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2026
Conference Paper
Title
Illusion of Precision: How Averaging Undermines Pulse Measurement Accuracy
Abstract
Accurate heart rate measurement is a fundamental requirement in many fields, including medicine, veterinary science, sports performance, and health monitoring. Devices designed to monitor pulse rate are widely used in both human and animal applications, and their reliability is often taken for granted.
However, a curious and counterintuitive effect can occur: some devices consistently report a minor changing value - such as 72 beats per minute for heart beat detection - regardless of the actual physiological state of the subject. At first glance, this appears to be a minor flaw, especially since the reported value may align with the average resting heart rate in humans. From a statistical standpoint, the device could even appear reasonably accurate when evaluated over a large group of individuals. Yet this illusion of accuracy masks a critical flaw: individual deviations are ignored, and potentially important physiological variations go undetected. This paper explores how averaging can give rise to the appearance of precision, while in reality, the measurement fails to reflect real-time or individualized heart rate data. We argue that devices with such behavior introduce systematic bias, which poses significant risks in both clinical and practical applications.
However, a curious and counterintuitive effect can occur: some devices consistently report a minor changing value - such as 72 beats per minute for heart beat detection - regardless of the actual physiological state of the subject. At first glance, this appears to be a minor flaw, especially since the reported value may align with the average resting heart rate in humans. From a statistical standpoint, the device could even appear reasonably accurate when evaluated over a large group of individuals. Yet this illusion of accuracy masks a critical flaw: individual deviations are ignored, and potentially important physiological variations go undetected. This paper explores how averaging can give rise to the appearance of precision, while in reality, the measurement fails to reflect real-time or individualized heart rate data. We argue that devices with such behavior introduce systematic bias, which poses significant risks in both clinical and practical applications.
Author(s)
Keyword(s)
Branche: Healthcare
Research Line: Computer vision (CV)
Research Line: Human computer interaction (HCI)
Research Line: Machine learning (ML)
LTA: Interactive decision-making support and assistance systems
LTA: Monitoring and control of processes and systems
LTA: Machine intelligence, algorithms, and data structures (incl. semantics)
Ambient intelligence (AmI)
Sensor data exploration
Vital signs
Data recognition