This letter addresses the problem of human activity recognition (HAR) of people wearing inertial sensors using data from the UCI-HAR dataset. We propose a light residual network, which obtains an F1-Score of 97.6% that outperforms previous works, while drastically reducing the number of parameters by a factor of 15, and thus the training complexity. In addition, we propose a new benchmark based on leave-one (person)-out cross-validation to standardize and unify future classifications on the same dataset, and to increase reliability and fairness in the comparisons.