Studies suggest that up to one-third of people in the general population have shown knee joint problems with radiological evidence (G. Peat et al., 2001). Knee osteoarthritis (OA), for example, is the single most common cause of disability in older adults. Current technology used to monitor the development of the injury still relies on complex and expensive 3-Dimensional Motion Capture System (3D-Mocap). Regardless of the precision and the accuracy of the 3D-Mocap, this system has some drawbacks, such as the complexity of the equipment and installation, and the high costs which make this system unaffordable for patient in-home monitoring and rehabilitation.
To address these issues, ITRI collaborated with Graduate Institute of Biomedical Engineering from National Taiwan University of Science and Technology to develop a smart knee brace that can detect abnormal knee alignment and send feedback when the user has an abnormal muscle activity. The smart knee brace consists of an electromyography (EMG) sensor which can record muscle activity and a triaxial accelerometer to record acceleration on the segment. The EMG sensors are placed on Vastus Lateralis and Vastus Medialis muscle to continuously monitor muscle activity. When there is abnormality detected, the system will generate tactile feedback to the designated muscle. This form of feedback is useful to solve the muscle imbalance problem which is common in athletes and may reduce sport injury risks.
Besides providing immediate tactile feedback, collected EMG and accelerometer data will also be used for other analysis, for example, diagnosis on knee alignment problems. With the trained model from Deep Neural Network, the data from sensors can be used to detect knee abnormality. More advanced application will be available in the future for predicting biomechanical variables such as joint movement, or joint loading with a combination of wearable sensor data and deep learning method. In the future, users will be able to know exactly how much loading they put on their knee joint when doing exercise or high risk activities with only using smart knee braces with wearable sensors. Compared with conventional knee braces which only correct the alignment, this solution opens up an opportunity to continuously monitor and record user’s data and develop recommendations for better movement strategy or exercise prescription.
In sum, I see much potential for this integrated system developed by ITRI. It can create a smart in-home healthcare monitoring and rehabilitation system which will bring more convenience to patients while providing clinicians simpler tools for knee injury monitoring to enhance the quality of diagnosis and treatment.
Future extended application for in-home healthcare with smart knee brace and wearable sensors.
G. Peat, R. McCarney, and P. Croft, Knee pain and osteoarthritis in older adults: a review of community burden and current use of primary health care. Annals of the Rheumatic Diseases. 60 (2), 91-97 (2001).
W. Hsu, et al., Controlled tactile and vibration feedback embedded in a smart knee brace. IEEE Consumer Electronics Magazine. 9 (1), 54-60 (2019).
Tommy Sugiarto is an associate engineer working at ITRI. Currently, he is also a PhD candidate in Biomedical Engineering at National Taiwan University of Science and Technology (NTUST). He got his master degree also in the same major and university while his bachelor degree was obtained from Department of Physics, University of Indonesia. His research interest is in Artificial Intelligence especially in biomedical applications.