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Novel Implantable Sensors and Biomedical Applications

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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 (25 December 2025) | Viewed by 23207

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Dr. Laura Cercenelli

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eDIMES Lab—Laboratory of Bioengineering, Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, 40138 Bologna, Italy
Interests: implantable sensors for monitoring cardio-pulmonary mechanics; prosthetic heart valves; 3D modeling; 3D printing for medical applications; virtual reality; augmented reality; patient-specific simulation for medical education; eye tracking systems
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Special Issue Information

Dear Colleagues,

With current technological advancements, the adoption of smart implants and non-invasive solutions is regaining momentum in managing chronic conditions, such as cardiovascular diseases, cancer, cognitive impairment, and managing and remotely monitoring infectious diseases, such as the novel coronavirus disease 2019 (COVID-19).

Various biomedical sensors have been successfully implemented to measure some important physiological parameters, such as heart rate and blood pressure, using both implantable sensor systems and noninvasive devices. Further, connecting these devices to a portable/wireless power supply and data transmission unit has the potential to support remote patient-tailored treatment and diagnostics of many diseases.

The development of implantable and noninvasive sensor systems which can be deployed at point of care, used at home, or integrated into wearable devices is a key factor to enable effective disease prevention, real-time health data collection and monitoring of chronic diseases, and early detection of diseases even before symptoms occur. This could reduce costs and time of hospitalization while improving the quality of life and mobility of patients.

The purpose of this Special Issue is to bring together state-of-the-art applications of both implantable and noninvasive sensors in biomedical field, with particular focus on cardiovascular applications.

We encourage authors to submit original research articles, reviews, case studies on the following topics, but not limited to:

New sensor materials and technologies for cardiovascular applications;
Implantable sensors for biomedical applications;
Noninvasive monitoring systems;
Sensors and systems for remote patient monitoring;
Multi-sensory-based devices;
Sensor integration with artificial intelligence approach.

Dr. Laura Cercenelli
Guest Editor

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Published Papers (10 papers)

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Research

18 pages, 919 KB

Open AccessArticle

Development of a Machine Learning-Based Predictive Model and Clinically Oriented Web Application for 30-Day Mortality Following Cardiac Surgery

by Telmo Miguel-Medina, Susel Góngora Alonso, Isabel de la Torre Díez, Miriam Blanco Sáez, Hector Lazaro Arrechea Elissalt, Atenea Ruigómez Noriega and María Lourdes del Río Solá

Sensors 2026, 26(5), 1656; https://doi.org/10.3390/s26051656 - 5 Mar 2026

Cited by 1 | Viewed by 669

Abstract

This study aimed to develop and validate a machine learning-based model for predicting 30-day mortality in cardiac surgery patients and to implement a functional, clinician-oriented web application that enables the real-time use of the model. A retrospective cohort of 325 cardiac surgery patients was analysed using supervised machine learning. After preprocessing and clinical feature selection, several models were trained and evaluated through cross-validation. XGBoost achieved the best results, with an AUC-ROC of 0.968, recall of 0.800, and Brier score of 0.058. To facilitate clinical usability, a web-based application was developed using StreamLit, enabling clinicians to input patient data and predict mortality in real time. The application includes SHAP-based explainability for each prediction, thereby ensuring model transparency. Preliminary feedback from clinicians indicated that the tool was intuitive and informative and showed potential for preoperative risk assessment. The integration of a robust ML (machine learning) model with a functional clinical application offers a practical tool for supporting decision-making in cardiac surgery. This combined approach enhances both accuracy and accessibility, which are key to real-world impacts. Future work will involve multicentre validation and user-centred refinement. Full article

(This article belongs to the Special Issue Novel Implantable Sensors and Biomedical Applications)

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23 pages, 3295 KB

Open AccessArticle

A Two-Level Ensemble Machine Learning Framework for OSA Classification Whilst Awake from Noisy Tracheal Breathing Sounds

by Vahid Bastani Najafabadi, Walid Ashraf, Ahmed Elwali and Zahra Moussavi

Sensors 2026, 26(4), 1349; https://doi.org/10.3390/s26041349 - 20 Feb 2026

Viewed by 532

Abstract

Obstructive sleep apnea (OSA), defined by repetitive airway obstruction during sleep, is significantly underdiagnosed, mainly due to the resource-intensive and time-consuming nature of sleep assessment technologies. Machine learning analysis of the tracheal breathing sounds (TBS) whilst awake offers an alternative approach for OSA quick screening. This study aimed to address the challenge of wakefulness OSA detection using TBS recorded with an inexpensive microphone in a noisy environment. Data of 247 individuals with various degrees of OSA severity were analyzed. Recorded data were segmented into inspiration and expiration phases, followed by acoustic features extraction, feature reduction, and classification. A two-level ensemble architecture was implemented. Nine sub-classifiers were stratified by anthropometric profiles. Each sub-classifier was constructed as an ensemble of bagged decision trees, with a final prediction via probability-based voting. The proposed algorithm achieved an accuracy of 77.1%, sensitivity of 84.3%, and specificity of 59.9%. Although these results have lower performance than those obtained previously using a high-quality microphone in a quiet room, they demonstrate that acoustic OSA detection whilst awake remains feasible, even in very noisy environments. Nevertheless, microphone quality emerged as a key determinant of classification performance. Full article

(This article belongs to the Special Issue Novel Implantable Sensors and Biomedical Applications)

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17 pages, 3031 KB

Open AccessArticle

Utilizing an Augmented Reality Headset to Accurately Quantify Lower Extremity Function in Parkinson’s Disease

by Andrew Bazyk, Colin Waltz, Ryan D. Kaya, Eric Zimmerman, Joshua D. Johnston, Benjamin L. Walter, Anson B. Rosenfeldt, Mandy Miller Koop and Jay L. Alberts

Sensors 2026, 26(4), 1216; https://doi.org/10.3390/s26041216 - 13 Feb 2026

Viewed by 609

Abstract

Subjective, imprecise evaluation of lower extremity function hinders the effective treatment of gait impairments in Parkinson’s disease (PD). Markerless motion capture (MMC) offers opportunities for integrating objective biomechanical outcomes into clinical practice. However, validation of MMC biomechanical outcomes is necessary for clinical adoption of MMC technologies. This project evaluated the criterion validity of a custom MMC algorithm (CART-MMC) against gold-standard 3D motion capture (Traditional-MC) and its known-groups validity in differentiating PD from healthy controls (HC). Sixty-two individuals with PD and 29 HCs completed a stepping in place paradigm. The trials were recorded by an augmented reality headset with embedded RGB and depth cameras. The CART-MMC algorithm was used to reconstruct a 3D pose model and compute biomechanical measures of lower extremity performance. CART-MMC outcomes were statistically equivalent, within 5% of Traditional-MC, for measures of step count, cadence, duration, height, height asymmetry, and normalized path length. CART-MMC captured significant between-group differences in step height, height variability, height asymmetry, duration variability, and normalized path length. In conclusion, CART-MMC provides valid biomechanical outcomes that characterize important domains of PD lower extremity function. Validated biomechanical evaluation tools present opportunities for tracking subtle changes in disease progression, informing targeted therapy, and monitoring treatment efficacy. Full article

(This article belongs to the Special Issue Novel Implantable Sensors and Biomedical Applications)

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15 pages, 6102 KB

Open AccessArticle

Design and Analysis of a Dual-Band Implantable Receiving Antenna for Wireless Power Transfer and Data Communication at 1.32 GHz and 2.58 GHz

by Ashfaq Ahmad, Sun-Woong Kim and Dong-You Choi

Sensors 2025, 25(24), 7507; https://doi.org/10.3390/s25247507 - 10 Dec 2025

Viewed by 999

Abstract

This paper presents the design and performance evaluation of a compact dual-band implantable antenna (Rx) operating at 1.32 GHz and 2.58 GHz for biomedical applications. The proposed antenna is designed to receive power and data from an external transmitting (Tx) antenna operating at 1.32 GHz. The measured impedance bandwidths of the Rx antenna are 190 MHz (1.23–1.42 GHz) and 230 MHz (2.47–2.70 GHz), covering both the power transfer and data communication bands. The wireless power transfer efficiency, represented by the transmission coefficient (

S_{21}

), is observed to be −40 dB at a spacing of 40 mm, where the Rx is located in the far-field region of the Tx. Specific Absorption Rate (SAR) analysis is performed to ensure electromagnetic safety compliance, and the results are within the acceptable exposure limits. The proposed antenna achieves a realized gain of −25 dB at 1.32 GHz and −25.8 dB at 2.58 GHz, demonstrating suitable performance for low-power implantable medical device communication and power transfer systems. The proposed design offers a promising solution for reliable biotelemetry and wireless power transfer in implantable biomedical systems. Full article

(This article belongs to the Special Issue Novel Implantable Sensors and Biomedical Applications)

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18 pages, 10549 KB

Open AccessArticle

Optimal Position and Orientation of an Ossicular Accelerometer for Human Auditory Prostheses

by Dmitrii Burovikhin, Panagiota Kitsopoulos, Michael Lauxmann and Karl Grosh

Sensors 2024, 24(24), 8084; https://doi.org/10.3390/s24248084 - 18 Dec 2024

Cited by 2 | Viewed by 1237

Abstract

In this study, a method for determining the optimal location and orientation of an implantable piezoelectric accelerometer on the short process of the incus is presented. The accelerometer is intended to be used as a replacement for an external microphone to enable totally implantable auditory prostheses. The optimal orientation of the sensor and the best attachment point are determined based on two criteria—maximum pressure sensitivity sum and minimum loudness level sum. The best location is determined to be near the incudomalleolar joint. We find that the angular orientation of the sensor is critical and provide guidelines on that orientation. The method described in this paper can be used to further optimize the design and performance of the accelerometer. Full article

(This article belongs to the Special Issue Novel Implantable Sensors and Biomedical Applications)

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12 pages, 6133 KB

Open AccessArticle

Passive Biotelemetric Detection of Tibial Debonding in Wireless Battery-Free Smart Knee Implants

by Thomas A. G. Hall, Frederic Cegla and Richard J. van Arkel

Sensors 2024, 24(5), 1696; https://doi.org/10.3390/s24051696 - 6 Mar 2024

Cited by 6 | Viewed by 2994

Abstract

Aseptic loosening is the dominant failure mechanism in contemporary knee replacement surgery, but diagnostic techniques are poorly sensitive to the early stages of loosening and poorly specific in delineating aseptic cases from infections. Smart implants have been proposed as a solution, but incorporating components for sensing, powering, processing, and communication increases device cost, size, and risk; hence, minimising onboard instrumentation is desirable. In this study, two wireless, battery-free smart implants were developed that used passive biotelemetry to measure fixation at the implant–cement interface of the tibial components. The sensing system comprised of a piezoelectric transducer and coil, with the transducer affixed to the superior surface of the tibial trays of both partial (PKR) and total knee replacement (TKR) systems. Fixation was measured via pulse-echo responses elicited via a three-coil inductive link. The instrumented systems could detect loss of fixation when the implants were partially debonded (+7.1% PKA, +32.6% TKA, both p < 0.001) and fully debonded in situ (+6.3% PKA, +32.5% TKA, both p < 0.001). Measurements were robust to variations in positioning of the external reader, soft tissue, and the femoral component. With low cost and small form factor, the smart implant concept could be adopted for clinical use, particularly for generating an understanding of uncertain aseptic loosening mechanisms. Full article

(This article belongs to the Special Issue Novel Implantable Sensors and Biomedical Applications)

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15 pages, 8298 KB

Open AccessArticle

First Ex Vivo Animal Study of a Biological Heart Valve Prosthesis Sensorized with Intravalvular Impedance

by Laura Cercenelli, Camilla Gironi, Barbara Bortolani and Emanuela Marcelli

Sensors 2023, 23(8), 3829; https://doi.org/10.3390/s23083829 - 8 Apr 2023

Cited by 3 | Viewed by 2822

Abstract

IntraValvular Impedance (IVI) sensing is an innovative concept for monitoring heart valve prostheses after implant. We recently demonstrated IVI sensing feasible in vitro for biological heart valves (BHVs). In this study, for the first time, we investigate ex vivo the IVI sensing applied to a BHV when it is surrounded by biological tissue, similar to a real implant condition. A commercial model of BHV was sensorized with three miniaturized electrodes embedded in the commissures of the valve leaflets and connected to an external impedance measurement unit. To perform ex vivo animal tests, the sensorized BHV was implanted in the aortic position of an explanted porcine heart, which was connected to a cardiac BioSimulator platform. The IVI signal was recorded in different dynamic cardiac conditions reproduced with the BioSimulator, varying the cardiac cycle rate and the stroke volume. For each condition, the maximum percent variation in the IVI signal was evaluated and compared. The IVI signal was also processed to calculate its first derivative (dIVI/dt), which should reflect the rate of the valve leaflets opening/closing. The results demonstrated that the IVI signal is well detectable when the sensorized BHV is surrounded by biological tissue, maintaining the similar increasing/decreasing trend that was found during in vitro experiments. The signal can also be informative on the rate of valve opening/closing, as indicated by the changes in dIVI/dt in different dynamic cardiac conditions. Full article

(This article belongs to the Special Issue Novel Implantable Sensors and Biomedical Applications)

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19 pages, 2366 KB

Open AccessArticle

Constrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients

by Ignacio Rodríguez-Rodríguez, María Campo-Valera, José-Víctor Rodríguez and Alberto Frisa-Rubio

Sensors 2023, 23(7), 3665; https://doi.org/10.3390/s23073665 - 31 Mar 2023

Cited by 18 | Viewed by 4274

Abstract

Individuals with diabetes mellitus type 1 (DM1) tend to check their blood sugar levels multiple times daily and utilize this information to predict their future glycemic levels. Based on these predictions, patients decide on the best approach to regulate their glucose levels with considerations such as insulin dosage and other related factors. Nevertheless, modern developments in Internet of Things (IoT) technology and innovative biomedical sensors have enabled the constant gathering of glucose level data using continuous glucose monitoring (CGM) in addition to other biomedical signals. With the use of machine learning (ML) algorithms, glycemic level patterns can be modeled, enabling accurate forecasting of this variable. Constrained devices have limited computational power, making it challenging to run complex machine learning algorithms directly on these devices. However, by leveraging edge computing, using lightweight machine learning algorithms, and performing preprocessing and feature extraction, it is possible to run machine learning algorithms on constrained devices despite these limitations. In this paper we test the burdens of some constrained IoT devices, probing that it is feasible to locally predict glycemia using a smartphone, up to 45 min in advance and with acceptable accuracy using random forest. Full article

(This article belongs to the Special Issue Novel Implantable Sensors and Biomedical Applications)

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16 pages, 4266 KB

Open AccessArticle

An Ex Vivo Study of Wireless Linkage Distance between Implantable LC Resonance Sensor and External Readout Coil

by Muhammad Farooq, Bilal Amin, Marcin J. Kraśny, Adnan Elahi, Muhammad Riaz ur Rehman, William Wijns and Atif Shahzad

Sensors 2022, 22(21), 8402; https://doi.org/10.3390/s22218402 - 1 Nov 2022

Cited by 11 | Viewed by 4332

Abstract

The wireless monitoring of key physiological parameters such as heart rate, respiratory rate, temperature, and pressure can aid in preventive healthcare, early diagnosis, and patient-tailored treatment. In wireless implantable sensors, the distance between the sensor and the reader device is prone to be influenced by the operating frequency, as well as by the medium between the sensor and the reader. This manuscript presents an ex vivo investigation of the wireless linkage between an implantable sensor and an external reader for medical applications. The sensor was designed and fabricated using a cost-effective and accessible fabrication process. The sensor is composed of a circular planar inductor (L) and a circular planar capacitor (C) to form an inductor–capacitor (LC) resonance tank circuit. The reader system comprises a readout coil and data acquisition instrumentation. To investigate the effect of biological medium on wireless linkage, the readout distance between the sensor and the readout coil was examined independently for porcine and ovine tissues. In the bench model, to mimic the bio-environment for the investigation, skin, muscle, and fat tissues were used. The relative magnitude of the reflection coefficient (

S_{11}

) at the readout coil was used as a metric to benchmark wireless linkage. A readable linkage signal was observed on the readout coil when the sensor was held up to 2.5 cm under layers of skin, muscle, and fat tissue. To increase the remote readout distance of the LC sensor, the effect of the repeater coil was also investigated. The experimental results showed that the magnitude of the reflection coefficient signal was increased 3–3.5 times in the presence of the repeater coil, thereby increasing the signal-to-noise ratio of the detected signal. Therefore, the repeater coil between the sensor and the readout coil allows a larger sensing range for a variety of applications in implanted or sealed fields. Full article

(This article belongs to the Special Issue Novel Implantable Sensors and Biomedical Applications)

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17 pages, 7097 KB

Open AccessArticle

Innovative IntraValvular Impedance Sensing Applied to Biological Heart Valve Prostheses: Design and In Vitro Evaluation

by Camilla Gironi, Laura Cercenelli, Barbara Bortolani, Nicolas Emiliani, Lorenzo Tartarini and Emanuela Marcelli

Sensors 2022, 22(21), 8297; https://doi.org/10.3390/s22218297 - 29 Oct 2022

Cited by 4 | Viewed by 2854

Abstract

Subclinical valve thrombosis in heart valve prostheses is characterized by the progressive reduction in leaflet motion detectable with advanced imaging diagnostics. However, without routine imaging surveillance, this subclinical thrombosis may be underdiagnosed. We recently proposed the novel concept of a sensorized heart valve prosthesis based on electrical impedance measurement (IntraValvular Impedance, IVI) using miniaturized electrodes embedded in the valve structure to generate a local electric field that is altered by the cyclic movement of the leaflets. In this study, we investigated the feasibility of the novel IVI-sensing concept applied to biological heart valves (BHVs). Three proof-of-concept prototypes of sensorized BHVs were assembled with different size, geometry and positioning of the electrodes to identify the optimal IVI-measurement configuration. Each prototype was tested in vitro on a hydrodynamic heart valve assessment platform. IVI signal was closely related to the electrodes’ positioning in the valve structure and showed greater sensitivity in the prototype with small electrodes embedded in the valve commissures. The novel concept of IVI sensing is feasible on BHVs and has great potential for monitoring the valve condition after implant, allowing for early detection of subclinical valve thrombosis and timely selection of an appropriate anticoagulation therapy. Full article

(This article belongs to the Special Issue Novel Implantable Sensors and Biomedical Applications)

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