Browsing by Author "Ranaweera, R. K. P. S"

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Embargo
Design and Analysis of An Anthropomorphic Two-DoF Ankle-Foot Orthosis
(IEEE, 2019-04-19) Ranaweera, R. K. P. S; Abayasiri, R. A. M; Gopura, R. A. R. C; Jayawardena, T. S. S; Mann, G. K. I
This paper proposes a two-degrees of freedom passive-dynamic ankle-foot orthosis (AFO). In view of enhancing anatomical conformity, an anthropomorphic design is proposed to minimize mechanical interferences between ankle and orthosis. The biomimetic features such as passive stabilizers and dampeners in the proposed mechanism intrinsically support the ankle and foot to maintain stability and improve shock-absorbing ability. The mobility, ranges of motion, and manipulability measures for the proposed AFO have been investigated using mathematical modeling and simulation approaches. The analysis revealed the effectiveness of the proposed AFO in meeting the complex kinematics of ankle joint compared to the predecessors. Potentially, the proposed AFO can serve as a platform to carry out research and development on robotic orthoses for the lower extremity.
Embargo
Design and Analysis of An Anthropomorphic Two-DoF Ankle-Foot Orthosis
(IEEE, 2019-04-19) Ranaweera, R. K. P. S; Abayasiri, R. A. M; Gopura, R. A. R. C; Jayawardena, T. S. S; Mann, G. K. I
This paper proposes a two-degrees of freedom passive-dynamic ankle-foot orthosis (AFO). In view of enhancing anatomical conformity, an anthropomorphic design is proposed to minimize mechanical interferences between ankle and orthosis. The biomimetic features such as passive stabilizers and dampeners in the proposed mechanism intrinsically support the ankle and foot to maintain stability and improve shock-absorbing ability. The mobility, ranges of motion, and manipulability measures for the proposed AFO have been investigated using mathematical modeling and simulation approaches. The analysis revealed the effectiveness of the proposed AFO in meeting the complex kinematics of ankle joint compared to the predecessors. Potentially, the proposed AFO can serve as a platform to carry out research and development on robotic orthoses for the lower extremity.
Open Access
Development of A Passively Powered Knee Exoskeleton for Squat Lifting
(Atlantis Press, 2018-05-01) Ranaweera, R. K. P. S; Gopura, R. A. R. C; Jayawardena, T. S. S; Mann, G. K
This paper proposes a knee exoskeleton with passive-powering mechanism to provide power assistance to the knee joint during squat lifting of objects from the ground. It is designed to capture and store 20% of the biomechanical energy dissipated at the biological knee joint during decent phase and return the harnessed energy in the ascent phase in a squatting cycle. The effectiveness of the proposed system was verified by evaluating performance of key muscles of knee joint using surface electromyography (sEMG) signals. Statistical data from experiments revealed a reduction of peak root-mean-square averages of sEMG signals of knee extensor muscles by 30 - 40% during squatting.
Embargo
Machine learning-based approach for modelling elastic modulus of woven fabrics
(IEEE, 2020-07-28) Kularatne, S.D.M.W; Ranawaka, R. A. H. S; Fernando, E. A. S. K; Niles, S. N; Jayawardena, T. S. S; Ranaweera, R. K. P. S
There has been a shift of focus from aesthetic properties to mechanical and functional properties of textiles with the recent developments in technical textiles and wearable technology. Therefore, understanding how various fabric parameters influence the mechanical properties of fabrics is paramount. In applications where compression and stretching of fabrics are important, the elastic modulus is a key fabric property that needed to be controlled precisely. Woven fabrics are capable of providing superior elastic properties, but how various fabric parameters affect elastic modulus is not well understood. In this study, two machine learning techniques were implemented to model the elastic modulus of woven fabrics and were compared with multivariable regressions. The two machine learning techniques used are Artificial Neural Network (ANN) and Random Forest Regression. As input variables; weave factor (numerical representation of weave structure), warp yarn count and pick density were used. Both ANN and Random Forest Regression were able to generate reasonably accurate results with Random Forest Regression been the better of the two methods. Using Random Forest Regression, feature importance of the input variables was obtained, and it proved that the weave structure has a notable impact on the elastic modulus of woven fabrics.