Master’s in Wearables and Biosignals Engineering
Online master's programme of 1,900 hours in wearables and biosignals engineering, structured in 12 modules covering biosignal physiology, electrodes and sensors, acquisition electronics, biosignal processing, wearable hardware, firmware and connectivity, on-device intelligent algorithms, health data platforms, clinical validation, regulation and user-centred design, with an assigned technical tutor.
Master’s in Wearables and Biosignals Engineering — studying from New Zealand
Electronics and telecommunications are significant New Zealand industries, with Chorus operating the national fibre network and Spark, One NZ and 2degrees providing mobile services. Embedded systems and electronics design are strong in Auckland, Christchurch and Wellington, serving agricultural technology, medical devices and industrial automation. Engineers in this field work for telecommunications providers, electronics manufacturers and research groups, and the Radio Spectrum Management team within the Ministry of Business, Innovation and Employment oversees spectrum use.
Fees for New Zealand: NZ$19,700 (New Zealand dollar), with instalments and employer sponsorship available.
Relevant bodies in New Zealand
Typical roles
Questions from New Zealand
How do New Zealand bodies recognise electronics and telecom engineers?
Engineering New Zealand is the body for engineering registration, including Chartered Professional Engineer status. Telecommunications is overseen by the Commerce Commission and the Ministry of Business, Innovation and Employment, and radio spectrum is managed by MBIE. SEIUM's qualifications are private and not accredited by a national body, so employers decide their weight.
Which New Zealand employers hire embedded systems and telecom engineers?
Employers include telecommunications operators, electronics manufacturers, device makers, defence and aerospace suppliers, and research institutions. Hiring often prioritises practical embedded work, firmware projects and certifications relevant to the role. A private online qualification may help your application, but it does not guarantee an offer.
Is this manageable online from New Zealand, and what study load should I expect?
The programmes are online and in English, so no student visa is needed to study from New Zealand. Each has an assigned technical tutor and a final project. Specialist courses are 300 hours, postgraduate diplomas 800 and master's programmes 1,900. Fees can be paid in instalments, and employer sponsorship is possible, so check funding options first.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Understand the physiological origin and characteristics of biosignals.
Select suitable electrodes and sensors for each biosignal.
Design low-noise analogue acquisition front-ends.
Implement real-time biosignal processing algorithms.
Design the hardware and firmware of low-power wearables.
Validate a wearable's measurements technically and clinically.
Develop a complete biosignal wearable with its regulatory strategy.
Who it's for
- Wearables engineers
- Biomedical engineers
- Biosignal processing engineers
- Low-power firmware developers
- Clinical validation engineers
- Digital health engineers
- Engineering or science graduates looking to move into health technology
What you'll work with
- IEC 60601-1 (safety of medical electrical equipment)
- IEC 60601-2-47 (ambulatory electrocardiographic systems)
- ISO 80601-2-61 (pulse oximeter equipment)
- Regulation (EU) 2017/745 (Medical Device Regulation)
- IEC 62304 (medical device software life cycle)
- ISO 10993-1 (biological evaluation of medical devices)
- IEC 62366-1 (usability engineering for medical devices)
- Regulation (EU) 2016/679 (General Data Protection Regulation)
- MATLAB Signal Processing Toolbox
- Python with NeuroKit2
- Edge Impulse
- nRF Connect SDK
- KiCad
12 modules · 1900 h
1.1Bioelectric and membrane potentials1.2The electrocardiogram and its origin1.3Surface electromyography1.4Electroencephalography signals1.5Principles of photoplethysmography1.6Electrodermal activity and sweating1.7Respiration and movement1.8Body temperature measurement1.9Amplitude and frequency characteristics1.10Case study: acquisition requirements for five biosignals
2.1The electrode-skin interface2.2Wet, dry and textile electrodes2.3PPG optical sensors2.4Accelerometers and inertial units2.5Bioimpedance sensors2.6Chemical sensors in sweat2.7Skin temperature sensors2.8Motion artefacts2.9Placement and ergonomics2.10Lab: comparison of dry and wet electrodes
3.1Instrumentation amplifiers3.2Common-mode rejection3.3Right-leg drive circuit3.4Integrated analogue front-ends3.5Sigma-delta analogue-to-digital converters3.6Analogue input filtering3.7Low-level noise3.8Patient protection and safety3.9Contact impedance measurement3.10Practical exercise: ECG front-end with the ADS1292
4.1Digital filtering of biosignals4.2Mains interference removal4.3QRS complex detection4.4Heart rate variability4.5SpO2 estimation4.6EEG spectral analysis4.7EMG processing4.8Motion artefact compensation4.9Real-time on-device processing4.10Lab: Pan-Tompkins algorithm in Python
5.1Architecture of a wearable5.2Microcontrollers for wearables5.3Power and battery management5.4Wireless charging of the wearable5.5Low-power displays5.6Flexible and stretchable electronics5.7Antennas in body-worn devices5.8Mechanical design and materials5.9Sealing and water resistance5.10Workshop: power budget for a wristband
6.1Embedded operating systems6.2Bluetooth LE and health profiles6.3Synchronisation with the phone6.4Local data storage6.5Firmware updates6.6On-device data security6.7Touch and haptic user interfaces6.8Sensor management in firmware6.9Firmware testing6.10Practical exercise: BLE heart rate profile
7.1Extraction of physiological features7.2Arrhythmia detection7.3Activity recognition7.4Sleep monitoring7.5Stress estimation7.6TinyML on microcontrollers7.7Validating algorithms with databases7.8Bias and user diversity7.9Clinical explainability of models7.10Lab: activity recognition with TinyML
8.1Phone-to-cloud architecture8.2HL7 FHIR interoperability8.3Apple HealthKit and Health Connect platforms8.4Dashboards for clinicians8.5Remote patient monitoring8.6Population data analysis8.7Secure storage of clinical data8.8Integration with electronic health records8.9Platform scalability8.10Practical exercise: sending FHIR observations from an app
9.1Technical validation of measurements9.2Comparison with reference equipment9.3Bland-Altman analysis9.4Designing user studies9.5Ethics and informed consent9.6Clinical investigation with medical devices9.7Diagnostic performance metrics9.8Validation in real-world conditions9.9Publishing results9.10Case study: validation of a wrist-worn pulse oximeter
10.1Wellness versus medical device10.2Applying the MDR to wearables10.3Algorithms as medical software10.4Electrical safety requirements10.5Biocompatibility of skin-contact materials10.6Requirements for pulse oximeters10.7Requirements for electrocardiographs10.8FDA regulation10.9Health data protection10.10Workshop: regulatory strategy for an ECG patch
11.1Usability and human factors11.2Comfort and prolonged wear11.3Adherence and motivation11.4Accessibility for diverse users11.5Design for older people11.6Feedback and data visualisation11.7Interaction design on small screens11.8Evaluation with users11.9Prototype iteration11.10Practical exercise: usability evaluation of a prototype
12.1Definition of the wearable and its intended purpose12.2Selection of biosignals and sensors12.3Front-end and hardware design12.4Firmware and connectivity12.5Processing algorithms12.6Technical validation of the prototype12.7Regulatory and usability analysis12.8Conclusions on the wearable12.9Thesis report12.10Master's Thesis: defence before a panel
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 you can combine it with your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB Signal Processing Toolbox, Python with NeuroKit2, Edge Impulse, nRF Connect SDK and KiCad, in free, academic or evaluation versions. Practical work is based on real procedures: ECG and PPG acquisition with integrated analogue front-ends, algorithm validation with public physiological databases and comparison studies against reference equipment. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: IEC 60601-1, IEC 60601-2-47, ISO 80601-2-61, Regulation (EU) 2017/745, IEC 62304, ISO 10993-1, IEC 62366-1, Regulation (EU) 2016/679.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- 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.
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 MATLAB Signal Processing Toolbox, Python with NeuroKit2, Edge Impulse, nRF Connect SDK and KiCad, 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 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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