TinyML in Healthcare: AI on Wearable Devices Training Course
TinyML involves embedding machine learning capabilities into wearable and medical devices that operate with low power and limited resources.
This instructor-led live training, available either online or onsite, is designed for intermediate-level professionals aiming to implement TinyML solutions for healthcare monitoring and diagnostic purposes.
Upon completion of this training, participants will be able to:
- Design and deploy TinyML models for processing health data in real-time.
- Collect, preprocess, and interpret biosensor data to derive AI-driven insights.
- Optimize models to run efficiently on wearable devices with constrained memory and power.
- Assess the clinical relevance, reliability, and safety of outputs generated by TinyML.
Course Format
- Lectures complemented by live demonstrations and interactive discussions.
- Practical exercises involving wearable device data and TinyML frameworks.
- Guided implementation exercises within a lab environment.
Customization Options
- For specialized training that aligns with specific healthcare devices or regulatory workflows, please reach out to us to tailor the program.
Course Outline
Foundations of TinyML in Healthcare
- Key characteristics of TinyML systems
- Specific constraints and requirements in healthcare
- Overview of wearable AI architectures
Biosignal Acquisition and Preprocessing
- Working with physiological sensors
- Techniques for noise reduction and filtering
- Feature extraction for medical time-series data
Developing TinyML Models for Wearables
- Selecting appropriate algorithms for physiological data
- Training models tailored for constrained environments
- Evaluating model performance on health datasets
Deploying Models on Wearable Devices
- Utilizing TensorFlow Lite Micro for on-device inference
- Integrating AI models into medical wearables
- Testing and validation on embedded hardware
Power and Memory Optimization
- Strategies to reduce computational load
- Optimizing data flow and memory usage
- Balancing accuracy with efficiency
Safety, Reliability, and Compliance
- Regulatory considerations for AI-enabled wearables
- Ensuring robustness and clinical usability
- Implementing fail-safe mechanisms and error handling
Case Studies and Healthcare Applications
- Wearable cardiac monitoring systems
- Activity recognition in rehabilitation contexts
- Continuous glucose and biometric tracking
Future Directions in Medical TinyML
- Multi-sensor fusion approaches
- Personalized health analytics
- Next-generation low-power AI chips
Summary and Next Steps
Requirements
- A solid understanding of fundamental machine learning concepts
- Prior experience with embedded or biomedical devices
- Familiarity with development using Python or C
Target Audience
- Healthcare professionals
- Biomedical engineers
- AI developers
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
TinyML in Healthcare: AI on Wearable Devices Training Course - Enquiry
Related Courses
Agentic AI in Healthcare
14 HoursAgentic AI refers to a methodology where AI systems independently plan, reason, and utilize tools to achieve specific objectives within set boundaries.
This instructor-led, live training (available online or onsite) targets intermediate-level healthcare and data teams looking to design, evaluate, and govern agentic AI solutions for clinical and operational applications.
Upon completing this training, participants will be equipped to:
- Articulate agentic AI concepts and constraints within healthcare settings.
- Construct secure agent workflows incorporating planning, memory, and tool integration.
- Develop retrieval-augmented agents that process clinical documents and knowledge bases.
- Assess, monitor, and govern agent behaviour using guardrails and human-in-the-loop controls.
Course Format
- Interactive lectures complemented by facilitated discussions.
- Guided labs and code walkthroughs conducted in a sandbox environment.
- Scenario-based exercises focusing on safety, evaluation, and governance.
Customization Options
- To request customized training for this course, please get in touch with us to arrange your session.
AI Agents for Healthcare and Diagnostics
14 HoursThis instructor-led, live training in Nigeria (online or onsite) is designed for intermediate to advanced healthcare professionals and AI developers who aim to implement AI-driven healthcare solutions.
By the end of this training, participants will be able to:
- Understand the role of AI agents in healthcare and diagnostics.
- Develop AI models for medical image analysis and predictive diagnostics.
- Integrate AI with electronic health records (EHR) and clinical workflows.
- Ensure compliance with healthcare regulations and ethical AI practices.
AI and AR/VR in Healthcare
14 HoursThis live, instructor-led training in Nigeria (online or on-site) is designed for intermediate-level healthcare professionals aiming to apply AI and AR/VR solutions for medical training, surgical simulations, and rehabilitation.
By the conclusion of this training, participants will be able to:
- Understand how AI enhances AR/VR experiences in healthcare.
- Use AR/VR for surgery simulations and medical training.
- Apply AR/VR tools in patient rehabilitation and therapy.
- Explore the ethical and privacy concerns in AI-enhanced medical tools.
AI for Healthcare using Google Colab
14 HoursThis live, instructor-led training in Nigeria (online or onsite) targets intermediate data scientists and healthcare professionals eager to utilize AI for advanced medical applications via Google Colab.
By the conclusion of this training, participants will be able to:
- Deploy AI models for healthcare solutions using Google Colab.
- Apply AI for predictive modeling on healthcare data.
- Analyze medical imagery using AI-driven techniques.
- Examine the ethical implications of AI in healthcare.
AI in Healthcare
21 HoursThis instructor-led, live training in Nigeria (online or onsite) is aimed at intermediate-level healthcare professionals and data scientists who wish to understand and apply AI technologies in healthcare environments.
By the end of this training, participants will be able to:
- Identify key healthcare challenges that AI can address.
- Analyze AI’s impact on patient care, safety, and medical research.
- Understand the relationship between AI and healthcare business models.
- Apply fundamental AI concepts to healthcare scenarios.
- Develop machine learning models for medical data analysis.
Building End-to-End TinyML Pipelines
21 HoursTinyML involves the deployment of optimized machine learning models onto edge devices with limited resources.
<\/p>This instructor-led live training session, available both online and onsite, is designed for senior technical professionals seeking to design, optimize, and implement complete TinyML pipelines.
<\/p>Upon completing this training, participants will be able to:
- Gather, prepare, and manage datasets specifically for TinyML applications.
- Train and optimize models to run efficiently on low-power microcontrollers.
- Transform models into lightweight formats compatible with edge devices.
- Deploy, test, and monitor TinyML applications in real-world hardware setups. <\/ul>
- Lectures guided by an instructor, supplemented by technical discussions.
- Practical laboratory sessions and iterative experimentation.
- Hands-on deployment exercises on microcontroller-based platforms. <\/ul>
- For tailored training that incorporates specific toolchains, hardware boards, or internal workflows, please contact us to make arrangements. <\/ul>
Course Format
Course Customization Options
ChatGPT for Healthcare
14 HoursThis instructor-led, live training in Nigeria (online or onsite) is designed for healthcare professionals and researchers aiming to harness ChatGPT to elevate patient care, optimize workflows, and enhance healthcare outcomes.
Upon completing this training, participants will be equipped to:
- Grasp the core concepts of ChatGPT and its healthcare applications.
- Employ ChatGPT to automate healthcare processes and patient interactions.
- Deliver precise medical information and support to patients via ChatGPT.
- Apply ChatGPT in medical research and analytical tasks.
Edge AI for Healthcare
14 HoursThis instructor-led, live training in Nigeria (online or onsite) is aimed at intermediate-level healthcare professionals, biomedical engineers, and AI developers who wish to leverage Edge AI for innovative healthcare solutions.
By the end of this training, participants will be able to:
- Understand the role and benefits of Edge AI in healthcare.
- Develop and deploy AI models on edge devices for healthcare applications.
- Implement Edge AI solutions in wearable devices and diagnostic tools.
- Design and deploy patient monitoring systems using Edge AI.
- Address ethical and regulatory considerations in healthcare AI applications.
Fine-Tuning AI for Healthcare: Medical Diagnosis and Predictive Analytics
14 HoursThis instructor-led live training in Nigeria (online or onsite) is designed for intermediate to advanced medical AI developers and data scientists aiming to fine-tune models for clinical diagnosis, disease prediction, and patient outcome forecasting using structured and unstructured medical data.
Upon completing this training, participants will be equipped to:
- Fine-tune AI models on healthcare datasets, including Electronic Medical Records (EMRs), imaging, and time-series data.
- Utilise transfer learning, domain adaptation, and model compression techniques within medical contexts.
- Navigate privacy concerns, bias mitigation, and regulatory compliance during model development.
- Deploy and monitor fine-tuned models in practical healthcare settings.
Generative AI and Prompt Engineering in Healthcare
8 HoursGenerative AI is a technology that creates new content such as text, images, and recommendations based on prompts and data.
This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level healthcare professionals who wish to use generative AI and prompt engineering to improve efficiency, accuracy, and communication in medical contexts.
By the end of this training, participants will be able to:
- Grasp the core concepts of generative AI and prompt engineering.
- Leverage AI tools to streamline clinical, administrative, and research tasks.
- Uphold ethical, safe, and compliant use of AI in healthcare.
- Refine prompts to achieve consistent and accurate results.
Course Format
- Interactive lectures and discussions.
- Practical exercises and case studies.
- Hands-on experimentation with AI tools.
Course Customization Options
- For bespoke training on this course, please contact us to arrange.
Generative AI in Healthcare: Transforming Medicine and Patient Care
21 HoursThis instructor-led, live training in Nigeria (online or onsite) is aimed at beginner-level to intermediate-level healthcare professionals, data analysts, and policy makers who wish to understand and apply generative AI in the context of healthcare.
By the end of this training, participants will be able to:
- Explain the principles and applications of generative AI in healthcare.
- Identify opportunities for generative AI to enhance drug discovery and personalized medicine.
- Utilize generative AI techniques for medical imaging and diagnostics.
- Assess the ethical implications of AI in medical settings.
- Develop strategies for integrating AI technologies into healthcare systems.
LangGraph in Healthcare: Workflow Orchestration for Regulated Environments
35 HoursLangGraph empowers the creation of stateful, multi-actor workflows driven by LLMs, offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are vital for ensuring compliance, interoperability, and the development of decision-support systems that seamlessly align with clinical workflows.
This instructor-led live training, available online or on-site, targets intermediate to advanced professionals eager to design, implement, and manage LangGraph-based healthcare solutions. The course addresses the regulatory, ethical, and operational challenges inherent in this field.
Upon completion of this training, participants will be capable of:
- Designing healthcare-specific LangGraph workflows that prioritise compliance and auditability.
- Integrating LangGraph applications with medical ontologies and standards such as FHIR, SNOMED CT, and ICD.
- Applying best practices for reliability, traceability, and explainability within sensitive environments.
- Deploying, monitoring, and validating LangGraph applications in healthcare production settings.
Course Format
- Interactive lectures and discussions.
- Hands-on exercises grounded in real-world case studies.
- Practical implementation within a live laboratory environment.
Customisation Options
- To arrange a tailored training session for this course, please get in touch with us.
Multimodal AI for Healthcare
21 HoursThis instructor-led live training in Nigeria (online or onsite) is designed for intermediate to advanced healthcare professionals, medical researchers, and AI developers aiming to apply multimodal AI in medical diagnostics and healthcare applications.
By the end of this training, participants will be able to:
- Understand the role of multimodal AI in modern healthcare.
- Integrate structured and unstructured medical data for AI-driven diagnostics.
- Apply AI techniques to analyze medical images and electronic health records.
- Develop predictive models for disease diagnosis and treatment recommendations.
- Implement speech and natural language processing (NLP) for medical transcription and patient interaction.
Ollama Applications in Healthcare
14 HoursOllama is a lightweight platform for running large language models locally.
This instructor-led, live training (online or onsite) is aimed at intermediate-level healthcare practitioners and IT teams who wish to deploy, customize, and operationalize Ollama-based AI solutions within clinical and administrative environments.
Upon completing this training, participants will be able to:
- Install and configure Ollama for secure use in healthcare settings.
- Integrate local LLMs into clinical workflows and administrative processes.
- Customize models for healthcare-specific terminology and tasks.
- Apply best practices for privacy, security, and regulatory compliance.
Format of the Course
- Interactive lecture and discussion.
- Hands-on demonstrations and guided exercises.
- Practical implementation in a sandboxed healthcare simulation environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Prompt Engineering for Healthcare
14 HoursThis instructor-led, live training in Nigeria (online or onsite) is aimed at intermediate-level healthcare professionals and AI developers who wish to leverage prompt engineering techniques for improving medical workflows, research efficiency, and patient outcomes.
By the end of this training, participants will be able to:
- Understand the fundamentals of prompt engineering in healthcare.
- Use AI prompts for clinical documentation and patient interactions.
- Leverage AI for medical research and literature review.
- Enhance drug discovery and clinical decision-making with AI-driven prompts.
- Ensure compliance with regulatory and ethical standards in healthcare AI.
