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Master'sHealth, Biomechanics, Wearables & Operational Safety Applications open

Master’s in Wearables, Biosensors & Data Engineering

Online master's programme of 1,900 hours in wearables, biosensors and data engineering: 12 modules covering the wearables ecosystem, sensors, embedded hardware, connectivity, physiological signal processing, analytics and machine learning, cloud data platforms, privacy and cybersecurity, validation, medical wearable regulation and user-centred design, with Regulation (EU) 2017/745, GDPR and Python, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Select suitable sensors to measure physiological variables in wearables.

02

Design low-power hardware and connectivity architectures.

03

Process physiological signals and extract reliable indicators.

04

Apply machine learning to wearable data with rigorous validation.

05

Build interoperable data platforms based on HL7 FHIR.

06

Ensure the privacy and cybersecurity of health data.

07

Define the regulatory strategy for a medical wearable under the MDR.

Profile

Who it's for

  • Wearable engineers developing consumer and medical devices.
  • Health data scientists.
  • Biomedical firmware developers.
  • Digital clinical validation engineers.
  • Health data privacy specialists.
  • Digital health product managers.
  • Engineering or science graduates looking to enter digital health.
Standards & tools

What you'll work with

  • Regulation (EU) 2017/745 (medical devices regulation (MDR))
  • Regulation (EU) 2016/679 (GDPR) (protection of personal data)
  • ISO 14971 (risk management for medical devices)
  • IEC 62304 (medical device software life cycle)
  • IEC 60601-1-11 (medical equipment for the home healthcare environment)
  • ISO/IEEE 11073 (personal health device communication)
  • HL7 FHIR R4 (health data interoperability standard)
  • IEC 81001-5-1 (cybersecurity of health software)
  • Python with pandas and scikit-learn
  • MATLAB
  • Nordic nRF Connect SDK
  • InfluxDB
  • Edge Impulse
Syllabus

12 modules · 1900 h

  1. 1.1Classification by body location
  2. 1.2Consumer and medical-grade wearables
  3. 1.3The data value chain
  4. 1.4Architecture from sensor to cloud
  5. 1.5Business and reimbursement models
  6. 1.6Use cases in health and sport
  7. 1.7Barriers to adoption and adherence
  8. 1.8Trends in rings and patches
  9. 1.9Wearables in decentralised clinical trials
  10. 1.10Case study: analysis of a commercial wearable
  1. 2.1MEMS accelerometers and gyroscopes
  2. 2.2Wrist-worn photoplethysmography (PPG)
  3. 2.3Electrocardiography with dry electrodes
  4. 2.4Bioimpedance and electrodermal activity
  5. 2.5Skin temperature and thermal sensors
  6. 2.6Chemical sensors in sweat
  7. 2.7Barometers and magnetometers
  8. 2.8Noise, drift and calibration
  9. 2.9Eye movement sensors and portable EEG
  10. 2.10Lab: characterisation of a PPG sensor
  1. 3.1Low-power ARM Cortex-M microcontrollers
  2. 3.2Analogue front ends for biopotentials
  3. 3.3Analogue-to-digital conversion and sampling
  4. 3.4Power and battery management
  5. 3.5Flexible PCB design
  6. 3.6Encapsulation and IP sealing
  7. 3.7Electromagnetic compatibility under IEC 60601-1-2
  8. 3.8Biocompatibility of skin-contact materials
  9. 3.9Selection of batteries and wireless chargers
  10. 3.10Practical: prototype with nRF52 and an inertial sensor
  1. 4.1Bluetooth Low Energy and GATT profiles
  2. 4.2IEEE 11073 protocols
  3. 4.3Wi-Fi, NFC and LPWAN
  4. 4.4Time synchronisation of data
  5. 4.5On-device compression and processing
  6. 4.6Mobile gateways and apps
  7. 4.7Reliability and packet loss
  8. 4.8Radio power consumption
  9. 4.9Bluetooth pairing security
  10. 4.10Workshop: BLE heart rate service
  1. 5.1FIR and IIR digital filtering
  2. 5.2Peak detection in ECG and PPG
  3. 5.3Time-domain and spectral HRV indices
  4. 5.4SpO2 estimation
  5. 5.5Motion artefact reduction
  6. 5.6Sensor fusion with Kalman filters
  7. 5.7Segmentation and time windows
  8. 5.8Feature extraction
  9. 5.9Step and cadence detection
  10. 5.10Practical: HRV from a PPG recording
  1. 6.1Human activity recognition
  2. 6.2Fall detection
  3. 6.3Sleep classification
  4. 6.4Time series models
  5. 6.5Convolutional and recurrent networks
  6. 6.6Validation with independent subjects
  7. 6.7Bias by skin tone and population
  8. 6.8Model explainability
  9. 6.9Anomaly detection in longitudinal trends
  10. 6.10Lab: activity classifier with IMU data
  1. 7.1Ingestion and storage of time series
  2. 7.2Streaming architectures
  3. 7.3Time series databases
  4. 7.4Interoperability with HL7 FHIR
  5. 7.5Open mHealth and data schemas
  6. 7.6Dashboards for clinicians and users
  7. 7.7Scalability and costs
  8. 7.8Apple HealthKit and Health Connect APIs
  9. 7.9Data governance and quality on platforms
  10. 7.10Practical: cloud ingestion pipeline
  1. 8.1GDPR and health data
  2. 8.2Consent and data minimisation
  3. 8.3Anonymisation and pseudonymisation
  4. 8.4On-device and in-transit encryption
  5. 8.5BLE vulnerabilities
  6. 8.6Security under IEC 81001-5-1
  7. 8.7Data protection impact assessment
  8. 8.8Security incident management
  9. 8.9Retention policies and user rights
  10. 8.10Workshop: impact assessment of a wearable
  1. 9.1Technical validation studies
  2. 9.2Bland–Altman analysis
  3. 9.3Verification, analytical and clinical validation studies
  4. 9.4Design of real-world studies
  5. 9.5Digital endpoints and biomarkers
  6. 9.6Handling missing data
  7. 9.7Reproducibility and reporting standards
  8. 9.8Evidence for payers
  9. 9.9Digital endpoints accepted by the EMA
  10. 9.10Practical: comparing a wearable against a Holter ECG
  1. 10.1Regulation (EU) 2017/745 and classification
  2. 10.2Rule 11 for software
  3. 10.3Software as a medical device
  4. 10.4Risk management under ISO 14971
  5. 10.5IEC 60601-1-11 for home use
  6. 10.6IEC 62304 software life cycle
  7. 10.7CE marking and notified bodies
  8. 10.8Post-market surveillance and PSUR
  9. 10.9FDA 510(k) and De Novo pathways
  10. 10.10Case study: classification of an ECG patch
  1. 11.1Usability and IEC 62366-1
  2. 11.2Interface design for small screens
  3. 11.3Ergonomics and comfort in prolonged use
  4. 11.4Adherence and behaviour change techniques
  5. 11.5Accessibility for older people
  6. 11.6Haptic feedback and notifications
  7. 11.7User studies
  8. 11.8Inclusive algorithm design
  9. 11.9Packaging design and user onboarding
  10. 11.10Workshop: interface prototype for a wristband
  1. 12.1Definition of the use case and user
  2. 12.2Requirements specification
  3. 12.3Hardware design or sensor selection
  4. 12.4Development of the data pipeline
  5. 12.5Analytical model and validation
  6. 12.6Privacy and security analysis
  7. 12.7Product regulatory strategy
  8. 12.8User evaluation plan
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: you progress at your own pace within the intake, so it is compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python with pandas and scikit-learn, MATLAB, Nordic nRF Connect SDK, InfluxDB and Edge Impulse, in free, academic or evaluation versions. Practical work is based on real procedures for validating wearables against reference equipment, real-world data collection with volunteers and prototyping devices with BLE connectivity. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Regulation (EU) 2017/745, Regulation (EU) 2016/679 (GDPR), ISO 14971, IEC 62304, IEC 60601-1-11, ISO/IEEE 11073, HL7 FHIR R4, IEC 81001-5-1.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Regulation (EU) 2017/745, GDPR and ISO 14971 applied to real cases, not as a theoretical annex.
Industry software:Python with pandas and scikit-learn, MATLAB, Nordic nRF Connect SDK, InfluxDB and Edge Impulse, in free, academic or evaluation versions.
Master's Thesis with defence:A complete wearable product concept, from use case and requirements to hardware or sensor selection, data pipeline, validated analytical model, privacy analysis and regulatory strategy, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Use case and requirements
Define the use case, target user and requirements specification of the wearable.
Hardware and sensors
Design the hardware or select sensors and prototype BLE connectivity.
Data pipeline and model
Develop the data pipeline and a validated analytical model using physiological signals.
Privacy and regulation
Analyse privacy and security and define the regulatory strategy under the MDR.
Master's Thesis
Present the product with its user evaluation plan, defended before a panel.
Career opportunities

Where this takes you

Wearable engineerHealth data scientistBiomedical firmware developerDigital clinical validation engineerHealth data privacy specialistDigital health product manager
Admissions & scholarships

Requirements

  • A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • An internet connection and a computer capable of running the programme's software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Python with pandas and scikit-learn, MATLAB, Nordic nRF Connect SDK, InfluxDB and Edge Impulse, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the programme's software, and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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