CCST6005 Science, Technology and Big Data
Human Wellness Technologies and Analytics
SDG-03
Sustainable Development Goals

[This is a 3-credit Common Core Microcredentials course focused on transdisciplinary project-based learning in a highly compressed format.]

  • CCMCs are optional, i.e. whether or not you take CCMCs (in place of one standard 6-credit CC course) for fulfilling the UG5(c) Common Core requirements, is at your discretion. However, if you opt to take CCMCs, you must take TWO of them, not more or less.
  • Students who have been granted Advanced Standing / Credit Transfer / Course Exemption / Internal Transfer for Common Core courses in their current programme are NOT eligible.
  • For students who have successfully completed two 3-credit CCMCs in place of one 6-credit Common Core course, the average grade point of the two CCMCs will be treated as the grade point of a 6-credit Common Core course for calculation of Graduation GPA under the Common Core Special Proviso.

Course Description

This course explores the innovative domain of Human Wellness Technologies and Analytics, addressing critical challenges such as inactivity and lack of motivation to exercise among elderly individuals and those with sedentary lifestyles. By integrating engineering methods with insights from social sciences, the course examines how advanced technologies can be applied to inspire greater exercise engagement and improve physical performance. Students will delve into the measurement and analysis of individual behaviors, physiological responses, and health conditions to better understand the factors influencing exercise motivation and ability.

Students will gain a comprehensive understanding of key health issues arising from aging and sedentary lifestyles, including inactivity, immobility, and disability. The course emphasizes advanced engineering techniques such as motion capture, balance tests, muscle activity measurements such as electromyography, and functional assessments, including the Short Physical Performance Battery (SPPB) and sit-to-stand tests. These tools enable precise evaluations of behavioral, functional, and physiological characteristics.

Through this interdisciplinary approach, students will learn to implement cutting-edge wellness technologies and analytics to address real-world health challenges. The course equips students with the knowledge and skills to harness these technologies for improving exercise engagement and fostering healthier, more active lifestyles.

Course Learning Outcomes

On completing the course, students will be able to:

  1. Demonstrate a critical understanding of the challenges arising from population aging and sedentary lifestyles in relation to immobility and disability.
  2. Explain the underlying theory and mechanisms of carious measurement devices used for wellness assessing and analysis.
  3. Demonstrate the ability to set up and use various measurement devices and test environments for assessments.
  4. Apply engineering and sensor-based devices along with data analysis techniques to assess and identify behavioral, functional, and physiological characteristics and deficiencies in humans.

Offer Semester and Day of Teaching

Summer Semester
Lecture & tutorial (combined)
9:30 am – 1:30 pm on August 11, 12, 13, 14, 15

Study Load

Activities Number of hours
Lectures 10
Practical (laboratory) classes 20
Assessment: Essay / Report writing 10
Assessment: Presentation (incl preparation) 5
Assessment: Group project 25
Total: 70

Assessment: 100% coursework

Assessment Tasks Weighting
Group project 50
Reflective writing 25
Individual presentation 25

Required Reading

  • Beaudart, C., Tilquin, N., Abramowicz, P., Baptista, F., Peng, D. J., Orlandi, F. de S., Drey, M., Dzhus, M., Fabrega-Cuadros, R., Fernandez-Garrido, J., Laurindo, L. F., Gasparik, A. -I., Geerinck, A., Emin, G., Iacob, S., Kilaite, J., Kumar, P., Lee, S. -C., Lou, V. W. Q., … Bruyere, O. (2023). Quality of life in sarcopenia measured with the SarQoL questionnaire: A meta-analysis of individual patient data. Maturitas, 180, 107902. From https://doi.org/10.1016/j.maturitas.2023.107902
  • Centers for Disease Control and Prevention. Assessment of 30-Second Chair Stand. From https://www.cdc.gov/steadi/pdf/STEADI-Assessment-30Sec-508.pdf
  • Chen, K., Lou, V. W. Q., & Cheng, C. Y. M. (2023). Intention to use robotic exoskeletons by older people: A fuzzy-set qualitative comparative analysis approach. Computers in Human Behavior, 141(107610). From https://doi.org/10.1016/j.chb.2022.107610
  • Cheng, C. Y. M., Lee, C. C. Y., Chen, C. K., & Lou, V. W. Q. (2022). Multidisciplinary collaboration on exoskeleton development adopting user-centered design: a systematic integrative review. Disability and Rehabilitation: Assistive Technology, 19(3), 909-937. From https://doi.org/10.1080/17483107.2022.2134470
  • Cros, D. P., Siao, P., & Vucic, S. (2011). Practical Approach to Electromyography: An Illustrated Guide for Clinicians. Demos Medical Publishing.
  • Fish, J. (2011). Shott Physical Performance Battery. In J. S. Kreutzer, J. DeLuca, B. Caplan (Eds.), Encyclopedia of Clinical Neuropsychology. Springer, New York, NY.
  • Liu, H., Wu, C., Lin, S., Xi, N., Lou, V. W. Q., Hu, Y., Or, C. K. L., & Chen, Y. (2024). From Skin Movement to Wearable Robotics: The Case of Robotic Gloves. Soft robotics, 11(5), 755–766. From https://doi.org/10.1089/soro.2023.0115
  • Wang, K., Zhang, H., Cheng, C. Y. M., Chen, M., Lai, K. W. C., Or, C. K., Chen, Y., Hu, Y., Vellaisamy, A. L. R., Lam, C. L. K., Xi, N., Lou, V. W. Q., & Li, W. J. (2023). High Accuracy Machine Learning Model for Sarcopenia Severity Diagnosis based on Sit-to-stand Motion Measured by Two Micro Motion Sensors. MedRxiv. Cold Spring Harbor Laboratory Press. From https://doi.org/10.1101/2023.05.18.23289933

Course Co-ordinator and Teacher(s)

Course Co-ordinator Contact
Professor K.L. Or
Department of Data and Systems Engineering, Faculty of Engineering		 Tel: 2859 2587
Email: klor@hku.hk
Teacher(s) Contact
Professor K.L. Or
Department of Data and Systems Engineering, Faculty of Engineering		 Tel: 2859 2587
Email: klor@hku.hk
Professor N. Xi
Department of Data and Systems Engineering, Faculty of Engineering		 Tel: 3917 2593
Email: xining@hku.hk
Professor V.W. Lou
Department of Social Work and Social Administration, Faculty of Social Sciences		 Tel: 3917 4835
Email: wlou@hku.hk