Abstract
An extremity rehabilitation program was proposed based on inertial measurement units (IMU) and virtual reality. A single IMU consists of a three-axis accelerometer, gyroscope, and geomagnetic sensors. One IMU is attached to the upper arm (master) and another to the forearm (slave). The IMUs are connected using a distributed sensor network implemented with interintegrated circuit communication. The motion-tracking algorithm running on a PC tracks the subject's hand based on the estimated IMU orientation and segment lengths through forward kinematics. The training contents, including various dynamic movements and static holds, were designed to evaluate the spatiotemporal aspects of the subject's functionality. The system was tested on a group of healthy subjects and a group with a simulated stiff elbow, allowing the evaluations to be quantitatively differentiated. The stiff elbow was simulated by taping the elbow to restrict the range of elbow motion. We expect the patients to be able to assess their own status without assistance from a therapist and select appropriate training methods to increase their rehabilitation effectiveness. Future studies will verify the availability and reliability of the upper extremity rehabilitation program for patients with a hemiplegia, leading to the development of an upper extremity rehabilitation program for three-dimensional movements of the upper extremities.
Highlights
A stroke, which is a type of cerebrovascular condition, is the third-highest cause of death in the United States [1]
Stroke patients often regain consciousness after onset, 30– 40% of patients suffer from hemiplegic complications such as a speech disorder or dementia, impeding their ability to live a normal life
Among the various disorders caused by a stroke, hemiplegia is very typical, with more than 80% of stroke patients displaying some form of hemiplegic disability [2]
Summary
A stroke, which is a type of cerebrovascular condition, is the third-highest cause of death in the United States [1]. We highlight the development of a motion-tracking system based on the use of inertial sensors and a distributed sensor network and show the functional feasibility of the rehabilitation system. In this indirect Kalman filter, the error state including the orientation, gyroscope offset, and magnetic distortion, xε,t =. QVG and QVG correspond to the covariances of the gyroscope offset and magnetic disturbance With these matrixes, the indirect Kalman filter estimates the current state from the previous state and state transition matrix A. Pk+1|k indicates the covariance of the state error estimated at time k + 1, based on that at previous time k: Pk+1|k = A kPk|kATk + Qk. xεk+1|k = Kk+1Vk+1, Pk+1|k+1 = A kPk+1|k+1ATk+1 + Qk+1, where zk+1 is a measurement consisting of acceleration and magnetometer signals, and Kk+1 corresponds to the Kalman filter gain.
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