Chapter 2. Prehension

Chapter 2 - Prehension 37
postures. In order to develop predictive models and a deeper under-
standing of human prehension, one must go beyond classification and
description. A quantitative approach with more than nominal levels of
metric is necessary. A first step crucial to this endeavor is the identifi-
cation of a set of parameters that could ultimately tie into a set of im-
portant control variables. For doing powerful experimental studies of
prehension, or even for developing an expert system or neural net-
work, one needs access to quantifiable variables, or parameters. Once
meaningful parameters have been identified, the next step is to develop
experimental and computational models to examine the range of these
parameters and their interrelations. These are necessary steps to eluci-
date the important control variables in prehension.
2.4.1 Encoding hand postures
Going beyond a taxonomy of grasp types, Jacobson and Sperling
(1976) presented a detailed coding system which describes qualitatively
the configuration of the grip of healthy and injured hands (see
Figure 2.8). Focusing mainly on hand postures and based on film
analysis, the code nominally labeled hand grips in terms of: fingers
and other parts of the hand involved; their relative positions; finger
joint angles; contact surfaces of the fingers and palm with objects; and
the relationship between the object’s longitudinal axis and the hand.
For example, in Figure 2.8, the posture on the left is used to hold a
small object between the thumb and index finger. The code for this
posture is: 1OMEIFIW2CMFIFM. This code denotes that the thumb
(finger 1) is in its opposed position (0), the metacarpophalangeal joint
is extended (ME), the interphalangeal joint is flexed (IF), and the tip is
in contact with the object (M). Further, the index finger (finger 2) is
adducted (C) against the middle finger, the metacarpophalangeal joint
is flexed (MF), both interphalangeal joints are flexed (IF), and the tip
is in contact with the object (M). The coding system was computerized
for larger investigations. A strength of their system is in its identification
of hand surfaces involved for a given grasp type, since this
can tie into how the sensory information will be gathered. Another
strength is in the identification of the number of fingers and finger
postures, since this ties into the application of force for a given object.
Several disadvantages of their system can be observed. First, they focus
on hand postures and only the longitudinal axis of the object. The
coding is still at a nominal level of measurement, although it does
provide a description of hand postures. In terms of using it for clinical
and/or occupational evaluation of hand function, it is time-consuming