Study Design, Experiment Design
Data Collection Strategy, Data Integration
Signal Conditioning, Signal Processing
Information Analysis, Feature Selection, Feature Validation
We offer coordination and quality control for non-invasive biosensor development projects from conceptual planning through the prototyping phase to the market ready product level.
What we do
DataSenseLabs specializes in biomedical R&D, bioinformatics, and metrology services. Our core services and expertise cover biosensor testing and validation, health IT, algorithm development and validation, experimental and statistical study design, complete design and process control of remote monitoring applications based on biosensor networks.
Our clients include HAS (Hungarian Academy of Sciences), biosensor manufacturer and application developer companies.
The quality and usability of your data will be determined by the below mentioned three factors: design of experiment, data collection strategy and data integration.
Study Design or Design of Experiment
This is the point where we establish the foundations of the whole workflow: either it is a research related scientific project or it is a technical development related engineering project
Data Collection Strategy
When we have a specific use case at the conceptual level, the next core of the foundation is how and why we collect the data. This strategy will determine the possible outputs of the classification algorithms at the Information layer.
Properly synchronized and labelled data with the purpose oriented conceptual design will lead to effective information analysis. Data integration layer is also one of the core elements where we optimize the why – what – how elements of the whole project.
Signal is a specific element that can be handled by specific experts within the workflow. This is the key connection between the data and information layer.
In this specific step we pull the signal up from the data level to clean and pre-process for further analysis.
After this step our signals will be fine tuned for the Different layers of the information analysis.
Information analysis is “the why” we started the whole project for. Decisions made within this layer will have a final impact on the project and will also accelerate further development and innovation.
Since we have a clean and well established foundation at the data and signal level, our analytics methods will generate conclusive outputs to support decisions efficiently and accurately.
The output of the feature validation layer will decide if we can move forward to the information analysis layer or we must change or improve specific fields within the signal or data layers of the workflow.
Feature selection is the soul of the whole information layer. This is always an interdisciplinary field of application depending on the specific question – either the purpose of the project is an R&D or business intelligence related application.