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
. 2021 May 12;21(10):3381.
doi: 10.3390/s21103381.

Human Activity Recognition for People with Knee Osteoarthritis-A Proof-of-Concept

Jay-Shian Tan  1 , Behrouz Khabbaz Beheshti  2 , Tara Binnie  1 , Paul Davey  1 , J P Caneiro  1 , Peter Kent  1 , Anne Smith  1 , Peter O'Sullivan  1 , Amity Campbell  1
Affiliations

Human Activity Recognition for People with Knee Osteoarthritis-A Proof-of-Concept

Jay-Shian Tan et al. Sensors (Basel). .

Abstract

Clinicians lack objective means for monitoring if their knee osteoarthritis patients are improving outside of the clinic (e.g., at home). Previous human activity recognition (HAR) models using wearable sensor data have only used data from healthy people and such models are typically imprecise for people who have medical conditions affecting movement. HAR models designed for people with knee osteoarthritis have classified rehabilitation exercises but not the clinically relevant activities of transitioning from a chair, negotiating stairs and walking, which are commonly monitored for improvement during therapy for this condition. Therefore, it is unknown if a HAR model trained on data from people who have knee osteoarthritis can be accurate in classifying these three clinically relevant activities. Therefore, we collected inertial measurement unit (IMU) data from 18 participants with knee osteoarthritis and trained convolutional neural network models to identify chair, stairs and walking activities, and phases. The model accuracy was 85% at the first level of classification (activity), 89-97% at the second (direction of movement) and 60-67% at the third level (phase). This study is the first proof-of-concept that an accurate HAR system can be developed using IMU data from people with knee osteoarthritis to classify activities and phases of activities.

Keywords: human activity recognition; inertial measurement units; knee osteoarthritis; machine learning; physical activity monitoring.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Placement of IMUs (purple) used for training the CNN models and Vicon marker (blue) placement for recording start and end times for each trial.
Figure 2
Figure 2
Architecture of the proposed human activity recognition system.
Figure 3
Figure 3
Visual representation of Level 1 activity ‘image’ patterns.
Figure 4
Figure 4
Decision tree for three levels of activity classification—Level 1 Activity, Level 2 Direction, Level 3 Phase.
Figure 5
Figure 5
Illustration of the segmented window sliding in 10 ms increments for each level of classification. Level 1—200 ms; Level 2—100 ms; Level 3—40 ms.
Figure 6
Figure 6
Confusion matrices for classification of activities/phases per classification level. Green cells represent correct classification and arrows represent the classification pathway from activities to phases of activities.
See this image and copyright information in PMC

References

    1. Ackerman I.N., Bohensky M.A., Zomer E., Tacey M., Gorelik A., Brand C.A., de Steiger R. The projected burden of primary total knee and hip replacement for osteoarthritis in Australia to the year 2030. BMC Musculoskelet. Disord. 2019;20:90. doi: 10.1186/s12891-019-2411-9. - DOI - PMC - PubMed
    1. Vos T., Barber R.M., Bell B., Bertozzi-Villa A., Biryukov S., Bolliger I., Charlson F., Davis A., Degenhardt L., Dicker D., et al. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;386:743–800. doi: 10.1016/S0140-6736(15)60692-4. - DOI - PMC - PubMed
    1. Machado G.P.M., Gignac M.A.M., Badley E.M. Participation restrictions among older adults with osteoarthritis: A mediated model of physical symptoms, activity limitations, and depression. Arthritis Care Res. 2008;59:129–135. doi: 10.1002/art.23259. - DOI - PubMed
    1. Wilkie R., Peat G., Thomas E., Croft P. Factors associated with restricted mobility outside the home in community-dwelling adults ages fifty years and older with knee pain: An. example of use of the International Classification of Functioning to investigate participation restriction. Arthritis Care Res. 2007;57:1381–1389. doi: 10.1002/art.23083. - DOI - PubMed
    1. Kokkotis C., Moustakidis S., Papageorgiou E., Giakas G., Tsaopoulos D.E. Machine learning in knee osteoarthritis: A review. Osteoarthr. Cartil. Open. 2020;2:100069. doi: 10.1016/j.ocarto.2020.100069. - DOI - PMC - PubMed

LinkOut - more resources