UTAR
Lee Kong Chian Faculty of Engineering
and Science (LKC FES) student Lim Chu Chen won First Prize in the
Keysight Track at the grand finale of Innovate Malaysia Design Competition
2021 under the Technology Tracks. The competition was held from 1 December
2020 to 2 August 2021. The winning prize was RM5,000.
Lim (top, middle) and Dr Chee (bottom, middle) during the competition
His project titled “Development of an AI-driven Wearable Object Recognition
Device for Rehabilitation Purposes” offers a solution to physiotherapy
patients by reducing their number of visits to the centres and enabling
their progress data to be collected and sent from home. It was truly an
ingenious invention with much engineering and scientific principles applied.
This invention serves as a perfect solution in this covid-19 pandemic
period. It reduces the physical contact between the patients and the
physicians, and at the same time, the patient can continue with their
treatment schedule.
Under the supervision of LKC FES Assoc Prof Dr Chee Pei Song, Lim invented a
smart glove that can recognise objects using a supervised machine learning
algorithm. The glove is able to track the degree of joint movements and
predict the object that the user is holding with a self-learning algorithm.
Moreover, with the data produced by the glove, the grip strength, joint
mobility and pinch strength of the user can be measured.
“This product does not require a specialist to gauge one’s improvements.
This simply means there will be a reduction in the number of visits to the
physiotherapy centres as the rehabilitation training can be done at home
with instant feedback from the glove. In any case, if a specialist advice is
needed, the data can be sent from the glove. The data then can be used by
the physiotherapist to gauge the long-term performance of the user,” Lim
said and added, “In addition, this glove can also provide an educated guess
of which exercise regimen is the most effective for any particular user.”
“Another benefit is the versatility of this product. This glove can also be
used in virtual reality, or an augmented reality landscape as this glove can
provide accurate finger movement data to a computer. So there will be no
need for bulky hand-held controllers to control elements in the virtual
world. The machine learning model of this project allows for a wide range of
applications due to its self-learning nature. Finally, the most important
aspect of this project is the cost. As you may know, the equipment required
for rehabilitation purposes usually cost high but my proposed solution is
much more affordable. So users can purchase and use this product at the
comfort of their own homes,” explained the Electrical and Electronic
Engineering student.
The inspiration for his idea stemmed from his late grandfather, who was
diagnosed with dementia and was in the verge of experiencing the Parkinson’s
disease.
Lim demonstrating how the data can be used to check the improvement of the
user over time
According to Lim, neurological diseases that affect motor skills, such as
stroke or Parkinson’s disease affects more than two million people annually
in the United States alone. He hopes that his invention could bring a
solution that is both affordable and sustainable to the world of
rehabilitation.
The Innovate Malaysia Design Competition is the largest design competition
in Malaysia, open to all third year or final year degree engineering,
computer science, IT, and science/mathematics students. The competition
aimed to promote innovative culture in engineering design work, tackle
real-world problems with practical engineering solutions, and churn out
brightest talents for product development, further research, and
commercialisation. Technology companies, including AWS, Intel, Keysight,
Microsoft, and SilTerra, worked together to co-organise the competition.
The competition introduced a total of five different tracks under the
Technology Tracks; they were SilTerra/CEDEC/Intel Track, Intel Track,
Keysight Track, Microsoft Track and AWS Track. Lim won First Prize in the
Keysight Track.
The competition received a total of 274 submissions from both public and
private universities across Malaysia. Among the universities that
participated were Universiti Sains Malaysia, Universiti Teknologi Malaysia,
Universiti Kebangsaan Malaysia, Universiti Tun Hussein Onn Malaysia,
Universiti Malaysia Pahang, Universiti Malaysia Perlis, Universiti Teknikal
Malaysia Melaka, International Islamic University Malaysia, Universiti
Teknologi MARA, Universiti Malaysia Sarawak, INTI International University,
Swinburne University, Multimedia University (Cyberjaya) and UTAR.
Learn more about his project here:
https://www.youtube.com/watch?v=BvOny3NeHCY
The SVM-based machine learning algorithm can assist the glove in recognising
objects with an accuracy of 91.88%
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