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Invasive and Non-Invasive Sensors: From Technology to Clinical Practice

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (15 May 2022) | Viewed by 20434

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Special Issue Editors

Dr. Shunrou Fujiwara

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Guest Editor

Department of Neurosurgery, Iwate Medical University, 1-1-1 Idaidori, Yahaba, 028-3694 Iwate, Japan
Interests: gait assessment; magnetic resonance imaging; cognition; cerebral blood flow and metablism; neurosurgery

Prof. Dr. Kuniaki Ogasawara

E-Mail Website
Co-Guest Editor

Department of Neurosurgery, Iwate Medical University, 1-1-1 Idaidori, Yahaba, 028-3694 Iwate, Japan
Interests: surgery for cerebrovascular diseases; cerebral blood flow and metabolism; brain positron emission tomography; brain single-photon emission tomography; cognitive changes due to cerebrovascular diseases and surgery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Many monitoring techniques have been used for brain function assessments before and during surgical treatment to prevent any function deficits after treatments. Furthermore, even after surgical treatment, monitoring techniques play a role in identifying subtle changes during the follow-up period.

Wearable device
Sensors for upper/lower limbs in healthy subjects/patients (like accelerometer)
Sensors for pre/intra/post-operatively monitoring blood flow/metabolism (like NIRS)

Large machine
Sensors for surgical planning system (like BrainLab, Surgiplan...etc)
Sensors for motion capture system for assessing usefulness of rehabilitations

Implanted device
Sensors relating to Brain Machine Interface (like vison, auditory...etc)
Sensors for deep brain stimulation for Parkinson's disease and psychiatric disease.
Sensors supporting pacemakers relating to cardiology

The other external sensing/imaging device including CT/MRI/Ultrasound
Quantification of blood flow/metabolism/iron deposits
Quantification of cerebrospinal/lymph fluid
Quantification of brain function

This Special Issue invites researchers to focus on pre-, intra-, and post-operative motor function (or motor-related function such as cerebral blood flow change, neural activity, etc.) assessments with less-invasive small sensors such as wearable devices, mobile wireless devices, pressure-sensitive sensors, etc. We look forward to your great contributions, or those of your colleagues, enriching the contents of this Special Issue.

Dr. Shunrou Fujiwara
Prof. Dr. Kuniaki Ogasawara
Guest Editor

Manuscript Submission Information

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Keywords

less-invasive
wearable
wireless
pressure-sensitive
small
motor function
gait
hand grasp
cerebral blood flow change
neural activity
EEG/MEG

Published Papers (7 papers)

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Research

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16 pages, 2886 KiB

Open AccessArticle

Assessment System for Predicting Maximal Safe Range for Heel Height by Using Force-Sensing Resistor Sensors and Regression Models

by Yi-Ting Hwang, Si-Huei Lee and Bor-Shing Lin

Sensors 2022, 22(9), 3442; https://doi.org/10.3390/s22093442 - 30 Apr 2022

Cited by 3 | Viewed by 1408

Abstract

Women often wear high-heeled shoes for professional or esthetic reasons. However, high-heeled shoes can cause discomfort and injury and can change the body’s center of gravity when maintaining balance. This study developed an assessment system for predicting the maximal safe range for heel height by recording the plantar pressure of participants’ feet by using force-sensing resistor (FSR) sensors and conducting analyses using regression models. Specifically, 100 young healthy women stood on an adjustable platform while physicians estimated the maximal safe height of high-heeled shoes. The collected FSR data combined with and without personal features were analyzed using regression models. The experimental results showed that the regression model based on the pressure data for the right foot had better predictive power than that based on data for the left foot, regardless of the module. The model with two heights had higher predictive power than that with a single height. Furthermore, adding personal features under the condition of two heights afforded the best predictive effect. These results can help wearers choose maximal safe high-heeled shoes to reduce injuries to the bones and lower limbs. Full article

(This article belongs to the Special Issue Invasive and Non-Invasive Sensors: From Technology to Clinical Practice)

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16 pages, 3898 KiB

Open AccessArticle

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

by Maohua Lin, Moaed A. Abd, Alex Taing, Chi-Tay Tsai, Frank D. Vrionis and Erik D. Engeberg

Sensors 2022, 22(1), 212; https://doi.org/10.3390/s22010212 - 29 Dec 2021

Cited by 6 | Viewed by 4950

Abstract

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. Full article

(This article belongs to the Special Issue Invasive and Non-Invasive Sensors: From Technology to Clinical Practice)

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18 pages, 3089 KiB

Open AccessArticle

Towards Tracking of Deep Brain Stimulation Electrodes Using an Integrated Magnetometer

by Thomas Quirin, Corentin Féry, Dorian Vogel, Céline Vergne, Mathieu Sarracanie, Najat Salameh, Morgan Madec, Simone Hemm, Luc Hébrard and Joris Pascal

Sensors 2021, 21(8), 2670; https://doi.org/10.3390/s21082670 - 10 Apr 2021

Cited by 3 | Viewed by 3035

Abstract

This paper presents a tracking system using magnetometers, possibly integrable in a deep brain stimulation (DBS) electrode. DBS is a treatment for movement disorders where the position of the implant is of prime importance. Positioning challenges during the surgery could be addressed thanks to a magnetic tracking. The system proposed in this paper, complementary to existing procedures, has been designed to bridge preoperative clinical imaging with DBS surgery, allowing the surgeon to increase his/her control on the implantation trajectory. Here the magnetic source required for tracking consists of three coils, and is experimentally mapped. This mapping has been performed with an in-house three-dimensional magnetic camera. The system demonstrates how magnetometers integrated directly at the tip of a DBS electrode, might improve treatment by monitoring the position during and after the surgery. The three-dimensional operation without line of sight has been demonstrated using a reference obtained with magnetic resonance imaging (MRI) of a simplified brain model. We observed experimentally a mean absolute error of 1.35 mm and an Euclidean error of 3.07 mm. Several areas of improvement to target errors below 1 mm are also discussed. Full article

(This article belongs to the Special Issue Invasive and Non-Invasive Sensors: From Technology to Clinical Practice)

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20 pages, 7381 KiB

Open AccessArticle

Real-Time Expanded Field-of-View for Minimally Invasive Surgery Using Multi-Camera Visual Simultaneous Localization and Mapping

by Ahmed Afifi, Chisato Takada, Yuichiro Yoshimura and Toshiya Nakaguchi

Sensors 2021, 21(6), 2106; https://doi.org/10.3390/s21062106 - 17 Mar 2021

Cited by 8 | Viewed by 2279

Abstract

Minimally invasive surgery is widely used because of its tremendous benefits to the patient. However, there are some challenges that surgeons face in this type of surgery, the most important of which is the narrow field of view. Therefore, we propose an approach to expand the field of view for minimally invasive surgery to enhance surgeons’ experience. It combines multiple views in real-time to produce a dynamic expanded view. The proposed approach extends the monocular Oriented features from an accelerated segment test and Rotated Binary robust independent elementary features—Simultaneous Localization And Mapping (ORB-SLAM) to work with a multi-camera setup. The ORB-SLAM’s three parallel threads, namely tracking, mapping and loop closing, are performed for each camera and new threads are added to calculate the relative cameras’ pose and to construct the expanded view. A new algorithm for estimating the optimal inter-camera correspondence matrix from a set of corresponding 3D map points is presented. This optimal transformation is then used to produce the final view. The proposed approach was evaluated using both human models and in vivo data. The evaluation results of the proposed correspondence matrix estimation algorithm prove its ability to reduce the error and to produce an accurate transformation. The results also show that when other approaches fail, the proposed approach can produce an expanded view. In this work, a real-time dynamic field-of-view expansion approach that can work in all situations regardless of images’ overlap is proposed. It outperforms the previous approaches and can also work at 21 fps. Full article

(This article belongs to the Special Issue Invasive and Non-Invasive Sensors: From Technology to Clinical Practice)

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14 pages, 2499 KiB

Open AccessArticle

Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty

by Carlo Dindorf, Wolfgang Teufl, Bertram Taetz, Gabriele Bleser and Michael Fröhlich

Sensors 2020, 20(16), 4385; https://doi.org/10.3390/s20164385 - 06 Aug 2020

Cited by 40 | Viewed by 3714

Abstract

Many machine learning models show black box characteristics and, therefore, a lack of transparency, interpretability, and trustworthiness. This strongly limits their practical application in clinical contexts. For overcoming these limitations, Explainable Artificial Intelligence (XAI) has shown promising results. The current study examined the influence of different input representations on a trained model’s accuracy, interpretability, as well as clinical relevancy using XAI methods. The gait of 27 healthy subjects and 20 subjects after total hip arthroplasty (THA) was recorded with an inertial measurement unit (IMU)-based system. Three different input representations were used for classification. Local Interpretable Model-Agnostic Explanations (LIME) was used for model interpretation. The best accuracy was achieved with automatically extracted features (mean accuracy M_acc = 100%), followed by features based on simple descriptive statistics (M_acc = 97.38%) and waveform data (M_acc = 95.88%). Globally seen, sagittal movement of the hip, knee, and pelvis as well as transversal movement of the ankle were especially important for this specific classification task. The current work shows that the type of input representation crucially determines interpretability as well as clinical relevance. A combined approach using different forms of representations seems advantageous. The results might assist physicians and therapists finding and addressing individual pathologic gait patterns. Full article

(This article belongs to the Special Issue Invasive and Non-Invasive Sensors: From Technology to Clinical Practice)

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7 pages, 565 KiB

Open AccessBrief Report

Brain Temperature Measured by Magnetic Resonance Spectroscopy to Predict Clinical Outcome in Patients with Infarction

by Tomohisa Ishida, Takashi Inoue, Tomoo Inoue, Toshiki Endo, Miki Fujimura, Kuniyasu Niizuma, Hidenori Endo and Teiji Tominaga

Sensors 2021, 21(2), 490; https://doi.org/10.3390/s21020490 - 12 Jan 2021

Cited by 7 | Viewed by 2266

Abstract

Acute ischemic stroke is characterized by dynamic changes in metabolism and hemodynamics, which can affect brain temperature. We used proton magnetic resonance (MR) spectroscopy under everyday clinical settings to measure brain temperature in seven patients with internal carotid artery occlusion to explore the relationship between lesion temperature and clinical course. Regions of interest were selected in the infarct area and the corresponding contralateral region. Single-voxel MR spectroscopy was performed using the following parameters: 2000-ms repetition time, 144-ms echo time, and 128 excitations. Brain temperature was calculated from the chemical shift between water and N-acetyl aspartate, choline-containing compounds, or creatine phosphate. Within 48 h of onset, compared with the contralateral region temperature, brain temperature in the ischemic lesion was lower in five patients and higher in two patients. Severe brain swelling occurred subsequently in three of the five patients with lower lesion temperatures, but in neither of the two patients with higher lesion temperatures. The use of proton MR spectroscopy to measure brain temperature in patients with internal carotid artery occlusion may predict brain swelling and subsequent motor deficits, allowing for more effective early surgical intervention. Moreover, our methodology allows for MR spectroscopy to be used in everyday clinical settings. Full article

(This article belongs to the Special Issue Invasive and Non-Invasive Sensors: From Technology to Clinical Practice)

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10 pages, 2155 KiB

Open AccessLetter

Comparison of Subjective and Objective Assessments on Improvement in Gait Function after Carotid Endarterectomy

by Tatsuhiko Takahashi, Shunrou Fujiwara, Suguru Igarashi, Toshihiko Ando, Kohei Chida, Masakazu Kobayashi, Kenji Yoshida, Takahiro Koji, Yoshitaka Kubo and Kuniaki Ogasawara

Sensors 2020, 20(22), 6590; https://doi.org/10.3390/s20226590 - 18 Nov 2020

Cited by 1 | Viewed by 1559

Abstract

The purpose of the present study was to determine whether objective gait test scores obtained using a tri-axial accelerometer can detect subjective improvement in gait as determined by the patient after carotid endarterectomy (CEA). Each patient undergoing CEA for ipsilateral internal carotid artery stenosis determined whether their gait was subjectively improved at six months after CEA when compared with preoperatively. Gait testing using a tri-axial accelerometer was also performed preoperatively and six months postoperatively. Twelve (15%) of 79 patients reported subjectively improved gait. Areas under the receiver operating characteristic curve for differences between pre- and postoperative test values in stride time, cadence, and ground floor reaction for detecting subjectively improved gait were 0.995 (95% confidence interval (CI), 0.945–1.000), 0.958 (95%CI, 0.887–0.990), and 0.851 (95%CI, 0.753–0.921), respectively. Cut-off points for value differences in detecting subjectively improved gait were identical to mean −1.7 standard deviation (SD) for stride time, mean +1.6 SD for cadence, and mean +0.4 SD for ground floor reaction of control values from normal subjects. Objective gait test scores obtained using the tri-axial accelerometer can detect subjective gait improvements after CEA. When determining significant postoperative improvements in gait using a tri-axial accelerometer, optimal cut-off points for each test value can be defined. Full article

(This article belongs to the Special Issue Invasive and Non-Invasive Sensors: From Technology to Clinical Practice)

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