A Smart Ventilation Machine:
Analyzing Data to humanizing data
TEAM: Shib Sahoo Shankar, Martina Eriksson
ROLE: Research, synthesis, ideation, prototyping, documentation (photographer + videographer)
DURATION: 10 weeks
Student Design Award, Tangible Embedded and Embodied Interaction Conference, Stockholm Sweden
SIDeR 2018, Student Interaction Design Research Conference Paper Presentation, Aalto, Finland.
The Concept :
Tangible Data Manipulation
Aero is a smart anesthesia ventilation machine that assists the nurse to monitor the patient, make assessments, and perform individualized care during surgery. Aero integrates data physicalization with tangible manipulation, and translates the patient data into a dynamic form by simulating patient breathing frequency and lung condition.
Data's loss of intrinsic meaning
When something as subjective as breathing is translated into numbers, its qualitative meaning is diluted. The current ventilation machine's GUI limits the capacity of the nurse to perceive the intrinsic meaning of that data that is associated with the patient. The GUI also fails to utilize the full interaction potential between human and machine. Furthermore, anesthesia nurses currently use statistical estimations and visual cues to set the patient's ventilation parameters, which can lead to cognitive overload, and avoidable partial lung collapse during pre-surgery intubation.
We address the current GUI’s limitations by converting patient data to a more comprehensive and physical form. Through this expressive mode of data translation, ventilation can be performed through tangible manipulation which also enables the anesthesia nurse to connect with their patient. Breathing graphs are translated in real time into dynamic forms that respond to direct interaction with the nurse.
Umea General Hospital; ICU, operating room
Umea Emergency Station; Ambulance
Uppsala University Hospital; operating room
Initial preparation of anesthesia/ventilation procedure timeline
Digestion of research, operating room observations
Understanding net processes and relations in OR system
Filling up the Holken with research!
How is sleep depth and pain measured during surgery?
Mapping areas of interest with in emergency anesthesia + ventilation
After synthesis, we divided our research into three areas of interest
Identifying areas of
problem or potential:
After co creation and validation workshops and interviews, we took a deep dive into the topic of ventilation, identifying the following 5 areas. This helped us to determine the trajectory of our first ideation.
How might we reduce cognitive overload, optimize human machine interaction, and connect the nurse to the patient?
Dual Interface Development:
Quantitative versus Qualitative Data
Mapping Manipulated, collected, and communicated data in the ventilation process.
Identifying qualitative and quantitative data.
material, Data expression, and Manipulation
Visual projection of anesthesia gas and oxygen flow within the lung.
Can this give the anesthesia nurse qualitative information that accurately shows air distribution in the lungs?
Abstracted Fabric Lung
Responsive lung mimics respiration rate, and experiments with expression of movement.
Can this physicalized lung accurately represent patient lung condition? How can is respond to touch?
Tangible resistance to simulate lung pressure and stiffness and give haptic feedback.
Can this method of tactile feedback give correlation between the data and patient to the nurse? Is there appropriate control through this direct manipulation?
Sketching form and movement
Electronic + mechanical Components
Incorporating Servo into prototype
Pressure sensitive lighting
Refinement of Concept Direction + Hybrid Fusion of Prototypes
Mapping GUI and TUI relations, interactions.
Sketching of GUI architecture.
Hybrid Systems Thinking:
How does Aero integrate into the ventilation process?
GUI versus TUI:
How Aero Distributes and Expresses Data
TUI Lung Pressure Expression
Aero's tangible user interface alerts nurse to patient lung condition through both physical form and color response.
Normal Lung Pressure
Low Lung Pressure
High Lung Pressure