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Last modified by Tjalling Haije on 2025/09/15 08:49
From version 6.1
edited by Rosa Van Tuijn
on 2025/07/08 11:11
Change comment: There is no comment for this version
To version 10.1
edited by Tjalling Haije
on 2025/09/15 08:49
Change comment: There is no comment for this version

Summary

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1 -XWiki.RosaVanTuijn
1 +XWiki.TjallingHaije
Content
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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 -
5 5  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.
6 6  
7 7  = 2. Method =
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18 18  
19 19  == 2.2 Experimental design ==
20 20  
21 -The table below shows the start questions of every questionnaire:
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:
22 22  
23 23  |(% colspan="2" %)Participant information
24 24  |Q: What type of partner are you?|(((
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164 164  
165 165  == 2.3 Tasks ==
166 166  
165 +Participants were asked to:
167 167  
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 +
168 168  == 2.4 Measures ==
169 169  
174 +The study measured:
170 170  
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 +
171 171  == 2.5 Procedure ==
172 172  
183 +During the field test in Greece:
173 173  
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 +
174 174  == 2.6 Material ==
175 175  
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.
176 176  
177 177  = 3. Results =
178 178  
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.
179 179  
180 -= 4. Discussion =
181 -
182 -
183 -= 5. Conclusions =
184 -
185 185  **Health data**
186 186  
187 187  Types of health data: heart rate, respiratory rate, body temperature, blood pressure, and mental health were frequently mentioned as essential.
188 188  
189 -Reasoning given for roles
205 +Reasoning given for how this information is important for a specific role:
190 190  
191 191  * Team Lead - Important for monitoring the overall safety of teams.
192 192  * Medical Personnel - Essential for making critical decisions.
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193 193  * Paramedic (Operational) - Necessary for directly treating team members.
194 194  * First Responder - Relevant for personal health and well-being.
195 195  
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.
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.
197 197  
198 198  
199 199  **Location data**
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200 200  
201 201  Types of Location data: Location data such as GPS coordinates, building heights, and paths to victims were frequently mentioned.
202 202  
203 -Reasoning given for roles
219 +Reasoning given for how this information is important for a specific role:
204 204  
205 205  * Team Lead - Essential for team coordination.
206 206  * Squad leader (Operational) - Necessary for instructing team members.
207 207  * First Responder - Helps with orientation and finding victims.
208 208  
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.
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.
210 210  
211 211  
212 212  **Communication data**
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213 213  
214 214  Types of communication data: Respondents emphasized the importance of RSSI (signal strength), signal speed, and interference detection.
215 215  
216 -Reasoning given for roles
232 +Reasoning given for how this information is important for a specific role:
217 217  
218 218  * Team Lead - Important for monitoring team connectivity.
219 219  * IT Specialist - Crucial for troubleshooting.
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220 220  * Squad leader (Operational) - Relevant for field communication.
221 221  * First Responder - Only needed for personal connectivity.
222 222  
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.
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.