My research concerns the human hand and human dexterity, with applications in areas such as robotic/prosthetic hand design and hand rehabilitation. It can be broken up into the following general topics.

Note: please see my Google Scholar page for up to date access to my publications.

Details of human manipulation

Few previous works have considered the kinematics of human precision manipulation during an active workspace exploration task. A paper presented at Haptics Symposium 2014 shows the overall positional workspace obtained when participants manipulate a small pointed object in the fingertips, using either the thumb and index fingers, or the thumb, index and middle fingers (2 and 3 finger cases). The results show the overall workspace for a representative participant, and the overall results show a slightly larger workspace for the two finger case. The results should be useful in better understanding what object motions people can achieve using only within-hand motion of the fingertips, and could be applied to haptic device design and applications, or to benchmark the dexterous manipulation performance of robotic hands.

Taking a detailed look at human hand motion requires a good understanding of human hand kinematics. I recently presented a paper at BioRob 2012 reviewing different kinematic models for the human hand (poster also available). The work focuses on analyzing how different modeling assumptions affect model accuracy.

For more information, please see:

Ian M. Bullock, Thomas Feix, and Aaron M. Dollar
Dexterous Workspace of Human Two- and Three-Fingered Precision Manipulation, Proceedings of the 2014 IEEE Haptics Symposium, Houston, TX, United States, February 23-26, 2014.

Ian M. Bullock, Julia Borras, and Aaron M. Dollar
Assessing Assumptions in Kinematic Hand Models: A Review, Proceedings of the 2012 IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), Roma, Italy, June 24-27, 2012.

Manipulation outside the laboratory

While controlled laboratory studies can provide valuable information about the details of manipulation mechanics, they are less able to provide an overall understanding of how people use their hands in daily life.

I am part of a research project that involves recording and analyzing video of natural hand use during work. The resulting data will provide valuable information about some of the most frequent grasps used by people in their daily lives. This information could then be used in many areas, such as to optimize the capabilities of a prosthetic device, or to help guide decisions made during hand surgery.

Two papers are now available related to this work:

Ian M. Bullock, Joshua Z. Zheng, Sara De La Rosa, Charlotte Guertler, Aaron M. Dollar
Grasp Frequency and Usage in Daily Household and Machine Shop Tasks, IEEE Transactions on Haptics, 22 Feb. 2013. IEEE computer Society Digital Library. IEEE Computer Society, DOI: 10.1109/ToH.2013.6

Ian M. Bullock, Thomas Feix, Aaron M. Dollar
Finding Small, Versatile Sets of Human Grasps to Span Common Objects, Proceedings of the 2013 IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, May 6-May 10, 2013.

Manipulation classification

The human hand is capable of an incredible range of manipulation behavior. As a result, coming up with a systematic and comprehensive classification for manipulation is challenging. It is also difficult to create a classification scheme that can apply to both human and robotic systems, so that the manipulation of a robotic system can be compared to the human hand.

Work with Aaron Dollar has produced a new classification scheme which can be applied to any type of manipulation motion. It uses a number of simple criteria to classify manipulation, such as whether there is motion, object contact, and motion within the hand. One strength of the classification is that it helps to emphasize the distinction between tasks where an object is fixed rigidly to the arm and tasks that involve dexterous within hand manipulation.

For more information, please see:

Ian M. Bullock, Raymond R. Ma, Aaron M. Dollar,
A Hand-Centric Classification of Human and Robot Dexterous Manipulation, IEEE Transactions on Haptics, 11 Sept. 2012. IEEE computer Society Digital Library. IEEE Computer Society, DOI: 10.1109/ToH.2012.53

Ian M. Bullock and Aaron M. Dollar
Classifying Human Manipulation Behavior, Proceedings of the 2011 IEEE International Conference on Rehabilitation Robotics (ICORR), Zurich, Switzerland, June 29-July 1, 2011.

© 2015 by Ian Bullock
Contact: ian.bullockGETRIDOFTHIS@yale.edu