Adaptive HMI Control for Cognitive Food Response Training
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Solution Overview
Problem
Conventional cognitive training methods for modifying perceived value and behavioral responses to physical objects, such as the Go/NoGo task and Cued Approach Training, are limited by short training sessions, lack of individual adjustments, and non-engaging task environments, and do not fully control for user expectations, leading to minimized efficacy and adherence issues.
Innovation Solution
A human machine interface (HMI) technique that controls cognitive training by repeatedly rendering physical objects associated with predefined criteria, monitoring user inputs, and updating performance metrics to reinforce declination of unhealthy food items and predilection for healthy food items, using a combination of Go/NoGo and Cued Approach Training tasks with adaptive difficulty and personalized settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional cognitive training tasks (Go/NoGo, CAT) are used with short training sessions and limited individual adjustments, then the task structure is simple and easy to implement, but the training efficacy and user adherence are minimized
Solution Approach 1:
The patent implements dynamic task parameters that adapt to user performance in real-time. The system adjusts difficulty levels, time limits, and stimulus presentation based on measured performance metrics, transforming static conventional tasks into dynamic adaptive training environments that maintain optimal challenge levels and sustain user engagement throughout the training session
Solution Approach 2:
The system employs parameter changes by modifying task characteristics such as response time windows, stimulus intensity, and feedback timing based on user performance. These parameter adjustments optimize training efficacy by keeping tasks within the user's optimal performance zone while progressively challenging cognitive capabilities, thereby improving adherence without excessive complexity
2Ease of operation
If conventional cognitive training uses non-engaging task environments, then the implementation is straightforward, but user adherence and motivation are reduced
Solution Approach 1:
The patent incorporates immediate performance feedback mechanisms that provide users with real-time information about their cognitive performance. The system delivers feedback through score updates, performance comparisons, and progress tracking, which enhances user engagement and motivation while maintaining straightforward task implementation through automated feedback loops
Solution Approach 2:
The system employs periodic action through structured training sessions with varying stimulus presentations and response requirements. By alternating between different task conditions and providing periodic performance assessments, the system maintains user interest and adherence while keeping the overall task structure simple and manageable
3Device complexity
If conventional cognitive training does not control for user expectations, then the task design is simpler, but causal inferences on training effectiveness are confounded
Solution Approach 1:
The patent applies preliminary action by implementing control conditions and baseline measurements before the actual training intervention. The system establishes control groups, pre-tests cognitive performance, and sets expectation benchmarks prior to training, enabling precise measurement of training effectiveness by comparing post-training outcomes against these pre-established baselines while maintaining manageable complexity through systematic pre-planning
Data Source
AI summary
A technique for controlling a human machine interface is provided. A first task includes outputting a predefined criterion applicable to each of a plurality of physical objects. Each object is associated with a category within a group of at least pairwise disjoint categories with the criterion fulfilled for each object in a first category and not fulfilled for each object in a second category. Controlling the HMI for the first task includes repeatedly performing the steps of rendering an object; monitoring the HMI for an input during a predefined first time period after the rendering of the object; and updating a first metric indicative of a performance measurement in the first task. The method further comprises rendering a plurality of receptacles each enclosing one of the objects for a second task.