write a abstract for an article in 200 words, the summary of the article is 'This research design and develop a finger motion tracking system using magnetic sensors, investigates its fundamental technical details analytically, and validated its performance experimentally. The method of detecting finger movement mentioned in this thesis avoids the occlusion issues of vision-based hand-finger motion tracking systems and proposes a compact, convenient, sensor-based system, which effectively reduces costs and energy consumption.
In this research, a small permanent magnet and a set of inertial and magnetic sensors are utilized respectively for calculating relative orientation and relative location. The permanent magnet is fixed on the dorsal side of the hand with Raspberry Pi 4b as our computer and control center unit, and the integrated sensor chips are fixed on the fingertips with connections to the SBC extend board through cables. 
In the designed architecture of the system, the angular velocity, acceleration rate and magnetic flux density are measured and recorded in real-timel by a 9-axis magnetic sensor icm-20948 in the magnetic field generated on the dorsal side of the hand. All data from 6 sensors are collected and transmitted via I2C communication simultaneously by an open source library for icm-20948 at an innovatively rate of 400kHz loaded in SBC, which is novel, convenient and universal for similar sensors utilization.
The finger motions are generated from the mathematical result of estimating relative orientations and locations between fingertips and the dorsal side of the hand, which requires a lot of matrix and differentiation operations. The positioning principle and algorithms used in our research are basically the same as the referential article [15] in the magnetic dipole model of the permanent magnet. A finger motion estimating system is proposed based on estimating the relative orientation based on the EKF algorithhm and estimating relative location based on the L-M algorithm. The dipole model is the fundamental theory in the derivation of estimating equations in magnetic field.
Finally, we compared the horizontal distance between the sensor and the magnet with its regression values under the dipole model in the experimental section. We determine the most suitable size of the permanent magnet and the effective range of the dipole model. Outside the range of too close or too far, the error of the dipole model will be too large to accept, which is impossible and impractical to accurately estimate the relative position of the fingertips. The orientation of the fingertip to the hand system in the transection through the z-axis of the dipole model is also presented in an  experiment. After moving the two sensors horizontally and vertically, we obtained stable images of round-trip motion, proving the accuracy of estimating orientation and the performance of real-time data transmission and processing within a certain range.
For the application of the finger motion tracking system, we design a real-time gesture recognization function based on the deep learning method. The system can recognize any gesture out of the preset library after learning in real-time and change rapidly when the gesture changes correspondingly. This could be utilized in applications of computer-human interaction or augmented reality technology as a method of intelligent input without traditional computer-human interaction devices such as keyboard, mouse, or buttons. It is also hygienic and convenient for medicine rehabilitation due to the low cost, low power consumption, and contactless characteristics of this system device.
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