Performance and activity classification post total knee arthroplasty
Research type
Research Study
Full title
Functional performance and classification of activities of daily living post total knee arthroplasty.
IRAS ID
314702
Contact name
Philip Riches
Contact email
Sponsor organisation
University of Strathclyde
Clinicaltrials.gov Identifier
NA, NA
Duration of Study in the UK
2 years, 0 months, 1 days
Research summary
Summary of Research
Total knee arthroplasty is becoming more prevalent with more than 100,000 procedures taking place in the United Kingdom each year. Post-operative knee stiffness and reduced knee movement are the most common difficulties and factors associated with patient dissatisfaction following surgery.Greater knee movement postoperatively indicates a better long-term knee mobility recovery. Therefore it is vital that patients receive adequate rehabilitative care and those who experience reduced knee range of motion are detected as soon as possible and assisted promptly.
Postoperative rehabilitation is predominately now home-based. However, home-based rehabilitation has been associated with poor compliance. There is therefore an increased demand for guidance and surveillance of patients on rehabilitation programs once in their home environment.
Wearable technologies present a solution to remotely monitor patients and enabling assessments of patient progress to be reviewed and performed at home.
EnMovi Ltd (a Scottish subsidiary of Stryker) have developed a MotionSense app to remotely support post-operative knee replacement rehabilitation. This provides personalised rehabilitation, tracking of home exercises and daily activity, and enables healthcare professionals to continuously monitor rehabilitative progress remotely.
The objective of this study is the collection of data using the EnMovi Ltd wearable sensors to enable the development and training of algorithms to classify function of the knee and monitor knee movement. The data should also be accurate and reliable, and include a broad level of functionality, for performance of all abilities to be monitored accurately during rehabilitation. Only once this is achieved can healthcare professionals make informed decisions regarding patients.
Summary of Results
This research investigated whether wearable sensors (IMUs) can accurately measure knee motion during recovery after knee replacement surgery (TKA). The study compared these sensors with a gold-standard motion capture system (Vicon) and found excellent results. The study included data from healthy participants (younger students and older adults), as well as TKA patients.
This research project had three major objectives, with the results of each detailed below.
1. Firstly the research aimed to determine the accuracy of the Wearable Device (MotionSense™):
o The MotionSense™ device could measure knee movement within a 5° error margin in both healthy people and TKA patients, during everyday activities.
o It worked well for most movements but struggled with deep knee bends and fast or complex motions. Still, it showed strong potential for use in healthcare and rehabilitation to track recovery progress.
2. The next objective was Understanding TKA Recovery:
o During this study we examined how patients recover after TKA, measuring their knee ROM and subjective satisfaction (via PROM scores).
o Key findings:
- Patients who were in better shape before surgery tended to recover better.
- However, patients with higher expectations before surgery sometimes felt less satisfied, even when their mobility improved.
- The study emphasised the importance of tailoring recovery plans to each patient since average results don't always reflect individual outcomes.
3. The final aim of the research evaluated the feasibility of an Open-Source Algorithm:
o An open-source algorithm is defined as an existing, openly available algorithm, that may be accessed by anyone to use freely. The final study tested whether an open-source algorithm could track knee motion using IMU sensors. It was also found to perform well, with less than 5° error across various activities.
o Errors in deep knee bends or fast movements weren’t a major issue for TKA patients, as they typically have limited ROM and slower movements.
o The algorithm’s flexibility makes it suitable for different clinical applications.
Overall Conclusions:
• Wearable devices like MotionSense™ and IMU sensors, combined with advanced algorithms, are accurate enough to monitor knee recovery and help guide rehabilitation.
• These tools could improve surgical planning and recovery by providing continuous data on a patient’s progress, potentially leading to more personalised treatment plans.
• With further development, they could revolutionise knee surgery and rehabilitation by integrating real-time data into healthcare systems.
The study highlights the importance of individualised care and the growing role of technology in improving surgical and rehabilitation outcomes.REC name
West of Scotland REC 4
REC reference
22/WS/0084
Date of REC Opinion
29 Aug 2022
REC opinion
Further Information Favourable Opinion