UTAR LKC FES student Lim Chu Chen wins first prize at Innovate Malaysia Design Competition 2021

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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