After conducting about 40 trials using the right hand (red color) and later 40 trials with the left hand (green color), it became clear that reaction time RT was at 0.74 seconds and which was very low. The index performance (IP) also reduced and settled at 6.7 bits/seconds while the standard deviation increased and settled at 0.39 second. The reason behind all this variations was due to the fact that I am right handed. The table below provides screenshots of the summary of the findings.
|40 Trials with right hand||40 Trials with left hand|
|Reaction time||0.54 s||0.74 s|
|Index of performance||9.9 bits/s||6.7 bits/s|
|Standard deviation||0.18 s||0.39 s|
Further tests was carried out and which were 100 trials with both the right hand and the left hand. The final result showed, IPR =8.3 bit/s while the IPL =5.1 bits/s. Handedness= (IPR – IPL) / min (IPR, IPL) = (8.3 – 5.1)/5.1 = 0.627. The results are positive because I am right handed and the final computation (0.627) is an indication of the degree of imbalance.
Effects of Fatigue
A grip exerciser was used to cause muscle fatigue on the muscles which regulate the movement of my fingers and wrist (Mark, 2015). Just under one minute, I managed to attain a 100% failure level. The outcomes of the failure level and index performance are shown in the table below after 40 trials.
|Failure Level||Index of Performance IP|
When find are done under a normalized IP, the final outcome would be represented in the line graph as shown below.
Similar results may be attained through performing a different activity that entail the use of heavy load on the day to day operational activity. The results can be interpreted by increasing the inability of the hand muscle to be able to react to the operations due to the weight of the load exercise. Even after a few minutes for break, the hand muscles were not able to perform at their initial peak level. This results can be summarized in the table below
From the above, it is clear that fatigue does have an effect on performance of an individual.
The table below gives a summary of the results of my index performance after performing 100 trials using various input methods.
|Input methods||Index of performance|
|Normal mouse||8.3 bits/s|
|Laptop trackpad||5.3 bits/s|
|Normal mouse which is on vertical surface||6.2 bits/s|
The table below gives a summary of the results of my index performance after performing 100 trials using various tracking speed
|Normal speed||8.3 bits/s|
|Low speed||3.3 bits/s|
|Much faster speed||8.2 bits/s|
The table below gives a summary of my findings regarding my IP after performing 100 trials once I had established a fixed target size through setting minW and maxW the same similar value of 10, 50 and 100. The final results showed that performance increased with increase in the target size.
|Target size||Index of performance|
A further experiment was done using 100 different trials for fixed distances through setting min D and max D with fixed values of 50, 200 and 400. The final results that index performance became less as the distances increased.
|Distance||Index of performance|
Performance can also get affected by pointing devices (MacKenzie, 2015). This will be illustrated through the use of various pointing devices using both the right and the left hand. The result are self-explanatory. Any unusual pointing lowers the IP and more the unusual state continues, the more the influence will be. The reason behind this is that we have a muscle memory and all of us are aware of our hand movement when moving the mouse pointer.
The scenario is different when using the joystick or touch pad since we have to think of what to do hence the milliseconds spent in thinking tend to affect the IP.
|3||Mouse that has more weight||9||7|
|4||Mouse with less weight||8||6|
It can therefore be concluded that if we are to increase the IP with every device that we intend to use, it would be necessary to do some initial training with the device.