The work presented in this paper compares the performance of different machine learning approaches, based on spectral and statistical features, in identifying coughs from accelerometry data sensed via a wearable attached to a shirt’s collar. The extracted features are separately used to train and evaluate neural network models for cough detection - first as a multi-class problem, second as a binary problem. The models’ performance was compared in terms of overall accuracy, cough sensitivity, specificity, and F1-score. It is concluded that the model using statistical features for a multi-class cough detection achieved the best cough sensitivity of 100% and F1-score of 0.95 with cough specificity of 97.3%. The four classification models were further evaluated for on-board performance as a TinyML cough system. They were all successfully deployed on Nordic Thing:53 using Edge impulse, and their memory requirements and estimated time per inference are reported. In terms of memory and time, the statistical- multi-class model was the smallest model occupying 13.7 KB of Flash memory and 1.1 KB of RAM; it was also the fastest model, requiring an estimated 1 ms per inference.