## 4 Discussion  
There is large agreement that the safety monitoring of workplaces and COPD requires the monitoring of environmental, behavioral, and physiological parameter [68; 69]. However, the majority of scientific work in mobile health and safety monitoring is limited to one at most two of these domain [2; 46]. Furthermore, the authors still use decentralized data collection and processing systems [47; 49]. Usually, they integrate sensors into wearables and combine with commercial devices (mostly for vital signs) to enhance the range of monitored parameters [30; 50]. In addition, many existing platforms offer data collection with several single-parameter devices integrated into a wireless body area network (WBAN) [70], and in some cases (unavoidable) stationary medical equipment [71]. However, there are common concerns regarding the practical application of these platforms in terms of convenience, economic, reliability, long-term usage, user-friendly, pervasive monitoring, and general performance [72]. The wrist-worn prototype proposed in this work introduces an innovative hardware solution with the 3D MPL approach. We intend to provide COPD patients an efficient companion for multi-parameter monitoring and real-time data transmission. However, patients can use our prototype also within sensor platforms [73; 74]. Nevertheless, with respect to the convenient wearability of the prototype, it can be applied for safety monitoring of workers in construction sites without interfering in the daily working routine and reducing the maneuverability. In comparison with the state-of-the-art in safety [2; 30] and COPD monitoring devices [49; 50], the proposed device improves wearability and increases the dimensions of monitoring (Table 9). Indeed, for the first time, we consider environmental monitoring and combine it with behavioral and physiological domains, all embedded into a lightweight wrist-worn device. Despite work that focuses on monitoring via \begin{table}
\begin{tabular}{l c c} \hline \hline
**Sensors and Sources of Consumption** & \(I_{\text{min}}\) **[mA]** & \(I_{\text{max}}\) **[mA]** \\ \hline A + C & 4.4 & 4.9 \\ A + C + I & 5.4 & 5.9 \\ A + B + C + K & 6.2 & 8.4 \\ A + B + C + E & 8.8 & 12.9 \\ A + B + C + G & 12.3 & 13.5 \\ A + B + C + H & 9.6 & 11.1 \\ A + B + C + D & 7.7 & 7.9 \\ A + B + C + E & 28 & 30.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Estimated maximum and minimum current consumption. distributed approaches and WBAN [29; 50] or integration of commercial devices [30; 49], we concentrate on standalone and independent monitoring with central data processing. The design supports convenient (wrist-worn) monitoring without imposing difficulties from weight [46], wire based communication [49], and interfering the users routine [50]. Moreover, our prototype supports sensor (de-) activation. This feature allows patient (user)-specific customization, for example, turning from single to multiple hazardous gas monitoring. Thus, there is bi-directional interaction of smartphone (as the gateway) and the device. In the long term, medical caregivers will configure the device. We briefly discuss the interpretation of the results and experimental tests. The output results of Section 3.1 shows a large range of loudness swinging. The respective sensor was set to high sensitivity to detect any changes (record the peaks) but taking the average changes into consideration and comparing with the delivered protocol, indicate the mode of transportation changes. Performing this experiment in a coastal city and large difference in humidity and temperature within a limited period of test is considered as the travel direction (e.g., toward sthe coast or in opposite direction) and the fine performance of the prototype. The specified resolution, response time, and recovery time of the prototype with the respect to air pollutants, depend on concentration of the exposed target gases.