This paper explores the application of a deep neural network (DNN) framework to human gait analysis for injury classification. The paper aims to identify whether a subject is healthy or has an injury of the ankle, knee, hip, or heel solely based on ground reaction force plate measurements. We consider how three DNNs-the multi-layer perceptron (MLP), fully convolutional network (FCN), and residual network (ResNet)-can be applied to gait analysis when the number of trainable network parameters far exceeds the number of training samples, and benchmark their performance in this context against that of shallow neural networks. The DNN architectures outperformed unsupervised clustering models?self-organizing map and k-means clustering?by a large margin. When tested against support vector machines, which is considered the state-of-the-art approach for supervised gait classification, DNNs performed equal or better despite the propensity for overfitting. We did not find evidence that applying data augmentation to the overfitting problem via timeGAN, a generative adversarial network for time-series data generation, leads to improved classification accuracy. While DNNs are hypothesized to have intrinsic feature extraction capability, the results suggest an advantage to implementing an explicit feature extraction process as a frontend for future applications of deep neural networks: specifically, principal component analysis (PCA) preprocessing improved classification accuracy robustness in all models. In the absence of an explicit feature extraction layer, the use of dropouts in convolutional neural networks caused significant performance degradation; by contrast, the use of a PCA frontend and dropouts synergistically achieved the highest test accuracy among the DNNs studied.