Open Positions

Fatigue in sports is a sustained feeling of exhaustion and decreased capacity to complete physical exercise. Fatigue affects athletic performance, training efficiency, and is linked to increased injury occurrence. Estimation of fatigue is important not only to professional sportsmen looking to be on the edge of their abilities, but also for hobby athletes trying to avoid overexertion and sports-related accidents. Fatigue is associated with decrease not only in physical, but also cognitive performance. However, despite a number of physiological, biochemical, psychological and performance parameters proposed as fatigue assessment metrics, there are no devices analyzing several biomarkers to provide reliable fatigue assessment. Gold standard fatigue assessment procedures are either subjective (self-reported scales), distracting (performance in standardized exercises), or invasive (blood tests). The present project aims to develop a wearable device that would be able to estimate user’s fatigue and exertion using non-invasive measurements.

Goals • Develop sensor for non-invasive measurements of fatigue biomarkers • Validate and test the produced sensors in clinical setting with runners • Write a scientific project report Tasks • Literature review (10%) • Wearable device development (40%) • Real-life and calibration data collection and analysis (40%) • Reporting and presentation (10%) Your Profile • Background in Chemistry, Chemical Engineering, Materials Science, or related fields. • Knowledge of electrochemistry and/or chemical sensors is highly desirable • Independent worker with critical thinking and problem-solving skills

Prof Dr Carlo Menon, Dr. Alexander Shokurov will supervise the student and the research will be performed at ETH Zurich’s Biomedical and Mobile Health Technology research group (www.bmht.ethz.ch) in the Balgrist Campus in Zurich, Switzerland. Contact: Dr. Alexander Shokurov (alexander.shokurov@hest.ethz.ch). To apply, use the button below to tell us why you want to do this project ("motivation"); attach a CV with your current program of study, your grades and any other info you deem relevant. Please include length of time that your project will occupy and a potential starting date.

One of the most important biomedical parameters that can be measured by wearable devices is human body movement. This movement is related limb motion, breathing, speech, heart rate etc. Accurate measurement of these movements requires precise strain and pressure sensors with high sensitivity. Mechanophores are materials capable of changing physical properties (most often color) in response to local mechanical stimuli, like strain or stress. This is achieved by insertion of stress-responsive molecular units into the polymeric backbone. In the present project, mechanophoric elastomeric materials would be synthesized to study if the mechanophoric action can enhance sensitivity of strain sensors for wearable applications. First, new approaches for facile upscaled production of mechanophoric elastomers will be developed through synthesis of small molecule functional cross-linkers, and then the developed mechanophore polymers will be applied as active material for electronic strain sensors. We intend to carry out an exciting multidisciplinary study of the organic synthesis strategies, physico-chemical behavior of the new materials, and their practical applications in wearable sensors.

Goals • Synthesize functional mechanophoric cross-linker small molecules; • Incorporate synthesized mechanophores into elastomers and verify mechanophoric action upon linear strain; • Validate the effect of mechanophoric units on strain sensing; • Write a scientific project report; Tasks • Literature review (10%) • Synthesis of functional cross-linkers and elastomers (40%) • Validation of the effect of mechanophoric behavior on strain sensor properties (40%) • Reporting and presentation (10%) Your Profile • Background in Chemistry, Chemical Engineering, Materials Science, or related fields • Independent worker with critical thinking and problem-solving skills

Contact Details Prof Dr Carlo Menon and Dr. Alexander Shokurov will supervise the student and the research will be performed at ETH Zurich’s Biomedical and Mobile Health Technology research group (www.bmht.ethz.ch) in the Balgrist Campus in Zurich, Switzerland. To apply, use the button below to tell us why you want to do this project ("motivation"); attach a CV with your current program of study, your grades and any other info you deem relevant.

Proper iodine intake is crucial for optimal health, impacting everything from metabolism to cognitive development in utero, as well as in both children and grown adults. However, assessing iodine levels can be challenging, particularly in point-of-care or remote settings. This project seeks to address this gap by developing a disposable sensor specifically designed to measure urinary iodine levels, providing rapid and accurate insights into patient nutrition. The proposed project aims to utilize electrochemical detection and synthesis techniques involving carbon- and silver- based nanomaterials to produce and validate sensors for aqueous iodine. This work will include the design and fabrication of the sensor, calibration for iodine detection in water, synthetic urine, and then in real samples. In case of high success, portable electronics capable of driving the developed detection protocol will be also elaborated and validated.

Goals • Develop the method for the graphene electrode formation and deposition of nanosilver • Validate and test the sensor’s accuracy and reliability in measuring urinary iodine levels in synthetic samples of progressive complexity and real samples • Develop electronic suite capable of driving the developed sensors • Write a scientific project report Tasks • Literature review (10%) • Optimize the deposition technique and fabrication process for the sensor element (50%) • Validation of the sensor in synthetic and real life samples (20%) • Development of signal read-out electronic components (10%) • Data collection and analysis, reporting and presentation (10%) Your Profile • Background in Biomedical technology, Chemistry, Materials Science or related fields • Prior experience with chemicals and standard chemical laboratory equipment • Knowledge of electrochemistry and/or electrochemical sensing is highly desirable • Independent worker with critical thinking and problem-solving skills

Prof Dr Carlo Menon and Dr. Alexander Shokurov will supervise the student and the research will be performed at ETH Zurich’s Biomedical and Mobile Health Technology research group (www.bmht.ethz.ch) in the Balgrist Campus in Zurich, Switzerland. When applying, please describe your motivation and your background in the cover letter, and attach your CV and all the transcripts of the previous studies.

In this project, we will characterize and evaluate the use of wearable piezoelectric sensors to monitor blood pressure using AI approaches. Data will be acquired using two sensors placed along a major artery, which will detect the pulse wave. From this, key physiological features will be extracted such as arterial wall displacement, diameter variation, and blood flow velocity, and pulse transit time which may be used in computational models of blood pressure estimation (e.g., Bramwell–Hill model). Machine learning and deep learning approaches will be used for blood pressure estimation and compared to established computational models. Other physiological parameters will be extracted as well, such as heart rate and respiration rate. This project will offer valuable experience in data processing, machine learning, and working with real-world health data for sport or health related monitoring.

•Implement physiological feature extraction algorithms (vessel wall segmentation, tracking) •Implement mathematical, ML and DL models for blood pressure estimation Tasks • Literature review (20%) • Data processing and key physiological feature extraction (20%) • Implement and calibrate mathematical models for blood pressure estimation (10%) • Implement ML and DL models for blood pressure and other physiological parameter estimation (30%) • Test and evaluation of models and comparison with gold standard (10%) • Report and presentation (10%)

Prof Dr Carlo Menon, Dr. Corin Otesteanu (corin.otesteanu@hest.ethz.ch) will supervise the student and the research will be performed at the Biomedical and Mobile Health Technology lab (www.bmht.ethz.ch) at ETH Zurich, Switzerland. To apply, use the button below to tell us why you want to do this project ("motivation") along with the type of project you are applying for (e.g., master project or thesis) and your timeline for the project; attach a mini CV with your current program of study, your grades and any other info you deem relevant--maybe the name and phone number of a postdoc or a professor willing to be your reference; and make any further comments ("additional remarks").

Sport monitoring has many benefits including injury prevention and performance optimization. Current methods for activity monitoring in sports mostly use camera-based motion tracking or the use of inertial measurement unit-based systems that are limited in their measurement space or are obtrusive to the activities of the user. Smart clothing offers a solution that makes monitoring of biomechanics during individual and team sports possible in different conditions. We have made textile-based wearable technology for unobtrusive monitoring of movement and completed a study in which we acquired real-world data from our technology during sport activities. Using machine learning methods, this project aims to extract useful information from our data such as activity type and fatigue. This project will offer valuable experience in data processing, machine learning, working with real-world data, and cutting-edge wearable technologies. **Your Profile** - Background in electrical engineering, computer engineering, mechanical engineering, or related fields - Prior experience with data analysis (signal processing, machine learning algorithms, or similar) and machine learning - Independent worker with critical thinking skills and problem solving skills

- Investigate methods to classify different sport-specific activities or predict biomechanical measures based on real-world data. - Develop and test models to assess the level of fatigue during sports. - Improve the model and compare it with other measurement methods.

Prof Dr Carlo Menon and Dr Chakaveh Ahmadizadeh will supervise the student and the research will be performed at the Biomedical and Mobile Health Technology lab (www.bmht.ethz.ch) at ETH Zurich, Switzerland. To apply, use the button below to tell us why you want to do this project ("motivation") along with the type of project you are applying for (e.g., master project or thesis) and your timeline for the project; attach a mini CV with your current program of study, your grades and any other info you deem relevant--maybe the name and phone number of a postdoc or a professor willing to be your reference; and make any further comments ("additional remarks").

Movement monitoring is a rapidly growing field with many applications in healthcare and fitness. Current methods for activity monitoring mostly use camera-based motion tracking or the inertial measurement unit-based systems that are limited in their measurement space or are obtrusive to the activities of the user. Smart clothing offers a solution that makes monitoring of biomechanics during individual and team sports possible in different conditions. We develop textile-based wearable technologies for unobtrusive monitoring of movement based on capacitive, resistive, or inductive sensors. Signals from these sensors are often acquired using benchtop LCR meters. However, it is crucial for our wearable applications to replace such solutions with portable and unobtrusive data acquisition methods. This project aims to design, develop, and test portable data acquisition systems for data recording from multiple capacitive sensors. This project will offer valuable experience in electronics, prototyping, embedded software, and cutting-edge wearable technologies. **Your Profile** - Background in electrical engineering, computer engineering, mechanical engineering, or related fields - Prior experience with electronics and prototyping (printed circuit board, electronic circuit testing and troubleshooting, or similar) - Independent worker with critical thinking skills and problem solving skills

- Identify areas for improvement in current data acquisition solutions. - Develop and test electronics and software for signal recording from capacitive sensors. - Implement developed solutions into textile-based wearable technologies for movement monitoring.

Prof Dr Carlo Menon and Dr Chakaveh Ahmadizadeh will supervise the student and the research will be performed at the Biomedical and Mobile Health Technology lab (www.bmht.ethz.ch) at ETH Zurich, Switzerland. To apply, use the button below to tell us why you want to do this project ("motivation") along with the type of project you are applying for (e.g., master project or thesis) and your timeline for the project; attach a mini CV with your current program of study, your grades and any other info you deem relevant--maybe the name and phone number of a postdoc or a professor willing to be your reference; and make any further comments ("additional remarks").

Restoring hand function is a key challenge for individuals with limb impairments or amputations. High-density surface electromyography (HD-sEMG) offers a non-invasive method to decode muscle activity and translate it into movement intent. By using HD-sEMG, high-resolution muscle activity can be mapped to classify various hand gestures. This project leverages the Hyser dataset, which contains 256-channel HD-sEMG recordings from 20 subjects performing 34 different hand gestures. These signals are acquired at a high sampling rate and include synchronized force measurements, making the dataset suitable for machine learning applications. The objective is to develop a gesture recognition system that enhances prosthetic hand control, enabling future amputees to intuitively control robotic limbs. Tasks: • Literature review (10%) • Data preprocessing and feature extraction (20%) • Develop machine learning algorithms for gesture classification (40%) • Evaluate performance and create a summary report (20%) • Reporting and presentation (10%) Your Profile: - Background in Biomedical Engineering, Electrical Engineering, Computer Science, Health Science and Technology or related fields. - Experience with data analysis and signal processing (using MATLAB or Python). - Familiarity with machine learning and deep learning frameworks is desirable. - Independent, motivated, and capable of critical thinking and problem-solving.

Goals: - Develop a machine learning model for classifying 34 hand gestures based on HD-sEMG signals. - Implement preprocessing and feature extraction techniques to improve signal quality. - Analyze and compare various machine learning algorithms, including deep learning approaches. - Provide a summary of gesture classification accuracy and system performance.

This research stems from a collaboration between Prof. Dr. Carlo Menon (ETH Zurich) and Prof. Dr. Moe Elgendi (Khalifa University, UAE). The candidate will be primarily supervised by Prof. Elgendi remotely. For inquiries or applications, please contact both: • Moe Elgendi – Mohamed.Elgendi@ku.ac.ae • Carlo Menon – carlo.menon@hest.ethz.ch

Gait and posture impairments significantly affect quality of life, increasing the risk of falls and related injuries. Robotic walkers and other assistive devices equipped with multimodal sensors offer an innovative approach to rehabilitation by providing real-time feedback and continuous monitoring. This project leverages a “multi-camera and multimodal dataset for posture and gait analysis,” which comprises data from 14 healthy participants walking at three different speeds across three distinct scenarios. The dataset includes synchronized depth images, inertial motion-capture recordings, and three-dimensional joint-angle measurements, providing a robust foundation for training machine-learning models to detect gait irregularities and postural deviations. DATASET “A multi-camera and multimodal dataset for posture and gait analysis” Palermo M., Mendes Lopes J., André J., Cerqueira J., Santos C. PhysioNet (2021) https://doi.org/10.13026/fyxw-n38 The objective is to develop algorithms that analyze joint kinematics, segment orientations, and gait cycles in order to identify anomalies and advance the design of next-generation assistive rehabilitation systems. Tasks: • Literature review on gait and posture analysis techniques (10%) • Data preprocessing and feature extraction from multimodal sensor data (20%) • Develop machine learning models for gait classification and posture analysis (40%) • Evaluate model performance and generate visualizations of gait/posture patterns (20%) • Reporting and presentation of results (10%) Your Profile: • Background in Biomedical Engineering, Electrical Engineering, Mechanical Engineering, Computer Science, Health Science and Technology, or related fields. • Experience with data analysis, machine learning, and signal processing (Python or MATLAB). • Familiarity with motion capture systems and biomechanics is desirable. • Ability to work independently with a problem-solving mindset.

• Develop machine learning and deep learning models for posture and gait analysis using multimodal data. • Create algorithms to track and classify gait patterns across different walking speeds and scenarios. • Extract meaningful biomechanical features to evaluate postural stability and movement quality. • Generate visual feedback and reports for gait and posture metrics.

Contact Details: This research stems from a collaboration between Prof. Dr. Carlo Menon (ETH Zurich) and Prof. Dr. Moe Elgendi (Khalifa University, UAE). The candidate will be primarily supervised by Prof. Elgendi remotely. For inquiries or applications, please contact both: • Moe Elgendi – Mohamed.Elgendi@ku.ac.ae • Carlo Menon – carlo.menon@hest.ethz.ch

Electrocardiograms (ECGs) are fundamental for diagnosing cardiovascular disease, yet paper‐based ECGs remain ubiquitous in many under‐resourced healthcare settings. This project develops AI‐driven models to detect and classify cardiovascular abnormalities directly from scanned paper ECGs—eliminating the need for time‐consuming manual digitization. We will leverage the recently accessible paper‐ECG scans in the PTB-XL dataset, which comprises 10-second, 12-lead ECG recordings from 21,837 clinical examinations. Although PTB-XL has been analyzed in multiple studies (e.g., Wagner et al., 2020; Strodthoff et al., 2020), the full-resolution paper‐ECG images were only recently made available under a restricted‐data agreement. Before any data access or model development, we will obtain formal approval from our institutional ethics board and fully comply with PTB-XL’s data‐use terms. By applying convolutional neural networks and transformer‐based architectures directly to these paper ECG images, we aim to automatically identify arrhythmias, ischemic patterns, and other key cardiac events. This approach promises to accelerate diagnosis, improve record preservation, and expand access to quality cardiovascular assessment in regions lacking digital ECG infrastructure. Tasks: • Literature review on ECG classification techniques and image-based medical diagnostics (10%) • Curate and preprocess ECG images from public datasets (e.g., PTB-XL, PhysioNet archives) (15%) • Develop data augmentation techniques for ECG images (rotation, noise reduction, distortion correction) (10%) • Build and train deep learning models (CNNs, Vision Transformers) for image classification (40%) • Evaluate models and fine-tune hyperparameters (15%) • Prepare final project report and present findings (10%) Your Profile: • Background in Biomedical Engineering, Electrical Engineering, Computer Science, Health Science and Technology, or related fields. • Experience with machine learning, image processing, and deep learning (using Python, TensorFlow, or PyTorch). • Interest in cardiovascular health and global health technologies. • Ability to work independently with strong analytical skills.

• Develop and train deep learning models to classify paper-based ECGs directly into categories of cardiovascular diseases. • Create preprocessing pipelines to enhance image quality, handle skewed or noisy ECG scans, and extract relevant waveforms. • Investigate transfer learning approaches using existing ECG datasets to improve model generalization. • Evaluate model performance against benchmark datasets and real-world clinical ECG scans.

Contact Details: This research stems from a collaboration between Prof. Dr. Carlo Menon (ETH Zurich) and Prof. Dr. Moe Elgendi (Khalifa University, UAE). The candidate will be primarily supervised by Prof. Elgendi remotely. For inquiries or applications, please contact both: • Moe Elgendi – Mohamed.Elgendi@ku.ac.ae • Carlo Menon – carlo.menon@hest.ethz.ch

The project aims to evaluate the effectiveness of wearable technology in identifying anxiety in individuals. This research makes use of publicly available datasets of individuals watching anxiety inducing videos . Data gathered from these devices include continuous measurements over 1 hour of respiration, ECG and electrodermal activity data. By developing machine learning methods, the aim is to continuously detect anxiety levels in an individualized manner. This project will offer valuable experience in data processing, machine learning, and working with real-world health data. Moreover, it has the potential to meaningfully impact the users wellbeing.

Goals • Quantitatively analyze the effect of anxiety inducing videos on several biomarkers extracted from wearable data • Investigate different machine learning models to classify anxiety levels (low, normal, high) from wearable device data. Tasks • Literature review (10%) • Data analysis (normalization techniques) (20%) • Design and implement shallow machine learning model for anxiety level classification (20%) • Design and implement a recurrent neural network for anxiety level classification (20%) • Test and evaluation of models (10%) • Report and presentation (20%)

Your Profile • Background in Electrical Engineering, Computer Science, Mechanical Engineering, or related fields • Prior experience with data analysis (signal processing, machine learning algorithms, or similar) • Independent worker with critical thinking and problem solving skills Contact Details Prof Dr Carlo Menon and Dr. Corin Otesteanu (corin.otesteanu@hest.ethz.ch) will supervise the student and the research will be performed at the Biomedical and Mobile Health Technology lab (www.bmht.ethz.ch) at ETH Zurich, Switzerland. To apply, use the button below to tell us why you want to do this project ("motivation") along with the type of project you are applying for (e.g., master project or thesis) and your timeline for the project; attach a mini CV with your current program of study, your grades and any other info you deem relevant--maybe the name and phone number of a postdoc or a professor willing to be your reference; and make any further comments ("additional remarks").

Wearable triboelectric nanogenerators (TENGs) are emerging as energy-harvesting and self-powered sensors that can transduce biomechanical motion into electrical signals. In this project, we will characterize and evaluate TENGs for real-time joint motion estimation and fatigue analysis. Controlled experiments will be conducted during repetitive tasks using an optical motion capture system or inertial measurement unit (IMU) as gold standard. The dataset will consist of TENG voltage outputs synchronized with 3D kinematic data from the reference systems. This project will offer valuable experience in data processing, machine learning, and working with real-world health data.

Goals • Characterize the TENG sensor signal for different joint movements • Implement ML and DL models for joint angle reconstruction • Benchmarking shallow, deep, and transformers based models for performance Tasks • Literature review (10%) • Characterize the TENG sensor signal in controlled settings (10%) • Implement ML and DL models for angle and angular velocity reconstruction (20%) • Data collection from 10-15 participants during a standardized protocol (30%) • Test and evaluation of models and comparison with gold standard (20%) • Report and presentation (10%)

Prof Dr Carlo Menon, Dr. Corin Otesteanu (corin.otesteanu@hest.ethz.ch) and Dr. Satyiaranjan Bairagi (satyaranjan.bairagi@hest.ethz.ch) will jointly supervise the student and the research will be performed at the Biomedical and Mobile Health Technology lab (www.bmht.ethz.ch) at ETH Zurich, Switzerland. To apply, use the button below to tell us why you want to do this project ("motivation") along with the type of project you are applying for (e.g., master project or thesis) and your timeline for the project; attach a mini CV with your current program of study, your grades and any other info you deem relevant--maybe the name and phone number of a postdoc or a professor willing to be your reference; and make any further comments ("additional remarks").

Innovations using metamaterials have been made in recent years, offering promising approaches for creating stretchable and adaptable structures in wearable electronics. Innovative ways of integrating wearables into our daily life and maximizing the functionality of such structures require new fabrication methods that are both cost-effective and versatile. In the present project, a new approach utilizing 3D printed molds to create stretchable auxetic structures is being developed. Soft substrates will be patterned to form auxetic structures that can be integrated into textiles. Simultaneously, the sensing elements will be embedded into the soft substrate using an insert molding technique. The resulting sensors are designed for motion monitoring, making them suitable for integration into garments and other wearable platforms. The ability of these sensors to maintain performance under mechanical deformation makes them particularly advantageous for wearable health monitoring systems.

- Develop a method for the production of capacitive auxetic structures - Fabricate functional wearable sensors - Write a project report **Tasks** - Literature review (10%) - Optimization of the fabrication of capacitive sensors (50%) - Characterization of the produced sensors (30%) - Data collection, reporting and presentation (10%) **Your profile** - Background in Applied Physics, Mechanical Engineering, Materials Science, Electronics Engineering, Biomedical Engineering, Health Science or related fields is desirable but not mandatory. - Independent worker with critical thinking and problem-solving skills

Prof. Dr. Carlo Menon and Pierre Kateb will supervise the student and the research will be performed at ETH Zurich’s Biomedical and Mobile Health Technology research group (www.bmht.ethz.ch) in the Balgrist Campus in Zurich, Switzerland. To apply, use the button below to tell us why you want to do this project ("motivation"); attach a CV with your current program of study, your grades and any other info you deem relevant.

Kirigami is the Japanese art of cutting paper, similar to origami, which is the art of folding paper. Innovations using kirigami-inspired designs and metamaterials have been made in recent years, offering promising approaches for creating stretchable and adaptable structures in wearable electronics. Innovative ways of integrating wearables into our daily life and maximizing the functionality of such structures require new fabrication methods that are both cost-effective and versatile. In the present project, a new approach utilizing laser-cutting and 3D printing for creating kirigami-inspired antennas on flexible substrates is being developed. Soft substrates will be patterned to form kirigami structures that can be integrated into textiles. Simultaneously, the antenna element will be embedded into the soft substrate using an insert molding technique. The resulting kirigami antennas are designed for applications such as wireless communication and motion monitoring, making them suitable for integration into garments and other wearable platforms. The ability of these antennas to maintain performance under mechanical deformation makes them particularly advantageous for wearable health monitoring systems.

- Develop a method for the production of kirigami - Fabricate functional wearable antennas - Write a project report **Tasks** - Literature review (10%) - Optimization of the fabrication of kirigamis (50%) - Characterization of the produced kirigami (30%) - Data collection, reporting and presentation (10%) **Your profile** - Background in Applied Physics, Health Science, Mechanical Engineering, Materials Science, Electronics Engineering, Biomedical Engineering or related fields is desirable but not mandatory. - Independent worker with critical thinking and problem-solving skills

Prof Dr Carlo Menon, Dr. Muhammad Zada and Pierre Kateb will supervise the student and the research will be performed at ETH Zurich’s Biomedical and Mobile Health Technology research group (www.bmht.ethz.ch) in the Balgrist Campus in Zurich, Switzerland. To apply, use the button below to tell us why you want to do this project ("motivation"); attach a CV with your current program of study, your grades and any other info you deem relevant.

Wearable biosensing platforms are increasingly explored for continuous, noninvasive health monitoring. This project focuses on developing a wireless sensor system based on resonant metamaterials combined with soft, ion-selective interfaces that can extract biomarkers from skin-interfaced fluids. By leveraging changes in electromagnetic properties, the sensor can detect specific physiological signals without requiring batteries or complex electronics. Students will investigate flexible materials and resonator designs, gaining interdisciplinary experience across electronics, materials science, and biomedical sensing

• Experimental work on hydrogel synthesis and functionalization • Fabricate and integration on flexible substrate • Wireless measurement, calibration, and performance evaluation • Write a scientific project report

Prof Dr Carlo Menon, Dr. Muhammad Zada and Dr. Weifeng Yang will supervise the student, and the research will be performed at ETH Zurich’s Biomedical and Mobile Health Technology research group (www.bmht.ethz.ch) in the Balgrist Campus in Zurich, Switzerland.