Changes for page b. Test: CFT1: Sensor data visualization

Summary

• Page properties (1 modified, 0 added, 0 removed)

Details

Page properties

Content

...	...	@@ -18,7 +18,7 @@
18	18
19	19	== 2.2 Experimental design ==
20	20
21		-The table below shows the start questions of every questionnaire:
	21	+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?|(((
...	...	@@ -164,24 +164,40 @@
164	164
165	165	== 2.3 Tasks ==
166	166
	167	+Participants were asked to:
167	167
	169	+* Describe how sensor data could support their role.
	170	+* Identify specific data needs relevant to their operational or tactical responsibilities.
	171	+* Evaluate various visualization formats (e.g., traffic lights, raw data, predictions).
	172	+* Provide feedback on the clarity and usefulness of each visualization type.
	173	+
168	168	== 2.4 Measures ==
169	169
	176	+The study measured:
170	170
	178	+* **Perceived usefulness** of different data types (e.g., heart rate, GPS).
	179	+* **Role-specific data needs**, categorized by tactical, and operational levels.
	180	+* **Visualization preferences**, including simplicity, detail, and trust in predictive models.
	181	+* **Qualitative feedback** on visualization examples and their applicability in field scenarios.
	182	+
171	171	== 2.5 Procedure ==
172	172
	185	+During the field test in Greece:
173	173
	187	+1. Participants were briefed on the goal of the study.
	188	+1. Each participant received a questionnaire about one of the technologies that they could fill in with their role in mind.
	189	+1. They completed the questionnaire individually, providing both structured and open-ended responses.
	190	+1. During the questionnaire they could ask questions to the researchers for extra clarity.
	191	+1. Visualization examples were shown to elicit preferences and feedback.
	192	+1. Responses were collected and analyzed to identify patterns across roles and technologies.
	193	+
174	174	== 2.6 Material ==
175	175
	196	+* **Five questionnaires made** created in Survalyzer and presented on a tablet.
	197	+* **Visualization examples** included traffic light indicators, raw and aggregated data, predictive analytics, and advisory outputs.
176	176
177	177	= 3. Results =
178	178
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.
...	...	@@ -193,7 +193,7 @@
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**
...	...	@@ -206,7 +206,7 @@
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**
...	...	@@ -220,4 +220,18 @@
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.