Variability in human walking depends on individual physiology, environment, and walking task. Consequently, in the field of wearable robotics, there is a clear need for customizing assistance to the user and task. Here, we developed a muscle-based assistance (MBA) strategy wherein exosuit assistance was derived from direct measurements of individuals’ muscle dynamics during specific tasks. We recorded individuals’ soleus muscle dynamics using ultrasonographic imaging during multiple walking speeds and inclines. From these prerecorded images, we estimated the force produced by the soleus through inefficient concentric contraction and designed the exosuit assistance profile to be proportional to that estimated force. We evaluated this approach with a bilateral ankle exosuit at each measured walking task. Compared with not wearing a device, the MBA ankle exosuit significantly reduced metabolic demand by an average of 15.9, 9.7, and 8.9% for level walking at 1.25, 1.5, and 1.75 meters second−1, respectively, and 7.8% at 1.25 meters second−1 at 5.71° incline while applying lower assistance levels than in existing literature. In an additional study (n = 2), we showed for multiple walking tasks that the MBA profile outperforms other bioinspired strategies and the average profile from a previous optimization study. Last, we show the feasibility of online assistance generation in a mobile version for overground outdoor walking. This muscle-based approach enables relatively rapid (~10 seconds) generation of individualized low-force assistance profiles that provide metabolic benefit. This approach may help support the adoption of wearable robotics in real-world, dynamic locomotor tasks by enabling comfortable, tailored, and adaptive assistance.