The issue of racial bias in health monitoring by wearable devices has indeed received some attention. Studies have shown that these devices may show different accuracy among people of different races and skin colors. This bias may be mainly reflected in the following aspects: 1. **Heart rate monitoring**: Studies have found that the photoplethysmography (PPG) technology of some wearable devices may not be as accurate in detecting heart rate on dark skin as on light skin. This is because PPG sensors use light reflection to measure blood flow, and the color and characteristics of the skin may affect the efficiency of light reflection. 2. **Data training bias**: Many machine learning algorithms are developed based on specific data sets. If these data sets lack racial diversity, it may cause the algorithm to perform poorly on certain groups. This means that the algorithms of wearable devices may not fully represent people of all racial backgrounds during the training phase. 3. **Lack of diversity testing**: If the product is not adequately tested in a sufficiently diverse racial sample during the development and testing phase, it may cause the device to perform poorly for certain races in real-world use. To address these issues, developers need to pay more attention to racial diversity in product design, data collection, and algorithm training. In addition, industry standards and clinical validation also require more research and testing on racial and skin color differences to ensure that these devices can provide reliable health monitoring data to all users fairly.