Key Points ▸ Wearable sensors enable recognition of unique outdoor training and racing activities. ▸ Spatiotemporal outcomes recorded by sensors reflected speed training and racing. ▸ These individual running cases set up for larger scale in-field running gait assessments. Outdoor running
Alexandra F. DeJong and Jay Hertel
Abigail M. Tyson, Stefan M. Duma and Steven Rowson
, 33 Studies have suggested that many wearable sensors may have insufficient sampling rate and bandwidth to capture high-frequency, short-duration unhelmeted impacts. 32 , 34 With a surge in relatively inexpensive, user-friendly wearable head impact sensors in the consumer market, coupled with
Brooklynn M. Knowles, Henry Yu and Christopher R. Dennison
Wearable kinematic sensors can be used to study head injury biomechanics based on kinematics and, more recently, based on tissue strain metrics using kinematics-driven brain models. These sensors require in-situ calibration and there is currently no data conveying wearable ability to estimate tissue strain. We simulated head impact (n = 871) to a 50th percentile Hybrid III (H-III) head wearing a hockey helmet instrumented with wearable GForceTracker (GFT) sensors measuring linear acceleration and angular velocity. A GFT was also fixed within the H-III head to establish a lower boundary on systematic errors. We quantified GFT errors relative to H-III measures based on peak kinematics and cumulative strain damage measure (CSDM). The smallest mean errors were 12% (peak resultant linear acceleration) and 15% (peak resultant angular velocity) for the GFT within the H-III. Errors for GFTs on the helmet were on average 54% (peak resultant linear acceleration) and 21% (peak resultant angular velocity). On average, the GFT inside the helmet overestimated CSDM by 0.15.
Jennifer L. Huberty, Jeni L. Matthews, Meynard Toledo, Lindsay Smith, Catherine L. Jarrett, Benjamin Duncan and Matthew P. Buman
Purpose: To estimate the energy expenditure (EE) of Vinyasa Flow and validate the Actigraph (AG) and GENEActiv (GA) for measuring EE in Vinyasa Flow. Methods: Participants (N = 22) were fitted to a mask attached to the Oxycon. An AG was placed on the left hip and a GA was placed on the non-dominant wrist. Participants were randomized to an initial resting activity before completing a 30-minute Vinyasa Flow video. AG data was scored using the Freedson VM3 (2011) and the Freedson Adult (1998) algorithms in the Actilife software platform. EE from GA were derived using cut points from a previous study. Date and time filters were added corresponding to the time stamps recorded by the tablet video files of each yoga session. Kcals and METs expended by participants were calculated using bodyweight measured during their visit. Data was analyzed using SPSS. A dependent samples t-test, an intraclass correlation coefficient (ICC), and mean absolute difference were used to determine agreement between variables. Results: According to the Oxycon, participation in Vinyasa Flow required an average EE of 3.2 ± 0.4 METs. The absolute agreement between the Oxycon, AG, or GA was poor (ICC < .20). The mean difference in METs for the AG was −2.1 ± 0.6 and GA was −1.4 ± 0.6 (all p < .01). Conclusion: According to the Oxycon, participation in Vinyasa Flow met the criteria for moderate-intensity physical activity. The AG and GA consistently underestimated EE. More research is needed to determine an accurate measurement for EE during yoga using a wearable device appropriate for free-living environments.
Reed D. Gurchiek, Hasthika S. Rupasinghe Arachchige Don, Lasanthi C. R. Pelawa Watagoda, Ryan S. McGinnis, Herman van Werkhoven, Alan R. Needle, Jeffrey M. McBride and Alan T. Arnholt
-down integration methods . Med Eng Phys . 2014 ; 36 ( 10 ): 1312 – 1321 . PubMed ID: 25199588 doi:10.1016/j.medengphy.2014.07.022 25199588 10.1016/j.medengphy.2014.07.022 21. McGinnis RS , Mahadevan N , Moon Y , et al . A machine learning approach for gait speed estimation using skin-mounted wearable
Tsuyoshi Saida, Masayuki Kawada, Daijiro Kuroki, Yuki Nakai, Takasuke Miyazaki, Ryoji Kiyama and Yasuhiro Tsuneyoshi
. However, the regularity of acceleration of the neck deteriorated post-TKA; this was inconsistent with our hypothesis. To our knowledge, this is the first study to analyze alterations in trunk fluctuation, symmetry, and regularity of gait following TKA in patients with knee OA using wearable sensors. The
Paul G. Montgomery and Brendan D. Maloney
Purpose: To determine the changes in game performance during tournament play of elite 3×3 basketball. Methods: A total of 361 men and 208 women competing in selected international tournaments had game demands assessed by wearable technology (global positioning system, inertial sensor, and heart rate) along with postgame blood lactate and perceived responses. Differences in the means for selected variables between games were compared using magnitude-based inferences and reported with effect size and associated confidence limits (CL), along with the percentage difference (ES; ±90% CL, %difference) of log-transformed data. Results: No clear differences were seen over a tournament period in PlayerLoad™ or PlayerLoad·minute−1. Tournament competition elicits variable changes between games for all inertial measures. Average peak heart rate was 198 (10) and 198 (9) beats·min−1, and average game heart rate was 164 (12) and 165 (18) beats·min−1 for men and women, respectively, with no change between games. Average game lactate was 6.3 (2.4) and 6.1 (2.2) mmol·L−1 for men and women, respectively. Average game ratings of perceived exertion were 5.7 (2.1) and 5.4 (2.0) AU for men and women, respectively. Although lactate and ratings of perceived exertion were variable between games, there was no difference over a tournament. Conclusions: The physical and physiological demands of elite 3×3 games over the duration of a tournament are similar regardless of pool or championship rounds. This may imply that maintaining technical and strategic aspects leads to success rather than minimizing fatigue through superior physical preparation. However, the physiological responses are high; caution is warranted in being underprepared for these demands in tournament play.
Anna Pulakka, Eric J. Shiroma, Tamara B. Harris, Jaana Pentti, Jussi Vahtera and Sari Stenholm
, & Katzmarzyk, 2014 ), Tracy ( Tracy et al., 2014 ) and Van Hees ( van Hees et al., 2015 ). Different methods have been used to separate wear time from non-wear time including algorithms, participant logs and wear sensors, all of which have their own strengths and weaknesses. Commonly used non-wear algorithms
Ivan A. Trujillo-Priego, Judy Zhou, Inge F. Werner, Weiyang Deng and Beth A. Smith
associated with improved motor development ( Carson et al., 2017 ). Studying infant activity and movement in the daily environment is important for determining optimal practices for promoting infant health and development. Wearable sensors can be used to characterize the quantity, acceleration, and type of
Christian A. Clermont, Lauren C. Benson, W. Brent Edwards, Blayne A. Hettinga and Reed Ferber
accuracy, sensitivity, and computing power, 16 wearable sensors have the potential to be an effective tool to measure the effects of fatigue on running biomechanics in the field, 17 – 20 but the research in this area is still in the early stages of development. 21 , 22 Reenalda et al 19 presented