Abstract
Relative orientation estimation between the hand and its fingers is important in many applications, such as virtual reality (VR), augmented reality (AR) and rehabilitation. It is still quite a big challenge to do the estimation by only exploiting inertial measurement units (IMUs) because of the integration drift that occurs in most approaches. When the hand is functionally used, there are many instances in which hand and finger tips move together, experiencing almost the same angular velocities, and in some of these cases, almost the same accelerations are measured in different 3D coordinate systems. Therefore, we hypothesize that relative orientations between the hand and the finger tips can be adequately estimated using 3D IMUs during such designated events (DEs) and in between these events. We fused this extra information from the DEs and IMU data with an extended Kalman filter (EKF). Our results show that errors in relative orientation can be smaller than five degrees if DEs are constantly present and the linear and angular movements of the whole hand are adequately rich. When the DEs are partially available in a functional water-drinking task, the orientation error is smaller than 10 degrees.
Highlights
Hand-finger movement tracing is useful in many areas, such as virtual reality (VR), augmented reality (AR), ergonomic assessment and especially medical applications [1,2,3,4,5]
The orientation error is smallest with the gyroscope and accelerometer, while the orientation error is largest with accelerometer data only
Compared with the inertial measurement units (IMUs)-based data glove system described by Salchov-Homer et al [19] and Kortier et al [28], we reduced the number of IMUs as much as possible and avoided magnetic disturbance, but still obtained comparable precision of estimated orientation
Summary
Hand-finger movement tracing is useful in many areas, such as virtual reality (VR), augmented reality (AR), ergonomic assessment and especially medical applications [1,2,3,4,5]. People who suffered from stroke or injury of the spinal cord need an effective rehabilitation therapy for recovery of body functions, including hand function. Therapists evaluate the hand function through some traditional assessments such as the Fugl–Meyer or Jebsen–Taylor hand function assessment [6,7]. The results may be subjective and dependent on the therapist. It is essential to provide a quantitative and understandable measurement to make the therapist’s diagnosis more objective. Several sensory systems can be used to trace hand motion, which can be categorized as camera-based, glove-based, magnetic actuator-based and inertial measurement unit (IMU)-based
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