Wearable device contest: algorithm is the key

If the current wearable device relies on product design to win, perhaps this idea is time to change, according to Wired magazine, the next frontier of wearable device technology is the algorithm. Wearable devices not only need to monitor various motion data, but also require accurate monitoring data. Now, you talk about the biggest industry trends with anyone with a scientific mind. You must always mention the term "wearable device". Two years ago, wearables was a relatively unfamiliar category of equipment, and today almost all technology companies are already involved in the wearable market.

Although the wearable market is highly competitive, technology companies are still vying to develop wearable products. Today, large technology companies are experimenting with differentiated competition in the physical design of wearable devices. Most wearables look very similar in shape, but in the next stage, the price, materials, colors, and styles of wearables offer more options. Then, wearable device developers will differentiate their software experience. After all, wearable devices without good software are just slick jewels.

So what is the next trend in the wearable industry? Perhaps the most important part of wearables: using algorithms to ensure that data accurately responds to a variety of actions and meets a wide range of users.

The current situation of wearable algorithms

Whether it's the Fuel Point of the Nike FuelBand wristband, the number of steps in the Fitbit Flex bracelet, or other wearable data, the wearable device can track and understand user actions in all directions, and then use proprietary algorithms to determine the action. At present, wearable devices still have many technical problems. One of them is that the algorithm almost always tracks the same data (such as the number of steps), so wearable devices are basically the same.

Specifically, most fitness wearable devices have a common problem, that is, they are essentially "pretty electronic pedometers" that do not recognize all of the user's behaviors around the clock. For example, when a user wants to do weightlifting, they will remove the Nike FuelBand wristband. FuelBand not only hinders exercise, but also cannot monitor exercise data. The user must manually enter the exercise information on the smartphone, and the exercise process is bound to be interrupted. As a result, the user is extremely disappointed with the interactive experience of the device.

The problem with wearable devices is not only the inability to monitor more body movements, but also the inability to accurately monitor motion data. Consumers are eager to get walking data throughout the day. Today is much more than yesterday, which means progress, but as consumers become more familiar with wearables, they will have higher requirements for accuracy. For professional athletes, the accuracy of the training data directly affects their performance.

What advice can a wearable device offer to users in the future? A truly smart wearable device should be able to analyze current and past user motion data and alert users around the clock. For example, when the wearable device recognizes that the user is sitting for too long, it encourages the user to walk up.

How to improve the accuracy of the algorithm?

Now, the algorithm can't really meet the customer's needs, we need to fully exploit the data through the best way. Technology companies such as Apple have hired a large number of data scientists, and the number of such talents will continue to increase.

Not only that, but as wearable product manufacturers narrow their market segments by defining user groups, user research has become a key component of any successful wearable device. But user research is not necessarily the only skill that researchers need, and researchers need to be highly sensitive, with superior analytical and creative skills. After all, user researchers will become "consumer spokespersons" who use the knowledge of wearable devices to spread the user's perceptions across the industry and predict the future success of the wearable device market.

Consumers need more than just beautiful electronic pedometers. They not only need equipment with tracking capabilities, but they also need equipment to guide fitness, sleep and diet. Users are eager to get all the daily activity data and need accurate data. If the algorithm always understands the user's activities and you continue to refine these algorithms, you will gain a long-term market advantage.

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