The wearable industry has surged in recent years thanks to a combination of enhanced tech capabilities and evolving consumer needs.  

As developers and enterprises struggle to gain the competitive edge over other rivals, many are turning to the capabilities of Artificial Intelligence (AI) and Machine Learning (ML) to offer smarter, more insightful functionality.  

Within the health and fitness wearable sector, this focus on integrated AI and ML tools has significant potential – with use cases such as proactively identifying and mitigating injuries and automatically designing individual training plan.

AI and the data reliability challenge 

While AI tools are being explored within this demanding sector, significant challenges prevent full-scale adoption.  

Alongside the complexity of introducing and training AI models, these capabilities also demand clean, reliable, and well-governed data – an issue that may prove difficult to overcome in the wearable sector, due to the data quality challenges associated with these devices having to cope with a large amount of human and environmental variability.  

However, while inconsistent data reliability can cause a significant number of challenges for Artificial Intelligence and Machine Learning tools, the sheer volume of data available from wearable devices and the number of users adopting the technology also provides significant opportunities for training and development of more advanced AI/ML techniques.  

Through analysing data from many consenting users over a period, AI and ML techniques can be trained to become more accurate than ever before – able to categorise different user types and trends to provide a specific service to the individual based on analysis of the collective.  

How can Artificial Intelligence and Machine Learning revolutionise health and fitness wearables? 

AI and ML-based tools can transform how athletes approach many aspects of their training offering benefits to personal performance. On the surface, AI tools allow for a predictive and proactive approach, harnessing models to predict the outcomes of multiple events before suggesting an optimised route.  

Within the health and fitness sector, these tools can provide valuable insights on data collected from wearable tech and user-entered data. Significant examples are:  

• Creating more advanced and intelligent training schedules 
• Preventing injury before it occurs 
• Proactive diagnosis of threatening medical conditions and emergencies 
• Physical indicators of mental health complications 

Creating advanced training plans with AI 

There’s significant potential, already beginning to be realised, to integrate AI within the creation of training schedules.  

The result: AI tools eradicating one of the most consistently identified issues with digital training plans – which is that they’re often simply templates or pre-built modules, rather than an adaptable schedule that adapts to user progress and preferences.  

These standardised templates, while convenient and accessible provide little or no variation for the individual athlete. This can lead to progress being stunted and athletes not reaching their performance goals.  

An AI-based training schedule empowers athletes to get the best results from their training plans. It enables the generation of responsive training plans that adapt based on collected wearable data and user-provided feedback, such as difficulty level, heart rate, pace, user availability and forced downtime – ensuring that runners get back to training in an optimal way.  

Proactive identification and mitigation of injury 

AI-enabled tools can combine with wearable collected data to prevent injuries that may otherwise prevent athletes from training due to the need for rest. Shin splints, knee injuries, and lower back pain – these injuries and more can seriously prevent athletes from training according to their schedule.  

Zone 7 AI is an excellent example of an organisation that has utilised the benefits of AI/ML technology to mitigate the risk of injury to elite professional athletes. 

With a combination of advanced health monitoring from consumer-focused or medical wearables and user-provided feedback, AI models can interpret and detect symptoms proactively. Alerting users to the need to rest, and adapting training plans accordingly, these tools can prevent serious injury from occurring. 

What’s more, this data can be shared with sports physiotherapists for a more universal and centralised approach to the health and progress of athletes.   

Proactive diagnosis of threatening medical conditions 

By introducing advanced health monitoring capabilities to everyday wearable tech, users may be alerted to potential health concerns before they occur such as the onset of a cold or flu, or worse. They can even be used to detect physical indicators of mental health, as demonstrated by progressive organisations such as Koa Health. With the addition of AI, these devices can suggest solutions or even alert emergency services.  

These capabilities are more important than ever when medical conditions are life-threatening and potentially fatal. By measuring heart rate variation and monitoring other vitals, AI-enabled medical wearables have the potential to proactively alert emergency services for a quicker response time, and a greater chance of survival. 

The future of AI within the health and fitness wearable sector 

As we turn to the future of AI within the health and fitness wearable sector, we’re eager to witness greater prescriptive analytics capabilities combined with greater accuracy and more intuitive decision-making support.  

As a result, we imagine a future in which athletes will not just be able to understand and be alerted to adaptations in their training plan, but understand how and why the AI models arrived and suggested this change. Within the medical field, AI-enabled wearables can monitor symptoms of illnesses for greater insights than ever, and proactively alert emergency responders for greater chances of survival in serious cases. 

For more information about the possibilities of AI and Machine Learning, and the potential use cases across a wide range of industries, download our datasheet here.