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Wearable AI: What Does the Implementation Mean for the Digital World

By Prateek Saxena
April 20, 2020 8. min read
Last update on: July 15, 2020

Wearables have been a part of our life and an important digital transformation for many years now. In the 1980s when the first digital hearing aids were first released, we couldn’t have imagined it to today become an inherent part of our lives. 

Back from 2004, which was dubbed as the year of wearable technology when there was a sudden rise in activity trackers once Apple Watch was introduced, the innovations that have been happening in the Wearable sector are no less than complete transformations. 

But some time between today when you are reading this article and a few years ago, there was a time when wearable devices started losing their charm – mostly because of the fact that people got bored with tracking their counts or getting phone call notifications on their smart watch. This was the time when the talks of making wearables smarter started doing rounds. 

Cut to today, the preparation of making wearable technology intelligent so that the value the users get at the back of every interaction is comprehensive is on a high. This preparation has been given the name of Wearable AI. As you can infer, the sector is what has emerged out of the incorporation of Artificial Intelligence and Wearable tech in the world. 

Table Of Content

Wearable Industry Shortcomings That Artificial Intelligence Solves

Statistics Around the Wearable Artificial Intelligence Market

Use Cases of AI in Wearable Technology

Challenges Associated With the Incorporation of AI in Wearable Technology

Wearable Industry Shortcomings That Artificial Intelligence Solves

The problem in wearable technology that would need an AI technologies solution is two-fold. 

On one side, there is a problem that users face in terms of lack of an action item. Imagine this, suppose your smart watch notifies you that you have walked 300 steps in a day. Now what would you like to do with the information? Don’t you want to know what the ideal step counts? Or need a comparison with the previous day’s step count? Or simply need information around how steps lead to your fitness goal, how many more steps would you need to meet your goal?

Now on the other side, there is a ‘what is the next step problem’ with the wearable service providers. When built simply on the idea of fetching information from the body and notifying the vitals to the users, there comes a time when users start asking the value proposition (although, the instance almost never comes in case of medical wearable apps which are devised to track vitals). What is difficult for service providers is to think about the expansion model – another one of the wearable challenges that AI solves.

AI technologies, at the back of their powerful armour, Machine Learning, does an amazing job at converting the plethora of data that the users give you into actionable intelligence. It helps your users and your business with the next steps. 

In a little while, we will share examples of brands using this incorporation in the real world. But before we do, let us look into some statistics throwing light at the rise of AI and Wearables convergence. 

Statistics Around the Wearable Artificial Intelligence Market

Wearable AI market

The wearable intelligence market is poised to reach USD180 Billion by 2025. There are AI and Wearables technology trends driving the growth of AI and Wearables:

  • Continuous improvements in the design, function, and variety of available wearable devices, in addition to constant innovations introduced by a slew of sound wearable app development company.
  • Emergence of edge computing on powerful processors like the Qualcomm® Snapdragon™ Wear 3100 Platform 
  • Enhanced AI algorithms and advancements in the wireless connectivity such as 5G, are increasing the functionality for providing a seamless user experience.
  • Smart wearable device market will be able to sustain a double-digit growth, with 780 Million units poised to sell between 2018-2022.

Use Cases of AI in Wearable Technology 

The use cases of artificial intelligence in wearable device is presently divided into two main categories: Healthcare & Safety, Enhancing AR/VR, and Intelligent Assistants. 

Use Cases of AI in Wearable

Enhancing Augmented and Virtual Reality 

The role of VR and Wearable in the Healthcare domain is already extremely prevalent in the market. The need, however, is to make them intelligent. 

The first and most uprising use cases of wearable AI is seen in their incorporation in the AR/VR segment. The mixed reality devices which use a blend of augmented and virtual reality can be enhanced greatly through AI technologies incorporation in the wearable industry. The present mixed reality headsets have to be connected to either a powerful PC or smartphones to work. But their performance however is dependent on the processor’s power. 

AI can lower down the wearable workload by adjusting the performance of the headset to what the users require at the moment. Through an interaction with the user and their environment, the intelligent machines are able to understand their preferences, the information that has to be displayed, and lower the latency which is experienced with a mixed reality. 

Example: In HoloLens 2, Microsoft has announced to incorporate a dedicated AI core processor for providing users with an expansive experience. The time to come will see more AI wearable devices emerging in the gaming industry. 

Intelligent Wearable Assistant 

With wearable intelligence being on the rise, Artificial Technology is being used for empowering the devices into becoming true assistants and thus improving the customer experience. The use case of AI Analytics in Wearable is fairly evident in the sports world where the gadgets and advanced sensors are embedded in the smart apparel and provide the users with real-time notification of their metrics in addition to actionable advice for improving performance and insights for lowering the injuries risk. 

Other than the sports industry, it is also seen in case of event and travel use cases, where the users are notified of their flight timings or movie schedule. 

Example: When we talk about the companies using wearable technology and AI combination, both Google and Apple offer the functionality of syncing their assistant services: Google Assistant and Siri with their Watch, EarPods and other wearables. 

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Health & Safety

The use cases of AI and wearable technology in healthcare is most sought after. On an individual level, both the technologies – AI in Healthcare and Wearable in Healthcare – are doing a lot of efforts to make the sector disruptive. There are multiple ways in which the combination of AI and Wearable are helping the healthcare industry a lot more responsive and preventative. 

  • The technology, through natural language processing and computer vision, learns the visual cues which the sighted people recognize in cities like paths, buildings, sidewalks, curbs, etc. It then uses the information by converting it into natural voice cues that help visually impaired wearers move around the city comfortably. 
  • Machine Learning through real-time monitoring can help learn all the seizures patterns. These wearable devices with AI capabilities can be worn by someone having epilepsy and can then alert them if a pattern is recognized, so that they get time to pull off the road or reac a safe spot in time. 
  • With the help of machine learning, meaningful data can be created by monitoring the physiological markers of stressors and emotional arousal in children with autism spectrum disorder. This, in turn, can help caregivers identify potential precursors, thus making the health and care domain a lot more preventive through the introduction of Wearables and AI in healthcare

Challenges Associated With the Incorporation of AI in Wearable Technology

Wearable AI development challanges

Battery life: When we were developing a wearable fitness tracking application, one of the key challenge areas for us in the entire AI project management cycle was managing the battery life. Since the computational power of wearables are a lot less compared to smartphones, the batteries tend to run out much sooner than ideal. Two approaches that we followed to counter this in our wearable technology development process was A. we processed only the data that we needed through edge computing and B. we optimized our code in a way that would support minimal usage. 

Privacy: A successful implementation of Artificial Intelligence Wearables calls for a lot of data. In this scenario, it is very important to keep privacy as the top priority. All throughout the development process we had to keep ourselves asking if at all we needed to collect certain information. The next important thing was to compute the derived data on the fly. All these measures tend to increase the wearable app development cost to a great extent, something that can be worrisome for the startup entrepreneurs.  

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Security: There are some security sensitive AIs like the ones that make use of wearable cameras for facial recognition, for such devices, it has been our priority to make use of on-device data processing in our wearable app development processes. The usage of edge computing saves the need to transmit data through the public or private cloud services. This avoidance offers a high level of privacy. But in instances where transmission of the data is necessary, we follow ways to encrypt the data to their entirety.

Prateek Saxena
Prateek Saxena
Co-founder of Appinventiv
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