Evaluating the Reliability of SpO₂ and BPM Readings in Commercial Smartwatches Compared to a Standard Oximeter

Accuracy SpO2 bpm oximeter smartwatch

Authors

  • Kusnanto Mukti Wibowo
    kusnantomukti@gmail.com
    Department of Medical Electronics Engineering Technology, Universitas Muhammadiyah Purwokerto, Purwokerto, Indonesia, Indonesia
  • Royan Royan Department of Medical Electronics Engineering Technology, Universitas Muhammadiyah Purwokerto, Purwokerto, Indonesia, Indonesia
  • Abdul Latif Department of Medical Electronics Engineering Technology, Universitas Muhammadiyah Purwokerto, Purwokerto, Indonesia, Indonesia
  • Fani Susanto Department of Radiologic Imaging Technology, Universitas Muhammadiyah Purwokerto, Purwokerto, Indonesia, Indonesia
  • Fatiatun Fatiatun Department of Physics Education, Universitas Sains Al-Qur’an, Wonosobo, Indonesia, Indonesia
  • Rudi Irmawanto Department of Electrical Engineering, Universitas Muhammadiyah Surabaya, Surabaya, Indonesia, Indonesia
  • Norhidayah Che Ani Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), 86400 Parit Raja, Batu Pahat, Johor, Malaysia, Malaysia

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The advancement of wearable technology has enabled commercial smartwatches to monitor vital health parameters such as blood oxygen saturation (SpO₂) and heart rate (BPM). This study aimed to evaluate the accuracy of SpO₂ and BPM readings from three commercial smartwatches: Realme C2 Pro, Oraimo 2 Plus OSW-32N, and Haylou LS02 Pro by comparing them to a standard medical-grade oximeter (Beurer PO40). A total of 34 participants were recruited, representing a range of skin tones identified using the Fitzpatrick Skin Type Scale (Types I–V). Statistical analyses, including Pearson’s correlation and Bland-Altman plots, were used to assess the relationship and agreement between devices. Results showed that the Realme C2 Pro provided the highest accuracy, with 99.58% for SpO₂ and 98.515% for BPM, while the Haylou LS02 Pro showed the lowest accuracy at 99.24% for SpO₂ and 97.29% for BPM. Bland-Altman analysis revealed small biases and narrow limits of agreement, indicating that the smartwatches produced readings closely aligned with those of the medical device. Despite minor discrepancies, all smartwatches demonstrated strong potential for health monitoring applications. The discussion highlights factors influencing measurement accuracy, including sensor quality, algorithm performance, and user-specific variables such as skin tone. These findings support the role of smartwatches as accessible tools for early health detection and continuous monitoring. Although not intended to replace clinical instruments, properly optimized smartwatches can complement healthcare systems by enabling timely interventions and enhancing disease management.