ECSS Paris 2023: CP-AP06
INTRODUCTION: Monitoring blood lactate (BL) concentration is a cornerstone of physiological profiling and training prescription in sports science. Conventional capillary sampling is invasive and provides only discrete data points, whereas wearable sweat-sensing technologies offer a non-invasive, continuous alternative. However, the physiological relevance and temporal dynamics of sweat lactate (SL) are not yet fully understood. This pilot study investigated the feasibility and temporal alignment of a novel wearable SL sensor, integrated onto an ultra-thin, conformable nanofilm, compared to gold-standard BL measurements during incremental cycling exercise. METHODS: Eight recreational cyclists (4 males, 4 females) performed an incremental cycling protocol under controlled laboratory conditions (25°C, 50%RH). The protocol commenced at 60 W, with workload increased by 20 W every 4 min until volitional exhaustion. Cadence was maintained at 80 rpm. Continuous SL was captured via an electrochemical sensor integrated onto a porous ultra-high-molecular-weight polyethylene (UHMWPE) nanofilm at the mid-anterior forearm [1]. BL was obtained during the final 10s of each 4-min stage. Data were normalized via min-max scaling, and a cross-correlation optimization algorithm (grid search: 0 to 1200s, 10s resolution) was employed to identify the maximal phase-coupling between systemic production and dermal secretion. RESULTS: In this pilot cohort (N=8), SL and BL profiles demonstrated strong temporal coupling. Raw data showed a mean Pearson correlation of 0.85±0.13, the correlation increased to 0.94 ± 0.03 after individualized temporal alignment. Analysis of physiological transport revealed a median lag of 65s (IQR: 155s), indicating substantial inter-individual differences in lactate transport and secretion kinetics. CONCLUSION: Preliminary findings support the feasibility of continuous SL monitoring during incremental exercise. While SL closely tracks systemic lactate dynamics following individualized temporal adjustment, pronounced variability in physiological lag suggests that personalized calibration strategies are necessary. Larger cohorts are required to determine whether SL monitoring can reliably inform real-time metabolic threshold detection. [1] A. K. Oktavius et al., "Fully-Conformable Porous Polyethylene Nanofilm Sweat Sensor for Sports Fatigue," IEEE Sensors Journal, 21, 7, 8861-8867, 2021.
Read CV Zahrasadat HosseiniECSS Paris 2023: CP-AP06
INTRODUCTION: Resistance training (RT) is a cornerstone of metabolic health and neuromuscular longevity [Westcott, W. L. (2012)]. However, unlike aerobic conditioning, which benefits from standardized metrics (heart rate, VO2max etc.), RT lacks accessible, objective quantification for load and fatigue. While Velocity-Based Training (VBT) serves as the current non-invasive gold standard for monitoring neuromuscular fatigue [Sánchez-Medina & González-Badillo, 2011], its reliance on specialized linear position transducers limits its utility to elite settings. Conversely, Rating of Perceived Exertion (RPE) remains the most common field method but is frequently compromised by subjective bias and poor intra-individual reliability [Zourdos et al., 2016]. METHODS: This study proposes a novel framework for detecting muscle fatigue using consumer-grade, wrist-worn devices. We integrated tri-axial accelerometer, gyroscope, and barometric altimeter data to capture high-resolution kinematic signatures across common compound lifts (squats, deadlifts, bench press etc) and auxiliary lifts (biceps curl, tricep extension, shoulder press etc). In total, 13 exercises were used that were labelled by the proctors during the data collection. Data were collected from 334 participants who performed a total of 2104 sets of various RPEs, simultaneously monitored via VBT-gold standard equipment (Speed4lifts S.L., Madrid, Spain and Gymaware, Braddon, Australia). Furthermore, human labelers were employed to provide labels on sets, reps and rep phases from video data. We utilized a comparative machine learning approach, evaluating Random Forest, Gradient Boosting, and Neural Network architectures to classify "severely fatigued" (defined as >35% velocity loss) vs “low to mid fatigue” sets. RESULTS: Our analysis found a weak correlation (0.21) between self-reported RPE and objective VBT-defined fatigue, indicating RPE is a poor metric for muscle fatigue. Conversely, by leveraging multi-modal sensor data from consumer wearables, our trained model achieved moderate classification accuracy of 72% for severely fatigued sets. CONCLUSION: These results suggest that integrated kinematic sensors in consumer wearables can provide an objective, "lab-free" proxy for VBT. By providing near-to-real-time feedback on fatigue and consistency, this technology offers a scalable solution to optimize training volume, minimize injury risk, ensure high-quality repetitions and enhance metabolic outcomes in the general population.
Read CV Louise Anderson-ConwayECSS Paris 2023: CP-AP06
INTRODUCTION: As athletes are increasingly exposed to hot conditions in training and competition due to rising global temperatures, reliable monitoring of physiological responses to thermal load becomes essential. Wearables offer practical opportunities to track core temperature (Tc), skin temperature (Ts), heart rate (HR), and related physiological signals. However, despite rapid technological advances, uncertainty remains regarding how accurately these devices quantify heat strain. Therefore, our aim was to synthesize evidence from studies evaluating wearables and computational models for assessing heat strain and heat related risks in various settings, including sport and exercise, or studies with direct applicability to sport due to their focus on thermoregulatory responses during physical activity. METHODS: This systematic review was conducted in line with the PRISMA framework. PubMed, ScienceDirect and Web of Science were searched for relevant studies in November 2025. Studies were eligible if they examined wearables to monitor physiological parameters under conditions associated with heat stress or heat-exacerbating factors. RESULTS: In total, 48 studies met all inclusion criteria and were grouped into four clusters: (1) Wearable validation (n = 21): several sport-applicable devices (e.g., CORE, Zephyr) showed variable agreement with Tc reference measures (bias = -0.47 to 0.51 °C), with accuracy influenced by, inter alia, environmental conditions, sensor placement, and activity intensity. Bias often increased at higher temperatures, indicating reduced reliability during hyperthermia. (2) Algorithmic Tc estimation (n = 9): compared with traditional linear or mechanistic models, Kalman filter and deep learning approaches, particularly those using multimodal inputs and phase specific update procedures, achieved superior accuracy (bias = -0.06 to 0.30 °C). (3) Real time heat strain monitoring (n = 9): wearables reliably tracked key indicators of heat strain. While much evidence stems from occupational settings, the findings are transferable to sport and exercise, where athletes may experience similar cardiovascular and thermal strain. Notably, perceived strain often exceeded physiological measures, reinforcing the value of objective, individualized feedback. (4) Predictive analytics for heat stress risk and heat strain estimation (n = 9): machine learning models that integrate wearable-derived physiological signals, environmental and contextual data improved early detection and prediction of excessive heat strain and heat related risks. CONCLUSION: Our review shows that wearables and predictive modelling show potential for individualized, real time assessment of heat strain and heat related risks. Yet, rigorous validation, physiologically grounded modelling, and broader population testing are essential for improving reliability, generalizability and real world applicability, especially within sport, where the applicability of existing findings and sport specific validation remain limited.
Read CV Ine De BotECSS Paris 2023: CP-AP06