ArticleSensors (Basel, Switzerland)2021
Human Activity Recognition for People with Knee Osteoarthritis-A Proof-of-Concept.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 20 citations in OpenAlex.
- Minimal Sensor Configuration for Human Activity Recognition in Patients With Hip Osteoarthritis: Proof-of-Concept Study.JMIR biomedical engineering · 2026Article
- Distinct Roles of Muscle Strength and Postural Stability in Objective and Subjective Function in Women with Bilateral Knee Osteoarthritis.Healthcare (Basel, Switzerland) · 2026Article
- Gait Parameters of Women Before Knee Joint Arthritis-Analysis Using the MoKA System.Sensors (Basel, Switzerland) · 2025Article
- A Novel Framework Based on Deep Learning Architecture for Continuous Human Activity Recognition with Inertial Sensors.Sensors (Basel, Switzerland) · 2024Article
- Interpretable Passive Multi-Modal Sensor Fusion for Human Identification and Activity Recognition.Sensors (Basel, Switzerland) · 2022Article
- IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review.Healthcare (Basel, Switzerland) · 2022Review
- Predicting Knee Joint Kinematics from Wearable Sensor Data in People with Knee Osteoarthritis and Clinical Considerations for Future Machine Learning Models.Sensors (Basel, Switzerland) · 2022Article
- Effects of sliding window variation in the performance of acceleration-based human activity recognition using deep learning models.PeerJ. Computer science · 2022Article
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
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.
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Registered trials
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