.00002/full},

doi = {10.3389/fspor.2020.00002},

year  = {2020},

date = {2020-01-23},

journal = {Front. Sports Act. Living},

keywords = {},

pubstate = {published},

tppubtype = {article}

}