Integrating Wearable Sensors and Video to Determine Microlocation-Specific Physiologic and Motion Biometrics-Method Development for Competitive Climbing

Abstract

Competitive indoor climbing has increased in popularity at the youth, collegiate, and Olympic levels. A critical aspect for improving performance is characterizing the physiologic response to different climbing strategies (e.g., work/rest patterns, pacing) and techniques (e.g., body position and movement) relative to location on climbing wall with spatially varying characteristics (e.g., wall inclinations, position of foot/hand holds). However, this response is not well understood due to the limited capabilities of climbing-specific measurement and assessment tools. In this study, we developed a novel method to examine time-resolved sensor-based measurements of multiple personal biometrics at different microlocations (finely spaced positions; MLs) along a climbing route. For the ML-specific biometric system (MLBS), we integrated continuous data from wearable biometric sensors and smartphone-based video during climbing, with a customized visualization and analysis system to determine three physiologic parameters (heart rate, breathing rate, ventilation rate) and one body movement parameter (hip acceleration), which are automatically time-matched to the corresponding video frame to determine ML-specific biometrics. Key features include: (1) biometric sensors that are seamlessly embedded in the fabric of an athletic compression shirt, and do not interfere with climbing performance, (2) climbing video, and (3) an interactive graphical user interface to rapidly visualize and analyze the time-matched biometrics and climbing video, determine timing sequence between the biometrics at key events, and calculate summary statistics. To demonstrate the capabilities of MLBS, we examined the relationship between changes in ML-specific climbing characteristics and changes in the physiologic parameters. Our study demonstrates the ability of MLBS to determine multiple time-resolved biometrics at different MLs, in support of developing and assessing different climbing strategies and training methods to help improve performance.

Wearable and Portable Devices for Acquisition of Cardiac Signals while Practicing Sport: A Scoping Review

“…Although chest bands offer greater accuracy in HR monitoring and cost less, wristbands are more desirable, because of their multifunctionality and comfort. In sport, sensor-embedded equipment and smart textiles are also exploited to enable users to have high-quality signals without hindering any movement [ 31 , 44 , 55 ].…”

“…simultaneously). Sensor-embedded equipment and smart textiles may enable users to have high-quality signals without hindering any movement, especially in contact sports [ 31 , 44 , 55 ], hence the development of a validation protocol for wearable devices measuring cardiac signals is desirable. With this common aim, six universities and one industrial partner joined to present a set of guidelines to obtain more comparable data.…”

“…As for the algorithms, each study was conducted with a different aim and different devices were used. The Hexoskin biometric compression shirt was used in [ 55 ] to demonstrate the capability of the microlocation-specific biometric system. The BioHarness 3.0 was used in [ 49 ] for the proposal of a tool called CaRiSMA 1.0, in [ 51 ] for the presentation of a predictive analytics framework for predicting soccer players’ performance data, and in [ 52 ] for the development of an algorithm for automatic detection of training phases.…”

Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities

“…Breen 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 learningspecific biometrics in the context of optimizing climbing performance.…”

Deep Learning‐Based Skin Diseases Classification using Smartphones

“…Both connectivity and sensors made smartphones a part of our daily lives. Today smartphones are used in many different areas like healthcare, [8][9][10][11][12] sports, [13] disasters, [14,15] agriculture, [16,17] and special needs. [18] This study presents a mobile system for diagnosis and monitoring of skin diseases.…”