Towards an Innovative Model in Wearable Expert System for Skiing

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Abstract

Mobile applications and portable devices are being used extensively in the healthcare sector due to their rapid development. Wearable devices having sensors can be used to collect, analyze, and transmit the vital signs of the wearer. In this paper, we have proposed a wearable expert system that supports and monitors the skier during his activity. This research work is motivated by the need to provide rapid assistance to skiers, especially during off-piste skiing, where its more dangerous, and seeking help is difficult with mishaps. Our approach mainly focuses on proposing an expert system that integrates wearable devices (helmet, goggles, digital watch) with the skier’s smartphone. We present an architecture model and knowledge artifacts to design a wearable expert system for skiing.

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Most cited references 7

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Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions.

M Ermes, J Mantyjarvi, I Korhonen … (2007)

Physical activity has a positive impact on people's well-being, and it may also decrease the occurrence of chronic diseases. Activity recognition with wearable sensors can provide feedback to the user about his/her lifestyle regarding physical activity and sports, and thus, promote a more active lifestyle. So far, activity recognition has mostly been studied in supervised laboratory settings. The aim of this study was to examine how well the daily activities and sports performed by the subjects in unsupervised settings can be recognized compared to supervised settings. The activities were recognized by using a hybrid classifier combining a tree structure containing a priori knowledge and artificial neural networks, and also by using three reference classifiers. Activity data were collected for 68 h from 12 subjects, out of which the activity was supervised for 21 h and unsupervised for 47 h. Activities were recognized based on signal features from 3-D accelerometers on hip and wrist and GPS information. The activities included lying down, sitting and standing, walking, running, cycling with an exercise bike, rowing with a rowing machine, playing football, Nordic walking, and cycling with a regular bike. The total accuracy of the activity recognition using both supervised and unsupervised data was 89% that was only 1% unit lower than the accuracy of activity recognition using only supervised data. However, the accuracy decreased by 17% unit when only supervised data were used for training and only unsupervised data for validation, which emphasizes the need for out-of-laboratory data in the development of activity-recognition systems. The results support a vision of recognizing a wider spectrum, and more complex activities in real life settings.

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Wearable Performance Devices in Sports Medicine

Ryan T. Li, Scott Kling, Michael Salata … (2016)

Context: Wearable performance devices and sensors are becoming more readily available to the general population and athletic teams. Advances in technology have allowed individual endurance athletes, sports teams, and physicians to monitor functional movements, workloads, and biometric markers to maximize performance and minimize injury. Movement sensors include pedometers, accelerometers/gyroscopes, and global positioning satellite (GPS) devices. Physiologic sensors include heart rate monitors, sleep monitors, temperature sensors, and integrated sensors. The purpose of this review is to familiarize health care professionals and team physicians with the various available types of wearable sensors, discuss their current utilization, and present future applications in sports medicine. Evidence Acquisition: Data were obtained from peer-reviewed literature through a search of the PubMed database. Included studies searched development, outcomes, and validation of wearable performance devices such as GPS, accelerometers, and physiologic monitors in sports. Study Design: Clinical review. Level of Evidence: Level 4. Results: Wearable sensors provide a method of monitoring real-time physiologic and movement parameters during training and competitive sports. These parameters can be used to detect position-specific patterns in movement, design more efficient sports-specific training programs for performance optimization, and screen for potential causes of injury. More recent advances in movement sensors have improved accuracy in detecting high-acceleration movements during competitive sports. Conclusion: Wearable devices are valuable instruments for the improvement of sports performance. Evidence for use of these devices in professional sports is still limited. Future developments are needed to establish training protocols using data from wearable devices.

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A methodology for validating artifact removal techniques for physiological signals.

Hasan Ayaz, Meltem Izzetoglu, Kevin Sweeney … (2012)

Artifact removal from physiological signals is an essential component of the biosignal processing pipeline. The need for powerful and robust methods for this process has become particularly acute as healthcare technology deployment undergoes transition from the current hospital-centric setting toward a wearable and ubiquitous monitoring environment. Currently, determining the relative efficacy and performance of the multiple artifact removal techniques available on real world data can be problematic, due to incomplete information on the uncorrupted desired signal. The majority of techniques are presently evaluated using simulated data, and therefore, the quality of the conclusions is contingent on the fidelity of the model used. Consequently, in the biomedical signal processing community, there is considerable focus on the generation and validation of appropriate signal models for use in artifact suppression. Most approaches rely on mathematical models which capture suitable approximations to the signal dynamics or underlying physiology and, therefore, introduce some uncertainty to subsequent predictions of algorithm performance. This paper describes a more empirical approach to the modeling of the desired signal that we demonstrate for functional brain monitoring tasks which allows for the procurement of a "ground truth" signal which is highly correlated to a true desired signal that has been contaminated with artifacts. The availability of this "ground truth," together with the corrupted signal, can then aid in determining the efficacy of selected artifact removal techniques. A number of commonly implemented artifact removal techniques were evaluated using the described methodology to validate the proposed novel test platform.

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Author and article information

Contributors

Emmanouel Garoufallou: garoufallou@gmail.com

María-Antonia Ovalle-Perandones: maovalle@ucm.es

Elson Kurian:

ORCID: http://orcid.org/0000-0002-6392-0870

e.kurian@campus.unimib.it

Sherwin Varghese:

ORCID: http://orcid.org/0000-0003-0191-8107

sherwin.varghese@sap.com

Stefano Fiorini:

ORCID: http://orcid.org/0000-0001-5432-7584

s.fiorini2@campus.unimib.it

Journal

Journal ID (publisher-id): 978-3-030-71903-6

Journal ID (doi): 10.1007/978-3-030-71903-6

Journal ID (nlm-ta): Metadata and Semantic Research

Title: Metadata and Semantic Research

Subtitle: 14th International Conference, MTSR 2020, Madrid, Spain, December 2–4, 2020, Revised Selected Papers

ISBN (Print): 978-3-030-71902-9

ISBN (Electronic): 978-3-030-71903-6

Publication date (Electronic): 22 February 2021

Publication date Collection: 2021

Volume: 1355

Pages: 403-410

Affiliations

[5 ]GRID grid.449057.b, ISNI 0000 0004 0416 1485, International Hellenic University, ; Thessaloniki, Greece

[6 ]GRID grid.4795.f, ISNI 0000 0001 2157 7667, Complutense University of Madrid, ; Madrid, Spain

[7 ]GRID grid.7563.7, ISNI 0000 0001 2174 1754, University of Milano-Bicocca, ; Viale Sarca 336, 20136 Milan, Italy

[8 ]SAP Labs Rd, EPIP Zone, Whitefield, Bengaluru, 560066 Karnataka India

Author information

Elson Kurian http://orcid.org/0000-0002-6392-0870

Sherwin Varghese http://orcid.org/0000-0003-0191-8107

Stefano Fiorini http://orcid.org/0000-0001-5432-7584

Article

Publisher ID: 37

DOI: 10.1007/978-3-030-71903-6_37

PMC ID: 7971798

SO-VID: 3cee4c29-ff56-4df1-bf3e-1ce6e4e907d7

License:

This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

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Keywords: wearable expert system,skiing,knowledge engineering,wearable devices,internet of things

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Keywords: wearable expert system, skiing, knowledge engineering, wearable devices, internet of things

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Towards an Innovative Model in Wearable Expert System for Skiing

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Most cited references 7

Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions.

Wearable Performance Devices in Sports Medicine

A methodology for validating artifact removal techniques for physiological signals.

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