Development of a Wearable Glove System with Multiple Sensors for Hand Kinematics Assessment

Abstract

In traditional hand function assessment, patients and physicians always need to accomplish complex activities and rating tasks. This paper proposes a novel wearable glove system for hand function assessment. A sensing system consisting of 12 nine-axis inertial and magnetic unit (IMMU) sensors is used to obtain the acceleration, angular velocity, and geomagnetic orientation of human hand movements. A complementary filter algorithm is applied to calculate the angles of joints after sensor calibration. A virtual hand model is also developed to map with the glove system in the Unity platform. The experimental results show that this glove system can capture and reproduce human hand motions with high accuracy. This smart glove system is expected to reduce the complexity and time consumption of hand kinematics assessment.

Intelligent Clothing Design and Data Acquisition under the Scientific Graphics Programming Combined with Wearable Multisensor

“…For the data acquisition of the intelligent Hanfu design system, due to the influence of external interference, the original data of the nine-axis sensor cannot be used directly, and the human body in motion is more complex [11]. erefore, the accelerometer, gyroscope, and magnetometer must be preprocessed to calibrate the data [12]. Next, error analysis and data preprocessing are carried out for data collected by different sensors.…”

Design and Validation of an IMU Based Full Hand Kinematic Measurement System

“…In contrast, many of the existing IMU based data gloves output data at lower sampling rates (e.g., 20 Hz [11], 50 Hz [17-18, 20, 23, 25], 60 Hz [21] etc.,) while some IMU based data gloves have lesser number of IMU sensors [12,17]. Furthermore, the HKMS uses Wi-Fi for wireless data transmission.…”

“…Many IMU data gloves output orientation data at low sampling rates, e.g., 20Hz [11], 50 Hz [18], 60 Hz [21] etc. Some IMU data gloves output raw data at sampling rates of 50 Hz [17,20,23,25], 100 Hz [12,16,22] etc., and process the raw data using sensor fusion algorithms to get orientation data in real time. However, some of these studies have not clearly stated what is the orientation data output rate after processing the raw data.…”

“…Over the last decade, the use of Inertial Measurement Units (IMUs) for the measurement of hand kinematics has gained serious traction due to their affordability, ruggedness, ease of use, and relatively good accuracy. Many existing IMU-based data gloves [12,[16][17][18][19][20][21][22][23][24][25] measure rudimentary quantities such as acceleration, tilt rate and magnetic field using the IMUs accelerometer, gyroscope and magnetometer, respectively. These quantities are then processed using sensor fusion algorithms that use appropriate filters to obtain the orientation of the IMU sensor and, in turn, the orientation of the finger phalanx to which the IMU is attached to.…”

“…The orientation data is in turn used to compute joint angles. Of late, a few IMUs such as the BNO055 (by Bosch Sensortec), BNO080/85/86 (co-developed by Hillcrest Labs and Bosch Sensortec), MPU9250 (by InvenSense) etc., have gained immense popularity amongst hobbyists and researchers alike [11,12]. This is due to the fact that they have an onboard microcontroller (integrated into the IMUs System in Package (SiP)) that implements a sensor fusion algorithm to output orientation data (in the form of quaternions and/or Euler angles) in real-time at sampling rates as high as 100 Hz.…”