# An Action Classification Method for Forklift Monitoring in Industry 4.0 Scenarios
## 4 Experimental Analysis
### Comparison with the State-of-the-Art  
Each state-of-the-art solution presented in Section 2 requires a different and custom architecture, so it is difficult to make a fair comparison by evaluating the classification performance of other pre-existing solutions directly on-site with the same antenna configuration and dataset. In any case, we can compare the proposed system with the others analyzed in Section 2 in terms of cost, encumbrance, and scalability. The cost of a COTS RFID system at the UHF-RFID band is mainly determined by the RFID reader, which may reach more than 1000S (USD). Each RFID antenna costs around 100-200S (USD) and, therefore, can be a significant cost for solutions requiring multiple antennas. The cost of a passive RFID tag can be considered negligible for small volumes of goods, as RFID inlay labels usually cost less than 0.15 (USD). Some passive RFID tags designed for metallic surfaces can cost around 10-20S (USD) each, but there are many models that can be bought for less than 5S. Battery-Assisted Passive (BAP), active, or sensor-equipped tags can reach a  
Figure 15: Histogram of the processing time (ms) for the \(N_{T}=264\) analyzed trials. cost of 30%, but they are usually not necessary. Metallic supports or shields shall be included in the total cost of the system, and therefore, it turns out that shielded gates [16; 18] are quite expensive solutions due to the large infrastructure required. The encumbrance is relative to the global volume occupied by the hardware needed to implement the gate, which could be very significant in the case of shielded gates. Cost and encumbrance together usually impact the scalability of the solution since a high cost, or alternatively, a high encumbrance, makes the solution less replicable inside the plant, factory, or warehouse. The scalability of a solution is also determined by the time required for the installation process. For instance, mounting several antennas at the ceiling, mounting several shielded gates, or installing several photocells or ultrasound barriers in addition to the RFID hardware could be a time-consuming operation, which must be considered as a significant cost. Finally, solutions based on a machine learning classification algorithm could require a supervised training process, which can be difficult to achieve in a short time, and huge amounts of data have to be collected in several operating conditions. As summarized in Table 2, the solutions based on shielded gates [16; 18] have been considered as \"High Cost\", \"High Encumbrance\" and \"Low Scalability\" due to the cost of the metal shields, their volume, and the installation complexity. On the other hand, shielded gates are the best options to filter out false positive readings. By referring to [19], we opted for \"Medium-High Cost\", \"Medium-High Encumbrance\", and \"Low-Medium scalability\". Indeed, the proposed solution requires antennas aggregated in panels. The cost and the encumbrance are lower than the shielded gates, but the cost of the antenna panel is not negligible and must be considered when taking into account the system scalability. The systems proposed in [22; 23; 24] have been evaluated as \"Low-Medium Cost\", \"Low-Medium Encumbrance\" and \"Medium-High Scalability\". Indeed, the three systems require two antennas, which increase the cost with respect to solutions with a single antenna, and the encumbrance cannot be considered as \"low\", too, as it is required to find enough space for two antennas. On the other hand, the installation of two antennas is indeed a fast process, and therefore, the scalability of the solutions is good. The solutions in [26; 28] are based on phase processing, such as the one presented in this paper, and also require a single antenna. Therefore, they are classified as \"Low Cost\", \"Low Encumbrance\", and \"High Scalability\" [33] as they still rely on a single antenna, but the scalability is considered \"Medium\" as the proposed solution is based on a neural network classifier, which requires a time-consuming training stage.