18
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Review on Wearable Technology Sensors Used in Consumer Sport Applications

      review-article
      , , *
      Sensors (Basel, Switzerland)
      MDPI
      wearable technology, sensors, data, smart, sport

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          This review paper discusses the trends and projections for wearable technology in the consumer sports sector (excluding professional sport). Analyzing the role of wearable technology for different users and why there is such a need for these devices in everyday lives. It shows how different sensors are influential in delivering a variety of readings that are useful in many ways regarding sport attributes. Wearables are increasing in function, and through integrating technology, users are gathering more data about themselves. The amount of wearable technology available is broad, each having its own role to play in different industries. Inertial measuring unit (IMU) and Global Positioning System (GPS) sensors are predominantly present in sport wearables but can be programmed for different needs. In this review, the differences are displayed to show which sensors are compatible and which ones can evolve sensor technology for sport applications.

          Related collections

          Most cited references88

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Wearable Sensors for Remote Health Monitoring

          Life expectancy in most countries has been increasing continually over the several few decades thanks to significant improvements in medicine, public health, as well as personal and environmental hygiene. However, increased life expectancy combined with falling birth rates are expected to engender a large aging demographic in the near future that would impose significant  burdens on the socio-economic structure of these countries. Therefore, it is essential to develop cost-effective, easy-to-use systems for the sake of elderly healthcare and well-being. Remote health monitoring, based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers an efficient and cost-effective solution that allows the elderly to continue to live in their comfortable home environment instead of expensive healthcare facilities. These systems will also allow healthcare personnel to monitor important physiological signs of their patients in real time, assess health conditions and provide feedback from distant facilities. In this paper, we have presented and compared several low-cost and non-invasive health and activity monitoring systems that were reported in recent years. A survey on textile-based sensors that can potentially be used in wearable systems is also presented. Finally, compatibility of several communication technologies as well as future perspectives and research challenges in remote monitoring systems will be discussed.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Accumulated workloads and the acute:chronic workload ratio relate to injury risk in elite youth football players

            Aim The purpose of this study was to investigate the relationship between physical workload and injury risk in elite youth football players. Methods The workload data and injury incidence of 32 players were monitored throughout 2 seasons. Multiple regression was used to compare cumulative (1, 2, 3 and 4-weekly) loads and acute:chronic (A:C) workload ratios (acute workload divided by chronic workload) between injured and non-injured players for specific GPS and accelerometer-derived variables:total distance (TD), high-speed distance (HSD), accelerations (ACC) and total load. Workloads were classified into discrete ranges by z-scores and the relative risk was determined. Results A very high number of ACC (≥9254) over 3 weeks was associated with the highest significant overall (relative risk (RR)=3.84) and non-contact injury risk (RR=5.11). Non-contact injury risk was significantly increased when a high acute HSD was combined with low chronic HSD (RR=2.55), but not with high chronic HSD (RR=0.47). Contact injury risk was greatest when A:C TD and ACC ratios were very high (1.76 and 1.77, respectively) (RR=4.98). Conclusions In general, higher accumulated and acute workloads were associated with a greater injury risk. However, progressive increases in chronic workload may develop the players' physical tolerance to higher acute loads and resilience to injury risk.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Evaluating the Validity of Current Mainstream Wearable Devices in Fitness Tracking Under Various Physical Activities: Comparative Study

              Background Wearable devices have attracted much attention from the market in recent years for their fitness monitoring and other health-related metrics; however, the accuracy of fitness tracking results still plays a major role in health promotion. Objective The aim of this study was to evaluate the accuracy of a host of latest wearable devices in measuring fitness-related indicators under various seminatural activities. Methods A total of 44 healthy subjects were recruited, and each subject was asked to simultaneously wear 6 devices (Apple Watch 2, Samsung Gear S3, Jawbone Up3, Fitbit Surge, Huawei Talk Band B3, and Xiaomi Mi Band 2) and 2 smartphone apps (Dongdong and Ledongli) to measure five major health indicators (heart rate, number of steps, distance, energy consumption, and sleep duration) under various activity states (resting, walking, running, cycling, and sleeping), which were then compared with the gold standard (manual measurements of the heart rate, number of steps, distance, and sleep, and energy consumption through oxygen consumption) and calculated to determine their respective mean absolute percentage errors (MAPEs). Results Wearable devices had a rather high measurement accuracy with respect to heart rate, number of steps, distance, and sleep duration, with a MAPE of approximately 0.10, whereas poor measurement accuracy was observed for energy consumption (calories), indicated by a MAPE of up to 0.44. The measurements varied for the same indicator measured by different fitness trackers. The variation in measurement of the number of steps was the highest (Apple Watch 2: 0.42; Dongdong: 0.01), whereas it was the lowest for heart rate (Samsung Gear S3: 0.34; Xiaomi Mi Band 2: 0.12). Measurements differed insignificantly for the same indicator measured under different states of activity; the MAPE of distance and energy measurements were in the range of 0.08 to 0.17 and 0.41 to 0.48, respectively. Overall, the Samsung Gear S3 performed the best for the measurement of heart rate under the resting state (MAPE of 0.04), whereas Dongdong performed the best for the measurement of the number of steps under the walking state (MAPE of 0.01). Fitbit Surge performed the best for distance measurement under the cycling state (MAPE of 0.04), and Huawei Talk Band B3 performed the best for energy consumption measurement under the walking state (MAPE of 0.17). Conclusions At present, mainstream devices are able to reliably measure heart rate, number of steps, distance, and sleep duration, which can be used as effective health evaluation indicators, but the measurement accuracy of energy consumption is still inadequate. Fitness trackers of different brands vary with regard to measurement of indicators and are all affected by the activity state, which indicates that manufacturers of fitness trackers need to improve their algorithms for different activity states.
                Bookmark

                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                28 April 2019
                May 2019
                : 19
                : 9
                : 1983
                Affiliations
                Design Department, Brunel University, London UB8 3PH, UK; 1203998@ 123456brunel.ac.uk (G.A.); Nadarajah.Manivannan@ 123456brunel.ac.uk (N.M.)
                Author notes
                [* ]Correspondence: David.Harrison@ 123456brunel.ac.uk ; Tel.: +44-(0)1895-274000; Fax: +44-(0)1895-232806
                Author information
                https://orcid.org/0000-0002-5466-2582
                Article
                sensors-19-01983
                10.3390/s19091983
                6540270
                31035333
                44df404f-c41d-40bb-8342-21051d6e5e7d
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 24 March 2019
                : 24 April 2019
                Categories
                Review

                Biomedical engineering
                wearable technology,sensors,data,smart,sport
                Biomedical engineering
                wearable technology, sensors, data, smart, sport

                Comments

                Comment on this article