Yinfeng Fang

About

Yinfeng Fang is a leading researcher at the intersection of robotics, human-machine interaction, and assistive technology, with a primary focus on robot-assisted therapy for children with Autism Spectrum Disorder (ASD) and dexterous robotic hand control. His most impactful work, "How to Build a Supervised Autonomous System for Robot-Enhanced Therapy for Children with Autism Spectrum Disorder" (138 citations), pioneers the transition from human-operated Wizard of Oz paradigms to autonomous robotic systems that can adaptively engage children with ASD, significantly reducing therapist workload. Fang further advanced this field with his "Sensing-Enhanced Therapy System" (32 citations), which integrates automatic sensory feature extraction to assess children's behavioral responses, bridging the gap between robotic autonomy and clinical assessment. In parallel, his comprehensive review "Optimal Grasp Planning of Multi-Fingered Robotic Hands" (52 citations) and subsequent work on intelligent computational control (14+ citations) have established foundational frameworks for stable, force-closure grasping in dexterous hands like the HIT/DLR II. His recent transfer learning approach to sEMG-based hand gesture recognition (12 citations) demonstrates ongoing innovation in prosthetic control, achieving cross-subject generalization. With over 300 total citations across his ten most-cited papers, Fang’s contributions are shaping both socially assistive robotics and bionic manipulation.

Research Focus

Key Achievements

Top Papers

1. 1
   How to Build a Supervised Autonomous System for Robot-Enhanced Therapy for Children with Autism Spectrum Disorder

   138 citations · 2017
2. 2
   Optimal grasp planning of multi-fingered robotic hands: a review

   52 citations · 2015
3. 3
   Sensing-Enhanced Therapy System for Assessing Children With Autism Spectrum Disorders: A Feasibility Study

   32 citations · 2018
4. 4
   Modelling EMG driven wrist movements using a bio-inspired neural network

   20 citations · 2021
5. 5
   Intelligent Computational Control of Multi-Fingered Dexterous Robotic Hand

   14 citations · 2015
6. 6
   Computation of Grasping and Manipulation for Multi-Fingered Robotic Hands

   14 citations · 2015
7. 7
   Transfer Learning Enhanced Cross-Subject Hand Gesture Recognition with sEMG

   12 citations · 2023
8. 8
   Finger pinch force estimation through muscle activations using a surface EMG sleeve on the forearm

   12 citations · 2014
9. 9
   Intelligent Computation in Grasping Control of Dexterous Robot Hand

   11 citations · 2015
10. 10
    The Mathematical Model and Control Scheme of a Four-Legged Robot Based on GZ-I and Note Module

    10 citations · 2010

Key Collaborators

Contact & Links