Rehabilitation following knee injury or surgery is critical for recovery of function and independence. However, patient non-adherence remains a significant barrier to success. Remote rehabilitation using mobile health (mHealth) technologies have potential for improving adherence to and execution of home exercise. We developed a remote rehabilitation management system combining two wireless inertial measurement units (IMUs) with an interactive mobile application and a web-based clinician portal (interACTION). However, in order to translate interACTION into the clinical setting, it was first necessary to verify the efficacy of measuring knee motion during rehabilitation exercises for physical therapy and determine if visual feedback significantly improves the participant’s ability to perform the exercises correctly. Therefore, the aim of this study was to verify the accuracy of the IMU-based knee angle measurement system during three common physical therapy exercises, quantify the effect of visual feedback on exercise performance, and understand the qualitative experience of the user interface through survey data. A convenience sample of ten healthy control participants were recruited for an IRB-approved protocol. Using the interACTION application in a controlled laboratory environment, participants performed ten repetitions of three knee rehabilitation exercises: heel slides, short arc quadriceps contractions, and sit-to-stand. The heel slide exercise was completed without feedback from the mobile application, then all exercises were performed with visual feedback. Exercises were recorded simultaneously by the IMU motion tracking sensors and a video-based motion tracking system. Validation showed moderate to good agreement between the two systems for all exercises and accuracy was within three degrees. Based on custom usability survey results, interACTION was well received. Overall, this study demonstrated the potential of interACTION to measure range of motion during rehabilitation exercises for physical therapy and visual feedback significantly improved the participant’s ability to perform the exercises correctly.
Miniature inertial measurement units (IMUs) are wearable sensors that measure limb segment or joint angles during dynamic movements. However, IMUs are generally prone to drift, external magnetic interference, and measurement noise. This paper presents a new class of nonlinear state estimation technique called state-dependent coefficient (SDC) estimation to accurately predict joint angles from IMU measurements. The SDC estimation method uses limb dynamics, instead of limb kinematics, to estimate the limb state. Importantly, the nonlinear limb dynamic model is formulated into state dependent matrices that facilitate the estimator design without performing a Jacobian linearization. The estimation method is experimentally demonstrated to predict knee joint angle measurements during functional electrical stimulation of the quadriceps muscle. The nonlinear knee musculoskeletal model was identified through a series of experiments. The SDC estimator was then compared to an Extended Kalman filter (EKF), which uses a Jacobian linearization and a rotation matrix method, which uses a kinematic model instead of the dynamic model. Each estimator’s performance was evaluated against the true value of the joint angle, which was measured through a rotary encoder. The experimental results showed that the SDC estimator, the rotation matrix method, and EKF had root mean square errors of 2.70°, 2.86°, and 4.42°, respectively. Our preliminary experimental results show the new estimator’s advantage over the EKF method but a slight advantage over the rotation matrix method. However, the information from the dynamic model allows the SDC method to use only one IMU to measure the knee angle compared to the rotation matrix method that uses 2 IMUs to estimate the angle.
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