# Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities
## 7 Applications of Wearables in Sports
### Other Sports  
In this study, the other sports category was created for applications other than more specific sports such as team or swing sports. This includes various sports such as climbing, running or swimming, as presented in Table 4.  
[PERSON] et al. aimed to extract four time-resolved sensor-based biometrics from a non-invasive athletic compression shirt integrated with a climbing video [130]. Using a Hexoskin WT device, hip acceleration, respiration rate, minute ventilation, and heart rate were recorded every 1 s. The results demonstrated the feasibility of using time-dependent non-invasive sensor data, along with climbing videos, for obtaining machine learning-specific biometrics in the context of optimizing climbing performance. WT can be used in swimming to monitor and improve swimmers' performance by providing feedback on posture, motion, speed, heart rate, and other physiological parameters. [PERSON] et al. proposed a framework for intelligent swimming analytics using wearable sensors for stroke classification [131], [PERSON] et al. developed a swimming motion analysis and posture recognition system using inertial sensors [132], and [PERSON] et al. designed a motion capture system for human swimming posture reconstruction using multisensor data fusion algorithms and a human biomechanical model [133]. [PERSON] et al. developed a classification model based on subject-specific changes in biomechanical gait patterns recorded with Lumo Run to classify changes in recreational runners' biomechanics in non-laboratory settings [52]. Pelvic drop, vertical oscillation of the pelvis, ground contact time, braking, pelvic rotation, and cadence were recorded under two different environmental conditions: winter running (mid-February to mid-March) and spring running (end of April to mid-May). The findings of the study supported the classification of subject-specific gait pattern changes based on weather conditions with an accuracy of over 80% and highlighted the importance of variable ranking in subject-specific machine-learning models. [PERSON] et al. conducted a randomized, placebo-controlled 8-week crossover study in novice runners [134]. The study aimed to determine the effects of graduated compression garments worn after training on recovery using daily heart rate variability (HRV) measures. Throughout the study, successive RR-interval differences and HRVs of all runners were monitored using a physiological monitor worn on the wrist (Biostrap). The results suggested that the use of graduated compression garments after training could be beneficial in mitigating some adverse effects of excessive exercise by attenuating the impact on vagally-mediated HRV. [PERSON] et al., evaluated the validity of a GNSS device (Catapult) to measure swimming kinematic variables [49]. The study involved 12 male and 9 female high-level swimmers who completed three 100 m efforts in butterfly, breaststroke, and freestyle. The mid-pool swimming velocity and stroke count obtained from a digital video recording synchronized with a GNSS device (minimax S4, Catapult) equipped with a tri-axial accelerometer were compared with the velocity and stroke count data derived from the video analysis. The validity of the integrated accelerometer and GNSS device was established for stroke count measurement in breaststroke and butterfly styles and for mid-pool swimming velocity in freestyle and breaststroke styles. [PERSON] et al. examined the relationship between low back pain (LBP) and lumbar lordosis in functional dance positions (standing with feet parallel, first position, grand plie in second position, _cambre_, _retre_ from fifth position, _develope a la seconde_, and first arabesque) in twenty-eight female and two male collegiate dancers [135]. The study also investigated the associations of hip passive range of motion (PROM) and core stability with LBP. Wearable motion sensors (dorsaVi Kew) were used for measuring lumbar lordosis.