Gibbs again took the leading role in studying the effect of different body parts in manual control. In a one dimensional target acquisition task, Gibbs (1962) studied the effect of three different body parts: the thumb (activated by the carpometacarpal joint), the hand (activated by the wrist), and the forearm (activated by the elbow) in both position and rate control systems with various control gains and time delays. Subjects' performance in Gibbs' study according to the ranking was: hand, forearm, and thumb.
Hammerton and Tickner (1966) later replicated Gibbs' study in a 2 DOF target acquisition task. Although Gibbs subsequently argued with Hammerton and Tickner about experimental methodology and credit ownership (Gibbs, 1967; Hammerton and Tickner, 1967) , the two studies in fact arrived at a very similar conclusion, that performance with the hand (wrist movement) was superior to that of the thumb and the forearm. This advantage was greater in more difficult tasks such as those with long time delays (Hammerton and Tickner, 1966) . Note that both studies found that the wrist was more effective than the thumb. Neither Gibbs nor Hammerton and Ticker included fingers in their studies, however.
The motor performance of different limbs has also been investigated in various Fitts' law studies. Fitts' law (Fitts, 1954) established the simple linear relationship: MT = a + b ID in tapping tasks, where MT is the movement time, ID = log2(2A/W) is the Index of Difficulty, A is the movement amplitude and W is the width of the target area. The slope parameter b, in units of seconds/bit, is the inverse of the motor system information processing rate. Fitts' law studies typically found this rate (1/b) to be in the vicinity of 10 bits/second when the arm was involved in the movement. Fitts (1954) speculated that other limbs such as fingers may show different processing rates. Later studies supported this hypothesis. Langolf, Chaffin, and Foulke (1976) investigated the Fitts' law relationship using amplitudes of A = 0.25 cm, A = 1.27 cm and A > 5.08 cm. For the first two amplitudes, the experiment was carried out using a microscope. For the large range (>5.08 cm), the experiment was carried with direct vision. Langolf and colleagues observed that for A = 0.25 cm subjects moved the stylus tip (a 1.1 mm peg) primarily with finger flexion and extension. For A = 1.27 cm, flexion and extension of both wrist and fingers occurred. For A > 5.08 cm, the forearm and upper arm were involved in the movements. With this method of allocating actuation to different muscle groups by controlling the range of movement, Langolf and colleagues concluded that the information processing rates (1/b) for the fingers, wrist, and arm were 38 bits/sec, 23 bits/sec and 10 bits/sec respectively (see Figure 6 in Langolf, et al., 1976). This study has been widely cited in the literature (e.g. Boff and Lincoln, 1988; Keele, 1986; Card, Mackinlay, and Robertson, 1991) as evidence that fingers are among the most dextrous organs.
Card et al. (1991) recently reviewed Fitts' law studies with various body parts (finger, wrist, arm, neck) and pointed out the limitations of the widely used computer input device - the mouse. They suggested "a promising direction for developing a device to beat the mouse by using the bandwidth of the fingers". Experimental work has not yet been produced to support this prediction, however.
In summary, both neurophysiological studies (the homunculus model) and FittsÌ law studies suggest that use of the small muscle groups (fingers and thumbs) should result in better performance than the large muscle groups (arm and shoulder). However some studies in manual control (e.g. Gibbs, 1962; and Hammerton and Tickner, 1966) are not completely consistent with such a prediction.
Due to their theoretical motivation, most studies in the literature
tend to compare performance of different muscle groups against
each other. From a practical and ecological point of view, such
a contrast is not necessary for the design of a 6 DOF input device.
The human upper limb as a whole (from shoulder to finger tips)
has evolved to be a highly dextrous and yet powerful device. Every
part of it has its purpose and function. What is needed in input
device design is to make use of all the parts according to their
respective advantages. The larger muscle groups that operate the
wrist, elbow, and shoulder have more power and a larger range
of movement. The smaller muscle groups that operate the fingers
and thumb have more dexterity. When all the parts work in synergy,
movement range and dexterity can both be maximised.