Robotic Replica of a Human Spine Uses Soft Magnetic Sensor Array to Forecast Intervertebral Loads and Posture after Surgery

Cervical disc implants are conventional surgical treatments for patients with degenerative disc disease, such as cervical myelopathy and radiculopathy. However, the surgeon still must determine the candidacy of cervical disc implants mainly from the findings of diagnostic imaging studies, which can sometimes lead to complications and implant failure. To help address these problems, a new approach was developed to enable surgeons to preview the post-operative effects of an artificial disc implant in a patient-specific fashion prior to surgery. To that end, a robotic replica of a person’s spine was 3D printed, modified to include an artificial disc implant, and outfitted with a soft magnetic sensor array. The aims of this study are threefold: first, to evaluate the potential of a soft magnetic sensor array to detect the location and amplitude of applied loads; second, to use the soft magnetic sensor array in a 3D printed human spine replica to distinguish between five different robotically actuated postures; and third, to compare the efficacy of four different machine learning algorithms to classify the loads, amplitudes, and postures obtained from the first and second aims. Benchtop experiments showed that the soft magnetic sensor array was capable of precisely detecting the location and amplitude of forces, which were successfully classified by four different machine learning algorithms that were compared for their capabilities: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and Artificial Neural Network (ANN). In particular, the RF and ANN algorithms were able to classify locations of loads applied 3.25 mm apart with 98.39% ± 1.50% and 98.05% ± 1.56% accuracies, respectively. Furthermore, the ANN had an accuracy of 94.46% ± 2.84% to classify the location that a 10 g load was applied. The artificial disc-implanted spine replica was subjected to flexion and extension by a robotic arm. Five different postures of the spine were successfully classified with 100% ± 0.0% accuracy with the ANN using the soft magnetic sensor array. All results indicated that the magnetic sensor array has promising potential to generate data prior to invasive surgeries that could be utilized to preoperatively assess the suitability of a particular intervention for specific patients and to potentially assist the postoperative care of people with cervical disc implants.

Biaxial FBAR Magnetic Sensor Based on Fe80Ga20 Anisotropic ΔE Effect

With the increasing application of personal navigation system in consumer electronics, the demand for multi-axis magnetic sensors based on MEMS is growing. We report a biaxial MEMS DC magnetic sensor consisting of an Mo/AlN/Fe80Ga20 film bulk acoustic resonator (FBAR), with anisotropy ΔE effect-based sensing principle. Different from the previously reported one-dimensional magnetic sensor based on the ΔE effect, the anisotropic ΔE effect was used to realize in-plane and out-of-plane two-dimensional magnetic field responses on a discrete sensor, and the sensor had two readout methods: resonant frequency f and return loss S11. The magnetic sensor realized the resonant frequency f shifted by 1.03 MHz and 0.2 MHz in the 567 Oe in-plane magnetic field and 720 Oe out-of-plane magnetic field, respectively, and the S11 changes by -30.2 dB and -0.92 dB. As the applied magnetic field increases, the -3 dB bandwidth quality factor Q3dB of the S11 curve gradually increases, and its maximum values in the in-plane and out-of-plane magnetic fields are 77143 and 1828, respectively, which reduces the detection limit of the magnetic sensor. The resonant magnetic sensor has stable high linear temperature and frequency drift characteristics, and its temperature frequency coefficient is -48.7 ppm/℃.

Magnetic Field-Based Vehicle Positioning System in Long Tunnel Environment

Recently, long tunnels are becoming more prevalent in Korea, and exits are added at certain sections of the tunnels. Thus, a navigation system should correctly guide the user toward the exit; however, adequate guidance is not delivered because the global navigation satellite system (GNSS) signal is not received inside a tunnel. Therefore, we present an accurate position estimation system using a magnetic field for vehicles passing through a tunnel. The position can be accurately estimated using the magnetic sensor of a smartphone with an appropriate attitude estimation and magnetic sensor calibration. Position estimation was realized by attaching the smartphone on the dashboard during navigation and calibrating the sensors using position information from the GNSS and magnetic field database before entering the tunnel. This study used magnetic field sequence data to estimate vehicle positions inside a tunnel. Furthermore, subsequence dynamic time warping was applied to compare the magnetic field data stored in the buffer with the magnetic field database, and the feasibility and performance of the proposed system was reviewed through an experiment in an actual tunnel. The analysis of the position estimation results confirmed that the proposed system could appropriately deliver tunnel navigation.

Analysis of the problems of mechanical gearboxes of transport systems and their solution using a magnetic sensor

Objective: Increasing reliability of mechanical gearboxes using a magnetic sensor, which will sig nifi cantly increase the reliability of transport mechanisms and systems in general. Methods: An analysis of incidents covered in the media was carried out. Results: Conclusions are drawn about the consequences of failure of mechanical gearboxes. To prevent emergency situations caused by a breakdown of a mechanical gearbox, it is proposed to use a magnetic sensor, which allows carrying out analysis and diagnostics of the gearbox and its components during operation. A mathematical model of a magnetic sensor is compiled and a block diagram of a device for processing the output signal of the sensor modulator is presented. Practical importance: Failure of a mechanical gearbox is a common problem that leads to a stop of transport mechanisms and systems. Detection of a mechanical gearbox malfunction can signifi cantly reduce the cost of repairs, as well as prevent unexpected damage to the mechanism, which can lead to a complete stop of the transport system. The use of a magnetic sensor in the traction gearbox of an electric train will reduce the likelihood of a traction gearbox breakdown during operation and reduce the cost of repairs, increase the reliability of the traction drive, and also simplify the commissioning of new electric trains and traction gearboxes.

The Effect of Arrive Angle of External Magnetic Field on The Shape of Hysteresis Curve Permalloy Ni80Fe20 By Simulation

Magnetic sensor is a type of sensor that utilizes changes in resistance caused by changes in the magnetic field H or B. One of the suitable magnetic materials to be used as a study material for making magnetic sensors is permalloy Ni80Fe20. The reading error of the magnetic sensor of the Ni80Fe20 permalloy material affects hysteresis curve of the material and requires correction of the angle of incidence of the external magnetic field in order to provide accurate results on the storage media. In this our current research using Finite Difference OOMMF, we investigated the effect of the angle of incidence of the external magnetic field (H) on the hysteresis curve was carried out on an application based on. The research was conducted by reviewing the parameter literature of the Ni80Fe20 permalloy material and then compiling it in a script and simulating it on an application based on Finite Difference OOMMF. The data obtained from the simulation are normalized magnetization (m), external magnetic field H and coercivity field (Hc) which have been influenced by the angle of incidence. The results of the hysteresis curve at a size of 5 nm with a variation of the angle of incidence 0o are indicated by the value of the external magnetic field H of 10000 mT to -10000 mT with a coercive field Hc of 5000 mT to -5000 mT. The normalized magnetization value m is 1 to -1. The variation of the angle of incidence of 30o produces a coercive field Hc of -108.3 mT to 108.3 mT and a normalized magnetization of 0.86 to -0.86. The 45o incident angle variation produces a coercive field Hc -88.4 mT to 88.4 mT and a normalized magnetization of -0.7 to 0.7

Performance of a Noninvasive Magnetic Sensor-Based Current Measurement System in Power Systems

A large increase in distributed generation integrated within power system networks has resulted in power quality challenges and in the need to resolve complex system faults. The monitoring of the real-time state of the power parameters of the transmission and distribution grid helps to control the stability and reliability of the grid. In such a scenario, having current monitoring equipment that is flexible and easy to install can always be of great help to reduce the price of energy monitoring and to increase the dependability of a smart grid. Advances in magnetic sensor research offer measurement system accuracy that is less complex to install and that can be obtained at a lower less cost. Tunneling magnetoresistive (TMR) sensors can be used to measure the AC current by sensing the magnetic field that is generated by the current-carrying conductor in a contactless manner. This paper illustrates the results of a thorough investigation of factors that can influence the performance of the TMR sensors that are used for the current phasor measurements of a single-phase AC current application, such as the effects of distance, harmonics, and conductor insulation.