Best Paper Award in the area of Diagnostic, Therapeutic, and Rehabilitation Engineering – IECBES 2021

The 2021 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES 2021) was held virtually on March 1 – 3 2021. This conference is a premier biomedical engineering and sciences conference in this region, which have carefully established strategic partnerships with key authorities and figures in the field to share cutting-edge research and innovation to facilitate and push boundaries in biomedical engineering and sciences research.

In the IECBES 2021, the submitted paper led by the CHST member, Dr Goh Choon Hian with the title, “Development of Clustering Algorithms for Older Faller in Malaysia” was selected as the Best Paper Award in the area of Diagnostic, Therapeutic, and Rehabilitation Engineering .

Abstract: Falls are a serious problem which lead to negative consequences on the quality of life especially for older people. Most falls are caused by the interaction of multiple risk factors. However, manual analysis in fall data is time consuming and high processing cost. Therefore, this study purpose is to develop a clustering-based fall risk algorithm which can provide assistance for clinicians. The proposed algorithm consists of several stages including data pre-processing, feature selection, feature extraction, clustering and characteristic interpretation. This study employed Malaysian Elders Longitudinal Research (MELoR) dataset. A total of 1279 subjects and 9 variables are selected for clustering. The combination of t-Distributed Stochastic Neighbour Embedding (t-SNE) for feature extraction, and K-means clustering algorithm are chosen to cluster the subjects into Low (13%), Intermediate A (19%), Intermediate B (21%) and High (31%) fall risk group. In comparison, older people with higher fall risk have slower gait, imbalance, weaker muscle strength, with cardiovascular disorder, poor performance in cognitive tests, and advancing age. To conclude, the proposed fall risk clustering algorithm is capable of grouping the subjects that have similar features. It presents a potential as assessment tool in management of falls.

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