Forklifts are mobile heavy machines that are used to transport, lift, or lower objects without the high physical effort of the operator. They work in different types of industries such as logistics, retail, food, mining, and construction, among others. Qualified personnel usually operate the forklifts to handle heavy loads in an environment surrounded by other workers. This creates a high risk of accidents due to the lack of visibility with a loaded forklift, the random movement of the workers around the area and possible risk maneuvers sometimes required in a normal day of operation. For example, in Chile 2000 accidents occur per year due to one of the mentioned situations. For this reason, the detection of risk maneuvers to prevent accidents is essential. This article shows a cost-effective solution proposal to implement an inertial sensor network with a dedicated wireless communication and automatic deep-learning algorithms to detect forklift risk events. A test bench was designed where a crane forklift equipped with four inertial sensors performed normal and risky maneuvers, according to the Occupational Safety and Health Administration (OSHA) 3949. During the forklift operation, the sensors measured the accelerations and angular velocities in three axes. Videos of the operation were also taken as reference. In this paper, we developed convolutional neural networks (CNN) and long-term memory (LSTM) algorithms to infer a risky maneuver from the inertial sensors data and compared it to the outcome of a video-based model trained on data labeled by a risk-prevention engineer. After field testing with the forklift, the inertial data-based algorithms had an average F1 of 0.93 versus video analysis which had an average F1 of 0.95. However, models based on inertial data take a quarter of the time to make the inference compared to video-based models.