Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 May;7(2):666-677.
doi: 10.1109/tmrb.2025.3560331. Epub 2025 Apr 14.

Machine Learning Enables Rapid Detection of Slips Using a Robotic Hip Exoskeleton

Reese R Peterson  1 , Jennifer K Leestma  2 , Inseung Kang  3 , Aaron J Young  4
Affiliations

Machine Learning Enables Rapid Detection of Slips Using a Robotic Hip Exoskeleton

Reese R Peterson et al. IEEE Trans Med Robot Bionics. 2025 May.

Abstract

Fall incidents due to slips are some of the most common causes of injuries for industry workers and older adults, motivating research to assist balance recovery following slips. To assist balance recovery during a slip, a detection algorithm that can work with an assistive device, such as an exoskeleton, needs to be able to detect slips rapidly after onset, which remains a critical gap in the field. Here, we compared the ability of linear discriminant analysis (LDA), extreme gradient boosting (XGBoost), and convolutional neural networks (CNN) to detect slip using only native sensors on a hip exoskeleton. We trained and evaluated user-independent models on early-stance (ES) and late-stance (LS) slips of various magnitudes collected through treadmill-based slips. All models, except LDA with LS slips, detected slips with >90% accuracy. Overall, he best model was XGBoost, with its fastest results achieving average detection times and median accuracies of 155.06 ms at 96.25% for ES slips and 228.88 ms at 93.75% for LS slips, while also achieving 100% sensitivity at 195.64 ms (ES) and 266.24 ms (LS). Our results indicate a promising direction for further research into designing a generalizable model for balance recovery during slip perturbations using robotic hip exoskeletons.

Keywords: Fall Prevention; Locomotion; Machine Learning; Robotic Hip Exoskeleton; Slip Detection.

PubMed Disclaimer

References

    1. Kakara R, Bergen G, Burns E, and Stevens M, “Nonfatal and Fatal Falls Among Adults Aged 65 Years — United States, 2020–2021,” MMWR Morb Mortal Wkly Rep, pp. 938–943, 2023. - PubMed
    1. Berg WP, Alessio HM, Mills EM, and Tong C, “Circumstances and consequences of falls in independent community-dwelling older adults,” Age and Ageing, vol. 26, pp. 261–268, July 1997. - PubMed
    1. B. of Labor Statistics, “Number of nonfatal occupational injuries and illnesses involving days away from work by industry and selected events or exposures leading to injury or illness, private industry, 2019,” 2019.
    1. Courtney TK, Sorock GS, Manning DP, Collins JW, and Holbein-Jenny MA, “Occupational slip, trip, and fall-related injuries–can the contribution of slipperiness be isolated?,” Ergonomics, vol. 44, pp. 1118–1137, Oct. 2001. Place: England. - PubMed
    1. Gard G, “Prevention of Slip and Fall Accidents: Risk Factors, Methods and Suggestions for Prevention,” Physical Therapy Reviews, vol. 5, pp. 175–182, Sept. 2000. Publisher: Taylor & Francis.

LinkOut - more resources