The issue of racial bias in health monitoring by wearable devices does exist, and it has attracted the attention of academia and industry. This problem mainly stems from many factors such as technical design, data bias, and algorithm limitations. The following is a detailed analysis： --- ### **1. Underrepresented data** -**Racial imbalance of training data**: The development of most health monitoring algorithms relies on biological data (such as heart rate, blood oxygen, skin electrical response, etc.) of specific populations (usually whites or other majority ethnic groups). If there is a lack of diversity in the data set, the adaptability of the algorithm to other skin tones or races may decrease. - **Typical case **: A 2020 study in the journal Nature pointed out that some blood oxygen monitoring devices based on photoelectric volumetric maps (PPG) have a higher error rate in dark-skinned people because the difference in light absorption rate of the sensor to dark skin is not fully calibrated. Calibrated. - **Physiological differences are not included in the model**: Different races may have differences in physiological indicators (such as blood pressure baseline and heart rate variability), but if the algorithm is not adjusted for these differences, it may lead to misjudgment. For example, African-Americans have a higher incidence of hypertension, but device calibration may not consider this group characteristic. --- ### **2. Limitations of technical design** -**Skin color deviation of optical sensors**: Photoelectric sensors (such as heart rate and blood oxygen monitoring) rely on the reflected signal of light after it penetrates the skin. Melanin in dark skin absorbs more light, resulting in a decrease in signal quality. Early devices may not be optimized for this physical characteristic. -**Impact of wearing location**: Differences in hair density or skin texture of certain ethnic groups may affect the contact between the device and the skin, further reducing the accuracy of the data. --- ### **3. Algorithm and verification defects** -**Algorithmic Bias**: If a machine learning model does not cover diverse racial data during the training phase, its output may be implicitly biased. For example, the US FDA's review guidelines for AI medical devices (2023) specifically emphasize the need to verify the effectiveness of algorithms in different races. -**Lack of clinical verification**: The participants in the pre-market clinical trials of many devices are mainly white, leading to doubts about the applicability of other races. A 2021 Harvard University study found that 76% of cardiovascular health research data came from European and American whites. --- ### **4. Industry improvement measures** -**Data diversification**: Companies such as Apple and Fitbit have begun to publicly recruit diverse subjects to improve their algorithms. For example, the Apple Heart Study has added data collection for African-American and Latino populations. -**Hardware iteration**: Some manufacturers use multi-wavelength sensors or adaptive algorithms to reduce the impact of skin color. -**Policies and standards**: The U.S. FDA requires manufacturers to submit racial disparity analysis reports, and the European Union's "AI Act" also includes the fairness of health equipment in the scope of regulation. --- ### **5. User's response suggestions** -**Optional use**: Dark-skinned users can choose to clearly mark devices that support multi-skin tone calibration (such as the latest Apple Watch or Fitbit Charge 6). -**Cross-verification**: It is recommended to compare key indicators (such as blood oxygen) with traditional medical equipment, especially when abnormal values occur. -**Participate in feedback**: Report abnormal data through manufacturer channels to promote technical improvement. --- ###**Summary** The racial bias of wearable devices is a real challenge to the universality of technology, but through data diversity, hardware innovation, and regulatory intervention, the industry is gradually improving this problem. Consumers can pay attention to the manufacturer's inclusiveness statement and third-party reviews when purchasing to make more informed choices.