<p>The effect of force-rate, i.e., rate of change of force stimuli, on continuous kinesthetic force perception has not been investigated and remains an open area for research. Previous studies do not account for the force-rate in the Weber fraction of kinesthetic force discrimination. However, this force-rate agnostic Weber fraction fails to explain continuous kinesthetic force discrimination fully. For example, if the signal changes very slowly, a participant may get accustomed to the change, and hence, a larger, just noticeable difference (JND) is expected. Conversely, for a fast-varying haptic force, a smaller JND is expected. In this work, we aim to explore the relationship between the Weber fraction and the force-rate. For this purpose, we designed an extensive psychophysical experiment where a participant is exposed to a linearly increasing kinesthetic force stimulus and is asked to react to the change. We utilize a machine learning-based approach to study the effect of force-rate on recorded haptic responses (perceived and non-perceived) of 10 participants while varying the force-rate stimuli in the range [1,&#xa0;5] <i>N</i>/<i>s</i>. We determine the perceptual boundary between the perceived and non-perceived recorded responses using different classifiers based on linear and power functions of force-rate. The result indicates that the Weber fraction decreases significantly as the force-rate increases. The random forest classifier also confirms the significance of the utilized features in both perceptual boundaries. These findings may be useful in many virtual reality applications and telepresence and teleaction systems (TPTA).</p>