Patients' self-report is the most common method for pain assessment. However, while self-reports are often convenient and useful, they have a number of limitations, including being highly subjective, inconsistent and cumbersome to obtain in long-term large-scale studies. Furthermore, they cannot be obtained reliably from the mentally impaired and other vulnerable populations, such as children and elderly people. Therefore, there is an ever-growing need for reliable automatic pain assessment methods as the means for detecting pain, and for evaluating and comparing the effectiveness of different pain reduction strategies. In this work, we present a novel pain intensity estimation method that uses multi-modal data from a wrist-worn sensor. Our algorithm combines different autonomic activity metrics derived from electrodermal activity and plethysmogram wrist signals to estimate the intensity of nociceptive stimulii. We verify the robustness of our algorithm in a single-center, comparative, randomized, crossover, clinical study that evaluates the impact of different injection parameters on subcutaneous injection pain tolerance.