There are a lot of AI wearables coming out these days. What kind of hardware is needed for these devices?

From a hardware perspective, sensors, low-power chips, and battery management are key. For example, biometric sensors like heart rate, blood oxygen, and body temperature; accelerometers and gyroscopes for motion detection; and UV and air quality sensors for environmental monitoring. Additionally, low-power AI chips like Qualcomm Snapdragon Wear or Google Edge TPU can run AI calculations on the device itself.

Oh, so the battery life also needs to be long, right?

That’s right. Real-time analysis requires a long battery life, so an energy-efficient design is essential. Some products even include features like solar charging or kinetic charging based on user movement to extend battery life.

So, does just having these sensors and chips automatically enable AI functionality?

No, software and algorithms are also important. Real-time processing of data received from sensors requires edge AI, machine learning models, and BLE/Wi-Fi/5G connectivity. Furthermore, data security must be ensured to ensure the safety of personal information.

What is edge AI? Is it different from cloud AI?

Edge AI refers to real-time analysis performed on the wearable itself. Because data doesn’t need to be sent to the cloud, it responds quickly and can function even without an internet connection. For example, it can perform posture analysis or detect heart rate abnormalities during exercise.

So, does the model for edge AI require separate training?

That’s right. Machine learning models are trained with various data to implement functions like activity recognition, anomaly detection, and health prediction. Of course, data must be managed in a secure environment.

Ah, I understand. So, what AI technology is applied?

AI technologies commonly used in wearables these days include computer vision (smart glasses recognize objects and gestures), NLP (voice commands and translation), and predictive analytics (predicting falls, heart problems, and blood sugar fluctuations). So, rather than relying on simple sensor data, they can make intelligent decisions in real time.

So, even products that look like smart glasses incorporate AI?

Almost all of them do. Computer vision allows for the recognition of surrounding objects, and NLP allows for voice commands. For example, they can perform tasks like finding directions, checking messages, and controlling gestures.

Wow, so how are health-related wearables used differently?

Predictive analytics is key in the health sector. AI analyzes heart rate, blood sugar, and body temperature data in real time, providing advance warning of events like heart problems, falls, and blood sugar spikes. This allows users to take proactive measures.

Ultimately, hardware, software, and AI technologies must all work together to function properly, right?

Exactly. Hardware like sensors, chips, and batteries; software like edge AI, machine learning, and connectivity; and AI technologies like computer vision, NLP, and predictive analytics must work together seamlessly to create a truly smart wearable.