Mitchell Yuwono

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BACKGROUND Falls can cause trauma, disability and death among older people. Ambulatory accelerometer devices are currently capable of detecting falls in a controlled environment. However, research suggests that most current approaches can tend to have insufficient sensitivity and specificity in non-laboratory environments, in part because impacts can be(More)
Prior work suggests that Particle Swarm Clustering (PSC) can be a powerful tool for solving clustering problems. This paper reviews parts of the PSC algorithm, and shows how and why a new class of algorithm is proposed in an attempt to improve on the efficiency and repeatability of PSC. This new implementation is referred to as Rapid Centroid Estimation(More)
Data clustering is a process where a set of data points is divided into groups of similar points. Recent approaches for data clustering have seen the development of unsupervised learning algorithms based on Particle Swarm Optimization (PSO) techniques. These include Particle Swarm Clustering (PSC) and Modified PSC (mPSC) algorithms for solving clustering(More)
The rapidly increasing population of elderly people has posed a big challenge to research in fall prevention and detection. Substantial amounts of injuries, disabilities, traumas and deaths among elderly people due to falls have been reported worldwide. There is therefore a need for a reliable, simple, and affordable automatic fall detection system. This(More)
This paper describes a nonparametric approach for analyzing gait and identifying bilateral heel-strike events in data from an inertial measurement unit worn on the waist. The approach automatically adapts to variations in gait of the subjects by including a classifier that continuously evolves as it “learns” aspects of each individual’s gait profile. The(More)
For people with severe spine injury, head movement recognition control has been proven to be one of the most convenient and intuitive ways to control a power wheelchair. While substantial research has been done in this area, the challenge to improve system reliability and accuracy remains due to the diversity in movement tendencies and the presence of(More)
Particle swarm algorithm has been extensively utilized as a tool to solve optimization problems. Recently proposed particle swarm±based clustering algorithm called the Rapid Centroid Estimation (RCE) is a lightweight alteration to Particle Swarm Clustering (PSC). The RCE in its standard form is shown to be superior to conventional PSC algorithm. We have(More)
Clustering can be especially effective where the data is irregular, noisy and/or not differentiable. A major obstacle for many clustering techniques is that they are computationally expensive, hence limited to smaller data volume and dimension. We propose a lightweight swarm clustering solution called Rapid Centroid Estimation (RCE). Based on our(More)