Analysis of fingertip-based features during pick and grasp
manipulation
Tatsuya Yamashita, Megumi Nakao, Member, IEEE and Tetsuya Matsuda, Member, IEEE

Abstract— This study is an exploration of fingertip-based
features that can distinguish pick and grasp actions. We
measured time-series data of fingertip force and accelerationfrom seven subjects grasping cylindrical urethane. The analysis
results of support vector machine learning show that thethree-dimensional feature obtained from fingertip force andacceleration is effective for discriminating the two operations.

I. INTRODUCTION
The functions of the fingertips are diverse and have gained
attention across the medical and mechanical fields. To
understand fingertip operations, Cutkosky presented a
hand-centric classification scheme [1]. Some studies haveattempted to measure and analyze fingertip operations [2, 3],
but the tactile sensation is impaired during measurement [2],or only single-finger operations have been analyzed [3]. As
such, there is no research that measures natural multi-finger
operations and analyzes the fingertip force.
This study explores fingertip-based features that candistinguish two multi-finger operations: pick and grasp. We
define a pick to be an operation that does not bring the palm
into contact with the object and a grasp to be an operation thatbrings the palm into contact with the object. We define
multi-dimensional features that can be calculated fromtime-series data of fingertip force and acceleration and selectfeatures that are effective for discriminating the twooperations.
II. METHODS
To analyze human finger operations without impairing thetactile sensation, we aim to fulfill the three conditions adopted
by the research of Nakao et al. [3]. To satisfy the threeconditions, we used HapLog (Tec Gihan Co., Ltd., JAPAN)(Fig. 1). We obtained four measurement values—pressure
for the thumb andfor the index finger as well as themagnitude of acceleration
for the thumb andfor theindex finger. We assumed that the difference between the pickand grasp appeared in the interval T from the time when thefinger touched the object to the time when the finger pressure
increases. Moreover, we assumed that the two operations can
be classified by the features based on fingertip force andacceleration. Then, we evaluated 10 feature values. (Table Ⅰ)Figure 1. Measurement system and changes in pressure and magnitudeof acceleration measured at the fingertip: (a) pick and (b) grasp.
Table Ⅰ. 10 Feature values
Name

F1F2F3F4F5F6F7F8F9F10F1/F3F2/F4Feature
Name
Featurefingertip force and acceleration from seven subjects picking
and grasping cylindrical urethane. When performing the pickand grasp, each subject was instructed to touch the side orbottom of the object. Figure 1 shows the time series of andfor one pick operation and grasp operation of subject 3. For
each subject, we attempted to distinguish the two operations
using the proposed 10 features. We used a support vector
machine to calculate the recognition rate. We evaluated therecognition performance of each feature and selected the top
five features of the recognition rate. We created a combinationof 34 features that included the features of both fingers andconfirmed the recognition rate for all sets.
The recognition rate is 78.2% for a three-dimensionalfeature consisting of F2, F3 and F9.
IV. CONCLUSION

This study analyzed features that can distinguish twooperations. The recognition rate was 78.2% when using thethree-dimensional feature; therefore, we conclude that pickand grasp operations were distinguishable in each subject.
REFERENCES[1]

III. EXPERIMENTS AND RESULTS

Experiments were conducted to obtain time-series data of[2]

T. Yamashita, M. Nakao, and T. Matsuda are with the Graduate School ofInformatics, Kyoto University, Yoshida-Honmachi, Kyoto, 606-8501, Japan(e-mail: tyamashita@sys.i.kyoto-u.ac.jp).

[3]

M.R. Cutkosky, “On Grasp Choice, Grasp Models, and Design ofHands for Manufacturing Tasks”, IEEE Trans. on Robotics andAutomation, vol. 3, no.5, 1989.
E. Battaglia, M. Bianchi, A. Altobelli, G. Grioli, M. Catalano, A. Serio,
M. Santello, A. Bicchi, “Thimble sense: a fingertip-wearable tactile
sensor for grasp analysis”, IEEE Trans. on Haptics, vol. 9, no. 1, pp.
121-133, 2016.
M. Nakao, M. Senoo and T. Matsuda, “Fingertip-based Feature
Analysis for the Push and Stroke Manipulation of Elastic Objects”,
IEEE Trans. on Haptics, vol. 4, no. 1, pp. 1-10, 2017.