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Last modified by Tjalling Haije on 2025/09/15 08:49
From version 9.1
edited by Tjalling Haije
on 2025/09/15 08:47
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To version 6.1
edited by Rosa Van Tuijn
on 2025/07/08 11:11
Change comment: There is no comment for this version

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1 -XWiki.TjallingHaije
1 +XWiki.RosaVanTuijn
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1 1  = 1. Introduction =
2 2  
3 +//<include a short summary of the claims to be tested, i.e., the effects of the functions in a specfic use case>//
4 +
3 3  The goal of this test was to understand what type of information would support each role at different levels (strategic, tactical, operational) in performing their tasks, particularly in decision-making. We focused mainly on the tactical and operational levels.
4 4  
5 5  = 2. Method =
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16 16  
17 17  == 2.2 Experimental design ==
18 18  
19 -The study used a role-based, exploratory design to assess how different types of sensor data (health, communication, location) support decision-making at tactical and operational levels. Five tailored questionnaires were developed, each focusing on a specific technology. The design emphasized gathering qualitative insights through open-ended questions and evaluating visualization preferences using example formats. An example of the question that were asked can be found below. The table below shows the start questions of every questionnaire:
21 +The table below shows the start questions of every questionnaire:
20 20  
21 21  |(% colspan="2" %)Participant information
22 22  |Q: What type of partner are you?|(((
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162 162  
163 163  == 2.3 Tasks ==
164 164  
165 -Participants were asked to:
166 166  
167 -* Describe how sensor data could support their role.
168 -* Identify specific data needs relevant to their operational or tactical responsibilities.
169 -* Evaluate various visualization formats (e.g., traffic lights, raw data, predictions).
170 -* Provide feedback on the clarity and usefulness of each visualization type.
171 -
172 172  == 2.4 Measures ==
173 173  
174 -The study measured:
175 175  
176 -* **Perceived usefulness** of different data types (e.g., heart rate, GPS).
177 -* **Role-specific data needs**, categorized by tactical, and operational levels.
178 -* **Visualization preferences**, including simplicity, detail, and trust in predictive models.
179 -* **Qualitative feedback** on visualization examples and their applicability in field scenarios.
180 -
181 181  == 2.5 Procedure ==
182 182  
183 -During the field test in Greece:
184 184  
185 -1. Participants were briefed on the goal of the study.
186 -1. Each participant received a questionnaire about one of the technologies that they could fill in with their role in mind.
187 -1. They completed the questionnaire individually, providing both structured and open-ended responses.
188 -1. During the questionnaire they could ask questions to the researchers for extra clarity.
189 -1. Visualization examples were shown to elicit preferences and feedback.
190 -1. Responses were collected and analyzed to identify patterns across roles and technologies.
191 -
192 192  == 2.6 Material ==
193 193  
194 -* **Five questionnaires made** created in Survalyzer and presented on a tablet.
195 -* **Visualization examples** included traffic light indicators, raw and aggregated data, predictive analytics, and advisory outputs.
196 196  
197 197  = 3. Results =
198 198  
199 -A total of 12 partners completed questionnaires during the field test in Athens. The health questionnaire was filled out by 5 partners, the communication questionnaire by 2 partners, and the location questionnaire by 4 participants. Although a questionnaire for the gas sensors (also by WEARIN’) was prepared, we decided not to focus on it in Athens since the gas sensor was not used during the exercises. The questionnaires were completed by individuals in various roles, including researchers, drone pilots, paramedics, incident commanders, chief SAR, and firefighters.
200 200  
180 += 4. Discussion =
181 +
182 +
183 += 5. Conclusions =
184 +
201 201  **Health data**
202 202  
203 203  Types of health data: heart rate, respiratory rate, body temperature, blood pressure, and mental health were frequently mentioned as essential.
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209 209  * Paramedic (Operational) - Necessary for directly treating team members.
210 210  * First Responder - Relevant for personal health and well-being.
211 211  
212 -Highlight: Health data is essential for a wide range of roles, but the requirements vary greatly. Medical personnel and paramedics request detailed and contextual data, while team leaders and first responders value summaries and simple alerts more. Transparency in predictive models is necessary to build trust.
196 +Conclusion: Health data is essential for a wide range of roles, but the requirements vary greatly. Medical personnel and paramedics request detailed and contextual data, while team leaders and first responders value summaries and simple alerts more. Transparency in predictive models is necessary to build trust.
213 213  
214 214  
215 215  **Location data**
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222 222  * Squad leader (Operational) - Necessary for instructing team members.
223 223  * First Responder - Helps with orientation and finding victims.
224 224  
225 -Highlight: Location data plays a crucial role in both tactical and operational decisions. Tactical team leaders want aggregated and sector-based data, while operational roles such as squad leaders and first responders need detailed and real-time information. 3D maps and interactive elements are valuable tools to improve navigation and coordination.
209 +Conclusion: Location data plays a crucial role in both tactical and operational decisions. Tactical team leaders want aggregated and sector-based data, while operational roles such as squad leaders and first responders need detailed and real-time information. 3D maps and interactive elements are valuable tools to improve navigation and coordination.
226 226  
227 227  
228 228  **Communication data**
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236 236  * Squad leader (Operational) - Relevant for field communication.
237 237  * First Responder - Only needed for personal connectivity.
238 238  
239 -Highlight: Communication plays a central role at all levels of USAR operations. Tactical users need extensive analyses to monitor team status, while operational roles such as IT specialists focus on technical troubleshooting. Advisory functions and visual simplicity could contribute to effectiveness in the field.
240 -
241 -= 4. Discussion =
242 -
243 -The study highlights the importance of tailoring sensor data visualization to user roles. Tactical users benefit from aggregated, strategic overviews, while operational users require detailed, real-time data. Visualization design must balance simplicity with informativeness, and predictive models must be transparent to build trust. These insights can guide future development of adaptive interfaces for USAR operations.
244 -
245 -= 5. Conclusions =
246 -
247 -The field test demonstrated that sensor data—when tailored to user roles and operational contexts—can significantly enhance decision-making in USAR operations. However, the type, granularity, and presentation of data must align with the specific needs of tactical and operational users.
248 -
249 -* **Health data** is universally valued but interpreted differently across roles. Medical personnel and paramedics require detailed, contextual information to make clinical decisions, while team leaders and first responders benefit more from simplified summaries and alerts. Trust in predictive health models hinges on transparency and clarity.
250 -* **Location data** is essential for both coordination and navigation. Tactical users prefer aggregated, sector-based overviews to manage teams, whereas operational users such as squad leaders and first responders need real-time, detailed data to orient themselves and locate victims. Tools like 3D maps and interactive visualizations are especially helpful.
251 -* **Communication data** supports both strategic oversight and technical troubleshooting. Tactical roles benefit from system-wide connectivity insights, while operational roles focus on individual and team-level communication. Simplicity in visualization and advisory features can improve usability and effectiveness in the field.
252 -
253 -Overall, the study underscores the importance of **role-specific data visualization** and the need for **adaptive interfaces** that balance detail with usability. Future developments should prioritize **clarity, trust, and contextual relevance** to ensure sensor data truly supports the diverse needs of USAR personnel.
223 +Conclusion: Communication plays a central role at all levels of USAR operations. Tactical users need extensive analyses to monitor team status, while operational roles such as IT specialists focus on technical troubleshooting. Advisory functions and visual simplicity could contribute to effectiveness in the field.