I am a Hawthorne Fellow in the Laboratory for Innovation in Autism, and my research projects aim to help with the early detection of autism by captureing their motor signatures using smart-tablet gameplay and wearable devices.
I did my undergraduate study in Occupatonal Therapy and then I received my PhD in Biomedical Engineering (both degrees from the National Cheng Kung University, Taiwan). During my PhD study, I was awarded funding for one-year training at the Mayo Clinic (Minnesota, USA) where I advanced my research in the biomechanics of trigger finger. After receiving my PhD degree, I did postdoctoral research at the Cleveland Clinic (Ohio, USA) where I investigated the motor control of individuals with carpal tunnel syndrome. I moved to UK in 2015 to work on an ERC-funded project at the University of Kent where I led biomechanical experiments to help understand human hand evolution. Then I joined the Laboratory for Innovation in Autism at the University of Strathclyde in 2018.
I am interested in exploring human movement biomechanics to improve health and wellbeing, and to answer research questions in a broader context. My current focus is to combine my clinical and engineering training to understand the motor signatures in autism to help with the early detection and intervention.
Please find more details about my research projects at https://sites.google.com/view/szuchinglu
- Early Career Committee (ECC), International Society for Autism Research (INSAR) (External organisation)
- International Society for Autism Research (INSAR) (External organisation)
- A comparative biomechanical perspective on human hand evolution
More professional activities
- IAA BtG: A new window into autism spectrum disorder from space research
- Clark, Ruaridh (Principal Investigator) Macdonald, Malcolm (Co-investigator) Lu, Szu-Ching (Co-investigator) Delafield-Butt, Jonathan (Principal Investigator) Macdonald, Malcolm (Co-investigator)
- Impact Accelerator Account: Bridging the Gaps project.
Network and dynamical systems analysis, developed by Clark and Macdonald within EPSRC-funded research, has enabled advances in autonomous drone control, brain neuroimaging analysis, dynamical system monitoring, and most recently the design of space systems. This research provides an analytical framework for evaluating swipe patterns from a recently completed, and world leading, autism diagnostic clinical trial of 760 pre-school children.
Autism spectrum disorder (ASD) is a neurodevelopmental condition affecting at least 700,000 individuals in the UK with an aggregate annual healthcare and support cost of at least £28 billion. Early identification, proceeded by therapeutic intervention, can produce significant, lifelong health and economic benefit. An ASD diagnosis currently requires a trained clinician, but there is a long and growing waiting list for such assessments. To meet demand, and create more accessible means of assessment, bespoke touchscreen games have been developed for early autism detection and recently trialled for children aged 3–6 years.
Touchscreen games provide a scalable alternative for detecting autism, with machine learning analysis able to detect autism with up to 93% accuracy from children’s motor patterns. Machine learning detects differences in user swipe interactions but cannot reveal the nature of these discrepancies, in particular how swipe patterns differ. By employing network analysis, we can identify – for the first time – the specific pattern signatures of autistic users, which will improve the detection of ASD and the accuracy in differentiating ASD from other neurodevelopmental disorders. We will explore how the development of children with neurodevelopmental disorders differs from their typically developed counterparts. Crucial insights that will form the basis of effective diagnosis, supporting and tailoring therapeutic interventions to address the massive economic impact of mis- or late diagnosis.
- 01-Jan-2022 - 01-Jan-2022
Graham Hills Building
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