Module Information
Course Delivery
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | Mini-project portfolio Exploration of a topic/data set to focus on and explore what insights its data can provide for that scenario. Is set in a particular area of student interest, and will include a viva 2000 Words | 50% |
| Semester Assessment | Literature review In depth evaluation of the principles, use (in either human or specific animal species) and limitations of a particular type of equipment covered in this module 2000 Words | 50% |
| Supplementary Assessment | Literature review In depth evaluation of the principles, use (in either human or specific animal species) and limitations of a particular type of equipment covered in this module 2000 Words | 50% |
| Supplementary Assessment | Mini-project portfolio Exploration of a topic/data set to focus on and explore what insights its data can provide for that scenario. Is set in a particular area of student interest, and will include a viva 2000 Words | 50% |
Learning Outcomes
On successful completion of this module students should be able to:
Critically evaluate the principles, applications, and limitations of contemporary movement analysis technologies across human or animal contexts.
Demonstrate understanding of data collection methods using wearable and video-based technologies
Interpret and present movement data using appropriate analytical techniques, considering the complexity and context of the chosen scenario.
Apply theoretical knowledge to assess the relevance and effectiveness of movement analysis tools in research, clinical, or applied settings.
Reflect on the research process, identifying challenges, progress, and personal learning through flexible assessment formats tailored to individual interests.
Brief description
This module examines gait and movement analysis using contemporary technologies such as wearables containing GPS tracking and accelerometers, bio-logging, and AI-supported video analysis. Students will gain hands-on experience with these tools and data, learning how to collect and interpret movement data and evaluate the methodologies used. Emphasis will be placed on understanding the principles behind the technologies, their practical applications across different contexts, and their limitations. Through a combination of practical sessions, seminars, and lectures, students will engage with the data collected and aligned to their future career interest. Students have the opportunity to apply and explore its relevance to human or animal scenario of their interest, with applications in behaviour, health and/or performance.
Aims
To equip students with the theoretical understanding and practical skills needed to analyse movement using GPS, accelerometers, bio-logging and video analyses. It fosters critical evaluation of data and methodologies across human or animal contexts, with applications in behaviour, health, and performance. Through hands-on experience and interdisciplinary exploration, students learn to apply movement analysis tools in both research and applied settings with an emphasis on their future career interests.
Content
- GPS data for behaviour analysis or sport performance,
- Accelerometer data analyses for sport and health,
- Wearables and bio-logging of physiological and environmental sensors,
- Video analysis of movement and behaviour
Each topic would comprise a 1 hour lecture and 2 hour practical that will showcase the use of this equipment in a student's area of interest (human or animal).
There will also an explicit focus in the delivered contents on developing your own mini-project and its execution, including example case studies, exploration of open access data principles, team work, and data analyses and interpretation.
Module Skills
| Skills Type | Skills details |
|---|---|
| Adaptability and resilience | Students will develop their own literature reviews and data-driven investigations tailored to their own interest (e.g. human or animal species), developing skills in sourcing, using public data, evaluating, and synthesising academic and applied evidence. |
| Co-ordinating with others | Students will work together in small groups (2-6 students) on the mini-project portfolio to explore one particular topic in depth and detail. |
| Critical and analytical thinking | Students will engage with real-world data sets, applying critical thinking to interpret complex movement patterns and address methodological limitations. |
| Digital capability | Students will analyse and interpret quantitative movement data from technologies such as GPS, accelerometers, and video tracking systems. |
| Professional communication | Students will present findings from their mini-project in a format of their choice (e.g., report, poster, video), demonstrating clarity, audience awareness, and scientific literacy. |
| Real world sense | Students will explore the movement analysis applications in diverse fields (e.g., sport, health, animal behaviour), helping students identify career pathways aligned with their interests. |
| Reflection | Through reflective components in the mini-project assessment, students will evaluate their progress, challenges, and strategies for improvement. |
| Subject Specific Skills | Students will use digital tools for data collection, processing, and analysis, including Excel, wearable tech platforms and AI-supported video analysis software. |
Notes
This module is at CQFW Level 6