New algorithm improves fitness tracker accuracy for people with obesity

Key Takeaways: 

• Researchers at Northwestern University have developed a validated, open-source algorithm that significantly improves the accuracy of fitness trackers for people with obesity.
• The new model achieves over 95% accuracy in estimating energy expenditure, addressing long-standing issues in existing algorithms that fail to reflect the lived experience of individuals with higher body weight.
• The technology is informed by both clinical testing and real-world scenarios, aiming to redefine how effort and activity are recognised across diverse body types.
  

A Breakthrough in Inclusive Fitness Technology

Fitness trackers have become an essential tool for many in monitoring daily physical activity and energy expenditure. However, for people living with obesity – whose energy usage, walking gait, and movement dynamics often differ from those without obesity – standard tracking algorithms have repeatedly fallen short.

Now, scientists at Northwestern University have introduced a new algorithm designed specifically to bridge this gap. The model enables wearable fitness devices, particularly wrist-worn smartwatches, to more accurately estimate the number of kilocalories burned during various forms of physical activity among people with obesity.

“People with obesity could gain major health insights from activity trackers, but most current devices miss the mark,” said Dr Nabil Alshurafa, Associate Professor of Behavioural Medicine at Northwestern University Feinberg School of Medicine. His laboratory – the HABits Lab – developed and validated the algorithm, which is open-source, rigorously testable, and ready for others to build upon. A companion activity-monitoring app, compatible with iOS and Android, is due for release later this year.

Why Current Trackers Fall Short

Most existing activity-monitoring algorithms are optimised for individuals without obesity, resulting in inaccurate data when used by people with higher body mass. Hip-worn trackers in particular are prone to errors due to variations in walking patterns and tilt angles, often underestimating energy expenditure.

While wrist-worn models offer better comfort and potential for consistent wear across body types, Alshurafa notes a key flaw: “Without a validated algorithm for wrist devices, we’re still in the dark about exactly how much activity and energy people with obesity really get each day – slowing our ability to tailor interventions and improve health outcomes.”

To evaluate performance, the Northwestern team compared their new model against 11 leading algorithms used in research-grade devices. Their method was not only more inclusive but also demonstrated superior accuracy. They even employed wearable cameras to validate each instance where wrist sensors failed to accurately capture calorie burn.

The team’s findings will be published on 19 June in Nature Scientific Reports.

A Personal Motivation: Fitness Should Not Exclude

Dr Alshurafa’s motivation for the project came from a personal experience while attending a fitness class alongside his mother-in-law, who lives with obesity.

“She worked harder than anyone else, yet when we glanced at the leaderboard, her numbers barely registered,” he recalled. “That moment hit me: fitness shouldn’t feel like a trap for the people who need it most.”

This disconnect between perceived effort and measured output led Alshurafa to rethink how fitness technology serves people across a range of body types.

Matching Gold-Standard Methods

The newly developed algorithm is trained using data from commercial fitness trackers and achieves accuracy comparable to gold-standard methods for measuring energy burn. It estimates how much energy a person with obesity expends per minute, even during complex or low-movement tasks, and consistently surpasses 95% accuracy in real-world settings.

“This advancement makes it easier for more people with obesity to track their daily activities and energy use,” said Alshurafa, underscoring its potential to support healthier outcomes through more reliable data.

Rigorous Testing in Lab and Life

To ensure accuracy, the research included two distinct study groups:

• Controlled Lab Testing:
  A group of 27 participants wore both a wrist-worn fitness tracker and a metabolic cart – a face mask device that measures oxygen intake and carbon dioxide output – to calculate kilocalorie burn and resting metabolic rate. Each participant performed a range of physical activities while data were collected and compared.
• Free-Living Evaluation:
  Another group of 25 participants wore both a fitness tracker and a body camera while going about their daily lives. The camera provided visual confirmation of physical activity, helping the team identify instances where the algorithm over- or under-estimated energy expenditure.

The researchers also explored less conventional forms of exercise. “Many couldn’t drop to the floor, but each one crushed wall-pushups, their arms shaking with effort,” said Alshurafa. “We celebrate ‘standard’ workouts as the ultimate test, but those standards leave out so many people. These experiences showed me we must rethink how gyms, trackers and exercise programmes measure success – so no one’s hard work goes unseen.”

Study Details and Contributors

The study, titled “Developing and comparing a new BMI inclusive energy burn algorithm on wrist-worn wearables,” represents a collaborative effort within and beyond Northwestern University.

In addition to lead author Boyang Wei, the research team included Christopher Romano and Bonnie Nolan. External collaborators were Mahdi Pedram and Whitney A. Morelli, both formerly affiliated with Northwestern.