Background: Individuals with type 1 diabetes (T1D) require close glucose monitoring to prevent both short- and long-term complications. Physical activity (PA) is a significant source of variability in metabolic dynamics, leading to glycemic fluctuations that depend on the type, intensity, and duration of the exercise. Accurately monitoring and classifying the type of PA is crucial for optimizing glycemic control and minimizing the risk of hypoglycemia. Method: This study utilizes the largest clinical trial of PA in people with T1D to date, the Type 1 Diabetes and Exercise Initiative (T1DEXI), which included both structured and unstructured PA sessions, to develop an online classification approach for identifying the type of PA (aerobic, interval, resistance). A computationally efficient convolutional neural network (CNN) was trained on time–frequency representations (spectrograms) of step count and heart rate signals, readily available from wearable devices, from the structured PA sessions of the T1DEXI dataset. The proposed methodology presents an ad hoc process for designing the spectrograms based on the CNN architecture to optimize the classifier’s performance. Results: The CNN-based classification approach was implemented using spectrograms of 5- and 30-min signals, resulting in two classifiers that achieve high classification accuracy when evaluated on the structured PA sessions. The 5-min classifier was then applied to unstructured PA sessions, where the predicted distribution of glucose changes for the activity types was consistent with clinical evidence. Conclusion: These results demonstrate the potential of the proposed approach for its integration into decision support systems or automated insulin delivery systems, enabling improved glucose management during exercise in T1D.