Biometrics and Extended Reality

Whether you have delivered training or experienced training (or any form of education for that matter!) you will be familiar with “performance metrics”.   

These performance metrics usually include one, or all of the following:

• Training ROI

• Cost per employee

• Performance post-training

• Changed operational efficiency

• Training experience satisfaction

• Course completion data

• Learner drop out rate

• Learner engagement

• Pass rates and scores

• Completion rate

Notice that these are all objective measures?

• What was your pass rate?

• What was your training ROI?

But, performance metrics don’t tell us about the personal experience of the student through their learning journey. 

Subjective measures can be difficult to assess without making direct connections with the learner. 

Biometric metrics such as tracking and analyzing physical movements, monitoring physiological responses, or measuring cognitive or emotional states can provide indications on the quality and quantity of the ‘learning’ in the learning experience. 

Biometrics plays a role in XR training in many ways. It can provide indications on the quality and quantity of the ‘learning’ the student is experiencing through their training.  

Some examples of biometrics that are used to improve XR training outcomes are:

1. Eye-tracking

• used to monitor where the learner is looking

• provides insight into learner’s attention and focus during training

• information can be used to optimize the training programme/provide feedback to the learner

2. Heart rate variability (HRV)

• measures time variation between heartbeats

• indicates stress or cognitive workload

• Monitoring HRV can help trainers to adjust the training program to optimize learning outcomes

3. Electroencephalography (EEG)

• measures the electrical activity of the brain

• insight into cognitive states (attention, engagement, and mental workload)

• used to optimize XR training by providing real-time feedback to the learner and adjusting the training program to improve learning outcomes

4. Motion capture

• used to track and analyze physical movements during training (posture, movements, and biomechanics,)

• used to optimize training techniques and prevent injuries

5. Facial expression analysis

• used to monitor emotional states such as stress, anxiety, or engagement

• data can be used to adjust training to optimize learning outcomes and minimize stress

This was just a snapshot at biometrics and how it can help improve XR training outcomes for learners.

The choice of which biometric technology or combination of technologies will depend on the specific goals of the training program and needs of the learners.

Contact us to learn more about which biometrics will work best with your XR experience to ensure you have engaging, memorable and effective training programmes for your business.

Trent Yates

Trent is a software and systems engineer with a passion for Virtual Reality and Augmented Reality. Following a career in the Australian Navy (electronic engineering and operations), Trent pursued his passion for immersive technology. He started a successful business, developing numerous Extended Reality (XR) experiences for enterprise and government. Since joining XKG he has been building CapitalXR, the "software as a service" and immersive learning experience division of XKG.