Design of an Electrocardiogram System for Physical Fitness Analysis using Heart Rate Variability and Maximal Oxygen Volume - Jana Utama; Yeffry Handoko; Candra Saefulloh (Telkom University, 2025)
This study proposes a novel, low-cost, and portable real-time ECG monitoring system integrating the AD8232 sensor and Arduino Nano for comprehensive heart rate variability (HRV) assessment. Unlike conventional ECG devices, the proposed system combines affordable hardware with an embedded processing algorithm capable of directly calculating key time-domain HRV parameters—RMSSD, SDNN, NN50, pNN50, and VO₂Max—without the need for high-end laboratory equipment. The primary objective is to develop and validate a system that can accurately capture physiological responses during both resting and active conditions, enabling fitness assessment and potential early cardiovascular risk detection in field applications. The system was tested on five healthy subjects aged 20–22 years, demonstrating stable operation for over 40 minutes. Results showed that the average heart rate (HR) increased by 6.56% ± 4.8% from rest to activity, with the largest increase in Subject 1 (+12.09%) and the smallest in Subject 3 (+0.93%). Resting RMSSD values ranged from 75.36 to 99.76 ms, decreasing on average by 6.21% ± 4.7% during activity. SDNN values ranged from 62.18–131.88 ms, with Subject 2 showing an increase (122.33→129.06 ms) and Subject 5 a significant decrease (131.88→88.48 ms). NN50 at rest reached a maximum of 124 counts (pNN50=77.78%) and a minimum of 23 counts (pNN50=27%), with an average pNN50 reduction of −18.42% ± 21.5% during activity; the largest drop occurred in Subject 5 (−54.64%), while Subject 1 increased (+26.07%). VO₂Max ranged from 29.98 ml/kg/min (poor) to 39.90 ml/kg/min (excellent), correlating with HRV trends. These findings confirm that the system can quantitatively differentiate autonomic responses between individuals, highlighting its potential for real-time, on-site physiological monitoring, fitness evaluation, and early detection of abnormal cardiovascular patterns.