Wearable Product Recommendation Modulation via Inertial Sensing
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Solution Overview
Problem
Current wearable devices do not effectively address the negative health impacts of prolonged sitting by providing personalized recommendations for consumable products based on user behavior, despite recognizing the importance of both physical activity and dietary choices.
Innovation Solution
A wearable device equipped with inertial sensors, a product code reader, and a processor that modulates product recommendations based on the user's sitting behavior, using nutritional data to adjust the recommendations and provide visual feedback through colored indicators, thereby encouraging healthier choices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If product recommendations are provided based on general physical activity data, then users receive basic health guidance, but the recommendations do not account for the specific negative health impacts of prolonged sitting
Solution Approach 1:
The system dynamically adjusts product recommendations based on real-time detection of sitting periods. The processor continuously monitors inertial sensor data to identify when the user transitions into a sedentary state, and automatically modulates recommendations accordingly - restricting high-calorie food recommendations during detected sitting periods while allowing them during active periods.
Solution Approach 2:
The system implements closed-loop feedback by using inertial sensors to detect sitting behavior, processing this data to determine sedentary periods, and then using this information to modulate product recommendations. The visual indicator provides immediate feedback to the user about whether their current sitting state affects product recommendations.
2Reliability
If the device provides detailed behavior-based product recommendations, then user health guidance is improved, but the device requires multiple sensors and processing capabilities increasing complexity
Solution Approach 1:
The inertial sensors serve multiple functions: they detect sitting periods, monitor physical activity levels, and provide data for both immediate product recommendation modulation and longer-term health trend analysis. This multi-functionality reduces the need for separate specialized sensors for each monitoring task.
Solution Approach 2:
The processor automatically identifies sitting periods by analyzing inertial sensor data without requiring manual user input or intervention. The system self-calibrates by learning user movement patterns and automatically determines when sedentary periods begin and end, reducing the need for complex setup procedures.
3Speed
If the device modulates recommendations in real-time based on sitting detection, then health guidance timeliness is improved, but processing and response time requirements increase
Solution Approach 1:
The system uses periodic sampling of inertial sensor data at optimized intervals to detect sitting periods. Rather than continuously processing every sensor reading, the processor evaluates motion data at predetermined time intervals, reducing computational load while maintaining accurate detection of sedentary state transitions.
Data Source
AI summary
A wearable device comprising: a memory configured to store product codes for consumable products and data indicating respective product recommendations or from which product recommendations can be derived; a product code reader for reading product codes from products; one or more inertial sensors for obtaining motion data for a wearer of the device; a visual indicator for providing a visual indication of a product recommendation, using data stored in the memory, in response to a read product code. The wearable device further comprises a processor configured to process the motion data to identify periods when the wearer is in a sitting position or other sedentary state, analyze the occurrence and durations of the periods, and modulate the recommendations accordingly for at least a subset of the product codes, whereby product recommendations change depending upon the identified periods.