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Sensor-Based Information for Personalized Exercise and Training

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 24259

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Special Issue Editors

Prof. Dr. Billy Sperlich

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Guest Editor

Institute of Sport Science, University of Würzburg, 97082 Würzburg, Germany
Interests: wearable sensor technology, endurance, load monitoring, elite performance, cardiorespiratory fitness, knowledge transfer

Prof. Peter Düking

E-Mail Website
Guest Editor

University of Würzburg, 97082 Würzburg, Germany
Interests: wearable sensor technology; endurance; elite performance; cardiorespiratory fitness

Special Issue Information

Dear Colleagues,

An increasing number of lightweight and wearable sensors (“wearables”) are being developed or are already commercially available for monitoring physiological, biomechanical or ambient data. Innovations are mainly in sensor technology, data algorithms (especially machine learning), and content- and application-related interpretation. The application of wearable sensors (e.g., using biofeedback) is thought to improve understanding of adaptation to exercise, (elite) performance, cardiorespiratory fitness, physical activity, and other health-related aspects.

The reliability and validity of provided data is often questionable, and further evidence is needed to prove the anticipated benefits of data monitoring and guidance with wearable sensor technology.

To improve the field of wearables for exercise, health, and performance applications, this Special Issue aims to publish manuscripts which:

Identify new sensors and algorithms (especially machine learning) in the field of exercise, health and performance applications;
Validate existing sensors and algorithms;
Correlate and/or interpret data with respect to exercise, health and/ or performance specific applications;
Evaluate effects of using wearable sensor technologies in the context of exercise, health and performance applications.

Prof. Dr. Billy Sperlich
Dr. Peter Düking
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

fitness
physical activity
wearable
sensor
algorithm
machine learning

Published Papers (6 papers)

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16 pages, 1038 KiB

Open AccessArticle

Comparison of Self-Reported and Device-Based Measured Physical Activity Using Measures of Stability, Reliability, and Validity in Adults and Children

by Janis Fiedler, Tobias Eckert, Alexander Burchartz, Alexander Woll and Kathrin Wunsch

Sensors 2021, 21(8), 2672; https://doi.org/10.3390/s21082672 - 10 Apr 2021

Cited by 20 | Viewed by 4166

Abstract

Quantification of physical activity (PA) depends on the type of measurement and analysis method making it difficult to compare adherence to PA guidelines. Therefore, test-retest reliability, validity, and stability for self-reported (i.e., questionnaire and diary) and device-based measured (i.e., accelerometry with 10/60 s epochs) PA was compared in 32 adults and 32 children from the SMARTFAMILY study to examine if differences in these measurement tools are systematic. PA was collected during two separate measurement weeks and the relationship for each quality criteria was analyzed using Spearman correlation. Results showed the highest PA values for questionnaires followed by 10-s and 60-s epochs measured by accelerometers. Levels of PA were lowest when measured by diary. Only accelerometry demonstrated reliable, valid, and stable results for the two measurement weeks, the questionnaire yielded mixed results and the diary showed only a few significant correlations. Overall, higher correlations for the quality criteria were found for moderate than for vigorous PA and the results differed between children and adults. Since the differences were not found to be systematic, the choice of measurement tools should be carefully considered by anyone working with PA outcomes, especially if vigorous PA is the parameter of interest. Full article

(This article belongs to the Special Issue Sensor-Based Information for Personalized Exercise and Training)

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18 pages, 2434 KiB

Open AccessArticle

Indirect Estimation of Vertical Ground Reaction Force from a Body-Mounted INS/GPS Using Machine Learning

by Dharmendra Sharma, Pavel Davidson, Philipp Müller and Robert Piché

Sensors 2021, 21(4), 1553; https://doi.org/10.3390/s21041553 - 23 Feb 2021

Cited by 17 | Viewed by 3107

Abstract

Vertical ground reaction force (vGRF) can be measured by force plates or instrumented treadmills, but their application is limited to indoor environments. Insoles remove this restriction but suffer from low durability (several hundred hours). Therefore, interest in the indirect estimation of vGRF using inertial measurement units and machine learning techniques has increased. This paper presents a methodology for indirectly estimating vGRF and other features used in gait analysis from measurements of a wearable GPS-aided inertial navigation system (INS/GPS) device. A set of 27 features was extracted from the INS/GPS data. Feature analysis showed that six of these features suffice to provide precise estimates of 11 different gait parameters. Bagged ensembles of regression trees were then trained and used for predicting gait parameters for a dataset from the test subject from whom the training data were collected and for a dataset from a subject for whom no training data were available. The prediction accuracies for the latter were significantly worse than for the first subject but still sufficiently good. K-nearest neighbor (KNN) and long short-term memory (LSTM) neural networks were then used for predicting vGRF and ground contact times. The KNN yielded a lower normalized root mean square error than the neural network for vGRF predictions but cannot detect new patterns in force curves. Full article

(This article belongs to the Special Issue Sensor-Based Information for Personalized Exercise and Training)

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13 pages, 5889 KiB

Open AccessCommunication

Validation of Player and Ball Tracking with a Local Positioning System

by Patrick Blauberger, Robert Marzilger and Martin Lames

Sensors 2021, 21(4), 1465; https://doi.org/10.3390/s21041465 - 20 Feb 2021

Cited by 18 | Viewed by 5472

Abstract

The aim of this study was the validation of player and ball position measurements of Kinexon’s local positioning system (LPS) in handball and football. Eight athletes conducted a sport-specific course (SSC) and small sided football games (SSG), simultaneously tracked by the LPS and an infrared camera-based motion capture system as reference system. Furthermore, football shots and handball throws were performed to evaluate ball tracking. The position root mean square error (RMSE) for player tracking was 9 cm for SSCs, the instantaneous peak speed showed a percentage deviation from the reference system of 0.7–1.7% for different exercises. The RMSE for SSGs was 8 cm. Covered distance was overestimated by 0.6% in SSCs and 1.0% in SSGs. The 2D RMSE of ball tracking was 15 cm in SSGs, 3D position errors of shot and throw impact locations were 17 cm and 21 cm. The methodology for the validation of a system’s accuracy in sports tracking requires extensive attention, especially in settings covering both, player and ball measurements. Most tracking errors for player tracking were smaller or in line with errors found for comparable systems in the literature. Ball tracking showed a larger error than player tracking. Here, the influence of the positioning of the sensor must be further reviewed. In total, the accuracy of Kinexon’s LPS has proven to represent the current state of the art for player and ball position detection in team sports. Full article

(This article belongs to the Special Issue Sensor-Based Information for Personalized Exercise and Training)

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10 pages, 1610 KiB

Open AccessArticle

The Relationship between Neuromuscular Control and Physical Activity in the Formation of the Visual-Psychomotor Schemes in Preschools

by Roxana Buzescu, Florentina Nechita and Silviu Gabriel Cioroiu

Sensors 2021, 21(1), 224; https://doi.org/10.3390/s21010224 - 31 Dec 2020

Cited by 5 | Viewed by 2557

Abstract

Background: This research has started from the empirical observation that preschoolers who practice systematic and continuous physical activities can solve the tasks they receive more accurately and in less time than those who do not do sports in an organized setting. Methods: The research was carried out in 2015 in the Laboratory of Physical Therapy and Special Motricity of the Faculty of Physical Education and Mountain Sports, Transilvania University of Brasov. The survey sample included 51 preschoolers (26 boys and 25 girls), and the study implemented “real experiment” type research with a post-test phase to find out to what extent cortical stability is dependent on practicing a form of systematic movement at the ages of 4–6 years by analyzing proprioceptive sense and neuromuscular control. Thus, we could see how a 4-to-6-year-old child’s brain responds to a given stimulus by using the ERGOSIM condition simulator, which provides real-time feedback. Results: The results of the study show significant values for the visual control of the subjects by adjusting movement. Conclusions: The practice of physical activities benefits from learning through the visual scheme, having real-time feedback, and subjects being able to maintain indices closer to the required model, on the one hand, and on the other, to return with spherical correction stimuli during a wrong move much better than those in the control group. The results suggest that systematic practice of psychomotricity can improve general development and cognition in children, and that implementing this methodology could thus be useful in educative intervention. Full article

(This article belongs to the Special Issue Sensor-Based Information for Personalized Exercise and Training)

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13 pages, 6402 KiB

Open AccessArticle

Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study

by Padraig Davidson, Peter Düking, Christoph Zinner, Billy Sperlich and Andreas Hotho

Sensors 2020, 20(9), 2637; https://doi.org/10.3390/s20092637 - 05 May 2020

Cited by 5 | Viewed by 4016

Abstract

The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot [...] Read more.

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in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1

Hz

) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of

84.8

% for the whole dataset,

81.8

% for the trained runners, and

86.1

% for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions. Full article

(This article belongs to the Special Issue Sensor-Based Information for Personalized Exercise and Training)

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10 pages, 568 KiB

Open AccessLetter

Validity of a Local Positioning System during Outdoor and Indoor Conditions for Team Sports

by Prisca S. Alt, Christian Baumgart, Olaf Ueberschär, Jürgen Freiwald and Matthias W. Hoppe

Sensors 2020, 20(20), 5733; https://doi.org/10.3390/s20205733 - 09 Oct 2020

Cited by 23 | Viewed by 3505

Abstract

This study aimed to compare the validity of a local positioning system (LPS) during outdoor and indoor conditions for team sports. The impact of different filtering techniques was also investigated. Five male team sport athletes (age: 27 ± 2 years; maximum oxygen uptake: 48.4 ± 5.1 mL/min/kg) performed 10 trials on a team sport-specific circuit on an artificial turf and in a sports hall. During the circuit, athletes wore two devices of a recent 20-Hz LPS. From the reported raw and differently filtered velocity data, distances covered during different walking, jogging, and sprinting sections within the circuit were computed for which the circuit was equipped with double-light timing gates as criterion measures. The validity was determined by comparing the known and measured distances via the relative typical error of estimate (TEE). The LPS validity for measuring distances covered was good to moderate during both environments (TEE: 0.9–7.1%), whereby the outdoor validity (TEE: 0.9–6.4%) was superior than indoor validity (TEE: 1.2–7.1%). During both environments, validity outcomes of an unknown manufacturer filter were superior (TEE: 0.9–6.2%) compared to those of a standard Butterworth filter (TEE: 0.9–6.4%) and to unprocessed raw data (TEE: 1.0–7.1%). Our findings show that the evaluated LPS can be considered as a good to moderately valid tracking technology to assess running-based movement patterns in team sports during outdoor and indoor conditions. However, outdoor was superior to indoor validity, and also impacted by the applied filtering technique. Our outcomes should be considered for practical purposes like match and training analyses in team sport environments. Full article

(This article belongs to the Special Issue Sensor-Based Information for Personalized Exercise and Training)

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