# Wearables for Industrial Work Safety: A Survey
## 3 State-of-the-Art Techniques in the Field of Industrial Wearables
### Localization Techniques  
As mentioned, identifying the exact location of the objects is one of the most important functions performed by wearable devices. There are two types of objects in industries: machinery/equipment and personnel. Accurate positioning is key to preventing worker collision with moving machinery, exclusion of an opportunity of unauthorized access to hazardous work areas and equipment, successful evacuation, and efficient distribution of labor. However, positioning continues to be one of the most challenging problems for industrial wearable devices due to the nature of the workplace (e.g., underground, underwater) and, at the same time, high accuracy requirements. Moreover, employee location tracking also raises data ownership, security, and privacy questions, which will be explored in more detail in the next section. All location tracking techniques could be divided into two groups: methods depending on range and range-free techniques [146]. The first group considers the conversion of various parameters to the range. It comprises time-based measurements (Time of Arrival (ToA)), Time Difference of Arrival (TDoA)), angle-based measurements (Angle of \begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Category** & **Technology** & **Frequency** & **Max. Data Rate** & **Refs.** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Short-range \\ (\(<\)100 m) \\ \end{tabular} } & RFID & 0.86 GHz & 40 kbps & [132] \\ \cline{2-5}  & Zigbee & 868 MHz, 915 MHz, 2.4 GHz & 20 kbps, 40 kbps, 250 kbps & [125] \\ \cline{2-5}  & Bluetooth & 2.4 GHz & 1–3 Mbps & [123; 133] \\ \cline{2-5}  & BLE & 2.4 GHz & 125 kbps–2 Mbps & [123; 133] \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Mid-range \\ (100 m-5 km) \\ \end{tabular} } & IEEE 802.11b & 2.4 GHz & 1–11 Mbps & [123; 134] \\ \cline{2-5}  & IEEE 802.11g & 2.4 GHz & 6–54 Mbps & [123; 135] \\ \cline{2-5}  & IEEE 802.11n & 2.4/5 GHz & Up to 600 Mbps & [123; 135] \\ \cline{2-5}  & IEEE 802.11 ac & 5 GHz & Up to 1 Gbps & [123; 135] \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Long-range \\ (\(>\)5 km) \\ \end{tabular} } & LoRa & 915–928/863–870/433 MHz & 50 kbps & [136; 137] \\ \cline{2-5}  & Sigfox & 868/902 MHz & 100 bps & [129] \\ \cline{1-1} \cline{2-5}  & LTE & 3 GPP frequency bands & 100 Mbps & [130] \\ \cline{1-1} \cline{2-5}  & LTE-M & 3 GPP frequency bands & 1 Mbps & [138] \\ \cline{1-1} \cline{2-5}  & NB-IoT & 3 GPP frequency bands & 250 kbps & [139; 140] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Communication technologies utilized in industrial wearable applications. Arrival (AoA), Angle of Departure (AoD), power-based measurements (Received Signal Strength Indicator (RSSI), in connection with which path loss models are used). In the second group's schemes, for example, in the Distance Vector-Hop algorithm (DV-HOP), anchors broadcasting their location to the whole network, and unknown nodes estimate their location based on the proximity to these known anchors (hop size and hope count). Such algorithms can be used without any additional equipment [146]. The choice between these two groups is based on the trade-off between price and accuracy: range-based techniques provide high precision, but their application is quite expensive. Range-free techniques are usually considered a cheaper and less precise alternative to the first group. To choose the localization solution for a particular case, one should review such parameters as the environment (outdoor/indoor), coverage, power consumption, scalability, price, and accuracy. The first question is what environment is more typical for industrial cases: outdoor or indoor. This question is essential since the different conditions dictate different approaches for these two cases [146]. While indoors is the scatter-rich environment where the path loss model's prediction is a big challenge, outdoors usually have Line-of-Sight. For the same reason, achieving high accuracy in the first case is much more complicated than in the second.