Team presenting at ICARM2018

Our team members were presenting research publications and doing lab demos during IEEE ICARM 2018 (The IEEE International Conference on Advanced Robotics and Mechatronics).


authors title
Sri Sai Krishna Suraj Narapareddi, Vineeth Muppalla, Parita Sanghani and Hongliang Ren Comparative Study of Unsupervised Segmentation Algorithms for Delineating Glioblastoma Multiforme Tumour
Avi Srivastava, Hongliang Ren and Liang Qiu Preoperative-Image Guided Neurosurgical Navigation Procedures with Electromagnetic Tracking: An Effective Pipeline and A Cadaver Study
Abhishek Bamotra and Hongliang Ren Characterization and Fabrication of Novel Soft Compliant Robotic End-Effectors with Negative Pressure and Mechanical Advantages
Hritwick Banerjee, Oh Yao Wei Aaron, Bok Seng Yeow and Hongliang Ren Fabrication and Initial Cadaveric Trials of Bi-directional Soft Hydrogel Robotic Benders Aiming for Biocompatible Robot-Tissue Interactions
Shradha Singhvi and Hongliang Ren Comparative Study of Motion Recognition with Temporal Modelling of Electromyography for Thumb and Index Finger Movements aiming for Wearable Robotic Finger Exercises
Wenjun Xu and Hongliang Ren Human Palpation Behavior Modeling with Mixture Models: Towards Autonomous Robotic Palpation

Dr. Ren gave keynote talk at HCR/i-CREATe 2018

Dr. Ren gave a keynote talk at the 2nd Shanghai International Symposium on Human-Centered Robotics, HCR 2018, held together with the 12th international
Convention on Rehabilitation Engineering and Assistive Technology i-CREATe 2018. our lab abstract “Design of a Human-Centered Compliant and Flexible Transoral Robotic System“ has also been accepted for “Poster Presentation” at the conference.

Our undergraduates project on surgical robotics received 2 awards

Our undergraduates DCP project on surgical robotics received 2 awards: FoE 32nd Innovation & Research Award (IRA High Achievement) and NUS Outstanding Undergraduate Researcher (OUR) Prize.

Cai Jiayi, Catherine Cai Xinchen Krishna Ramachandra Seenivasan Lalithkumar Ren Hongliang (Advisor), Project: Image-guided minimally invasive robotic surgery, Faculty of Engineering Innovation & Research Award (High Achievement) & Outstanding Undergraduate Researcher Prize

Showcasing at innovfest unbound 2018

Our team’s showcasing at innovfest unbound 2018 (attended by ~14,000 key players in the technology and innovation scene, Southeast Asia’s largest innovation festival) for two technologies in flexible robotics: ONR grasper and ACTORS.


Dr. Ren received the 2018 IAMBE EARLY CAREER AWARD with an invited talk

Dr. Ren received the 2018 IAMBE EARLY CAREER AWARD from The International Academy for Medical and Biological Engineering (IAMBE) in June of 2018 at The IUPESM 2018 – World Congress on Medical Physics & Biomedical Engineering, Prague, Czech Republic. There are 3 awardees internationally and Dr. Ren is the awardee for Asia-Pacific region according to the IAMBE website. Meanwhile, Dr. Ren gave an invited talk in the EARLY CAREER AWARD session of the conference.

A novel constrained tendon-driven serpentine manipulator


In this paper, a novel constrained tendon-driven serpentine manipulator (CTSM) suited for minimally invasive surgery is presented. It comprises a flexible backbone, a set of controlling tendons and a constraint. In the CTSM not only the curvature of the bending section can be controlled but also the length. Specifically, the curvature is controlled by the tendons, and the length is controlled by a constraint tube, which is translational and is concentric with the flexible backbone. The kinematic model of the CTSM is developed based on the piecewise constant curvature assumption. An analysis shows that by introducing the translational constraint both the workspace and dexterity of the manipulator are improved. The stiffer the constraint the larger the workspace expansion and the smaller the dexterity enhancement. A prototype is developed and the experimental results validate the design idea and analysis.

full text

A preliminary study of force estimation based on surface EMG: Towards neuromechanically guided soft oral rehabilitation robot


Surface electromyography (sEMG) signals have been extensively studied in the area of intention detection, force estimation and control of rehabilitation devices. Studies regarding sEMG based jaw muscle force estimation are necessary towards building intuitive neural-controlled soft oral rehabilitation robot (SORR). This paper presents a force estimation algorithm based on masseter muscle sEMG signals to be used in the control of a developed SORR. Experiments were conducted to collect masseter muscle sEMG signals and biting force from 10 healthy subjects. By using two different time-frequency analysis, signal features were extracted and then input to an empirically established second-order polynomial force estimator to get the estimated force. Comparison has been made regarding to the performance of the proposed feature extraction algorithms. The results obtained from both the algorithms represent a decent accuracy in force estimation, indicating high implementation feasibility in the application of the neural-controlled SORR.