## 4 Discussion  
This study successfully developed an algorithm that may be used to compute torque peaks from isometric dynamometry data in an acute sense. The series of modules and functions developed enabled peak calculations over a short window of time. Furthermore, the randomization method inspired by [PERSON] and [PERSON] [11] was implemented to deliver pulse trains in a random fashion, reducing any bias of order. In addition, the use of block-partition notation and pulse train labelling within partitions may be used by future researchers as a framework upon which torques generated from electrical stimulation may be discussed. A major function of this algorithm was peak detection of torque data. Using a modified form of previously developed code, in concert with the findpeaks function, enabled peaks to be detected with a high degree of accuracy. The use of the intrinsic findpeaks function has been used in various contexts to detect peaks in biological data. It has been used in gait analysis [13], muscle dynamics [14], surgical skills analysis [15], and sleep medicine to measure peaks in flow rate and epiglottic pressure data [16]. However, in our study, findpeaks was used for the case of torque peak detection in isometric dynamometry, which is less well-known. The findpeaks function is simple to implement and access via a single line command or the use of the Signal Processing Toolbox [17]. However, the presented algorithm significantly expanded upon the basic findpeaks function through the addition of a user-prompted set of programs (namely, ginputadd, ginputremove, ginputadd2). These user-prompted commands allowed for the removal of anomalous peaks or the addition of missing peaks in dynamometry data that cannot be picked up by the findpeaks function, which uses a periodic detection algorithm. These algorithms put forward in this paper, therefore, may be adopted by other researchers who aim to analyse data that do not occur with regular periodicity, similar to the presented data, where the torque waveform had several partitions of data with varying periods in between. These tailor-designed functions allow for the selection or removal of peaks from torque data so that the magnitude of all contractions can be calculated. Future extension of this work could also involve sharing of the code of the algorithm on platforms such as Github [18]. It could further be adapted as a Python version, which has the advantage of being available for free. This could increase the likelihood of professionals who may not necessarily be familiar with coding to be involved in programming for diagnostic purposes. This may be particularly advantageous in a health sciences research setting, provided that there is adequate research support for clinical staff from, for example, a biomedical engineer researcher. This experimental work demonstrated that, at most, there were minor differences in torques generated during stimulation at different duty cycles, as reflected by the ANT metrics. There was a slightly larger ANT for duty cycles with both ON and OFF time doubled and trebled, in comparison with those in isolation. Perhaps there would have been substantially larger differences if stimulation had been delivered for a longer period of time. In addition, the relationship between the degree of \"ON\" and \"OFF\" and the decrease in torques/ANT metrics (i.e., fatigue) may differ between acute and chronic stimulation. For example, [PERSON] and [PERSON] [19] demonstrated that continuous stimulation can cause less decrease in force than intermittent stimulation in their study using a 1:1 duty cycle delivered at 300 us and 20 Hz. Similarly, [PERSON] and colleagues [20] also suggested that intermittent stimulation may cause more fatigue than continuous over a shorter period of time. However, if a muscle is subjected to continuous stimulation for much longer, there will be much more fatigue than a continuous stimulation analogue.