Machine learning model detects indoor or outdoor walks based only on movement data

The broad availability of wearable sensors permits for wealthy information assortment on an individual’s strolling patterns, offering precious well being insights. However elements like location, terrain or the constructed atmosphere alter strolling habits, making information troublesome to interpret with out context.

A University of Michigan-led analysis workforce developed and validated a classification mannequin utilizing a single accelerometer worn on the thigh to precisely distinguish indoor and outside strolling and analyze how every context affected strolling habits.

They discovered that contributors walked considerably sooner, longer and extra constantly when strolling outdoor in comparison with indoors.

“This improves on previous models that relied on multiple sensors and GPS data to contextualize walking data, which reduced practicality and raised security concerns,” mentioned Alex Shorter, co-author of the examine printed in Scientific Reports, and an affiliate professor of mechanical engineering.

“This method has the potential to be used with more common sensors like smartwatches. We hope to form a foundation for future investigation of the nuances of human walking behavior in different environments,” mentioned Loubna Baroudi, first writer of the paper and a doctoral graduate of mechanical engineering.

Added Shorter, “This technology could be used in a health care setting to track the amount and type of walking a patient does post-surgery or during physical rehabilitation to monitor mobility progress.”

The researchers first collected information over seven days from 20 contributors sporting an accelerometer taped to the thigh to permit unobtrusive placement whereas amassing movement information. Contributors self-reported the aim of the stroll and GPS coordinates, which the researchers used to label the accelerometer information as indoor or outside strolling bouts.

This labeled dataset was then used to coach and validate two machine studying fashions, random forest and ensemble Help Vector Machine (SVM), utilizing a leave-one-participant-out validation scheme on 15 of the 20 contributors. The skilled fashions had been examined on the 5 remaining contributors’ information to pick the very best performing mannequin.

“Validating our algorithm based on walking data collected during daily routines rather than in a laboratory setting greatly enhances its use for broad, real-world applications,” mentioned Baroudi.

The chosen mannequin was then used to label indoor and outside walks and analyze the kinematics of every setting from a second dataset collected from 15 contributors over 14 days.

Out of doors strolling intervals had been considerably longer, concerned much less standing time, and had the next stride velocity. If utilized to exercise trackers, aiming for the next proportion of outside in comparison with indoor strolling might encourage extra vigorous strolling bouts.

Extra co-authors embrace Kira Barton of the University of Michigan and Stephen M. Cain of West Virginia University.