Lower-body Total Joint Replacement
Conditions
Keywords
Total joint replacement, Physiotherapy, Kalman filtering, Kinematic modeling, Hidden Markov model, Motion segmentation
Brief summary
Physiotherapists spend a large amount of their time with patients observing their rehabilitation techniques. A patient going through rehabilitation exercises are routine and does not necessarily require the attention of the physiotherapist. This research will develop a sensor system that will be strapped onto the patients and will provide feedback on how accurately the exercise is being executed. This will free up the physiotherapist to focus on diagnosis and other tasks that will better utilize the physiotherapist's training. A previous study has shown that this system is feasible for healthy subjects. This study would test to see if this system is extendable to rehabilitation subjects.
Detailed description
Many of the tasks performed regularly by physiotherapists during any given rehabilitation session are repetitive and do not rely on the physiotherapist's expertise, and could be performed and observed by automated means. The developed system will detect patient body postures and movements with data collected through sensors such as accelerometers. This data will be pattern matched to a predetermined movement pattern and feedback will be provided for patients regarding accuracy of their exercises. This data can be logged for the physiotherapist to examine at a later time. By automating this component of a rehabilitation session, the system will allow the physiotherapist to focus diagnosis and other tasks that will better utilize their training. The specific application for this prototype will be post-operative knee/hip replacement patients, so all devices must be non-invasive and must not interfere with normal recovery processes. A previous version of this experiment on healthy participants has been successfully performed. This study would like to examine the feasibility of this system on rehabilitation subjects, as the movement patterns of a subject in physical rehabilitation may be dramatically different then a healthy subject. No intervention is suggested by the system, as this study is observational.
Interventions
None listed
Sponsors
Toronto Rehabilitation Institute
Natural Sciences and Engineering Research Council, Canada
University of Waterloo
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* Any patient who has had hip and/or knee total joint replacement and requires rehabilitation, and are assessed by physiotherapists as likely able to finish their rehabilitation cycle and be discharged to home. * Patients with medical complications or other injuries will be not be excluded, as we are interested in seeing the movement profiles of a wide range of people. * In-patients
Exclusion criteria
* Patients at risk of developing serious postoperative complications, such as an infection, myocardial infarction or anything that requires subsequent surgery, they may be excluded. * Out-patients. * Patients who cannot give explicit consent or understand the physiotherapist's instructions * Patients who do not speak fluent English
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Joint angle assessment | 2 weeks | Inertial measurement unit (IMU) data will be translated to joint angles via extended Kalman filter and kinematic modeling. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Motion segmentation and identification | 2 weeks | The joint angle data will be processed to automatically segment and identify rehabilitation motion. |
Countries
Canada
Outcome results
None listed