Route Accessibility for Mobility Assistive Technology Users
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Solution Overview
Problem
Conventional route generation methods fail to provide personalized and optimized routes for mobility impaired individuals using mobility assistive tools, as they do not adequately consider contextual factors such as user cognitive and affective states, environmental conditions, and location-specific challenges.
Innovation Solution
A system that maintains a database of route difficulty levels and utilizes machine learning algorithms to predict physical exertion required for mobility assistive tools, selecting and prioritizing routes based on user data from devices like smartwatches and mobile phones, incorporating contextual factors like weather and crowd pressure to optimize route recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional route generation methods are used, then route recommendations can be provided, but they fail to account for user-specific contextual factors such as cognitive and affective states, environmental conditions, and location-specific challenges
Solution Approach 1:
The system dynamically adjusts route recommendations based on real-time changes in user contextual factors. The route generation algorithm continuously incorporates updated user data (cognitive state, affective state, environmental conditions) to adapt the recommended route, ensuring it remains optimized for the user's current situation rather than using a static pre-planned path.
Solution Approach 2:
The system implements feedback loops where user responses, device data from smartwatches and mobile phones, and environmental sensor information are continuously fed back into the route generation algorithm. This feedback mechanism allows the system to learn from actual user behavior and environmental conditions to refine future route recommendations.
2Ease of operation
If routes are selected based solely on distance and time, then routing is simple and fast, but physical exertion and accessibility for mobility impaired users are not optimized
Solution Approach 1:
The system changes the parameters used for route evaluation from traditional distance and time metrics to include accessibility parameters specific to mobility impaired users. The algorithm considers factors such as terrain difficulty, elevation changes, surface conditions, and proximity to accessible facilities, transforming the optimization criteria to better serve the target user population.
Solution Approach 2:
The system performs preliminary analysis of route accessibility and physical exertion requirements before presenting recommendations to the user. By pre-calculating and filtering routes based on accessibility criteria and estimated exertion levels, the system reduces the computational burden during actual route selection and presents only the most suitable options.
3Measurement precision
If detailed user data from multiple devices is collected and analyzed, then personalized route optimization is achieved, but system complexity and data processing requirements increase
Solution Approach 1:
The system employs a universal data processing framework that handles multiple types of user data from various devices (smartwatches, mobile phones, environmental sensors) through a single integrated architecture. The same core algorithms process diverse data types (physiological metrics, location data, environmental conditions) to generate unified route recommendations, reducing the need for separate specialized processing systems.
Data Source
AI summary
A database comprising data associated with one or more routes is maintained. The data associated with the one or more routes comprises difficulty level data for utilizing one or more mobility assistive tools to traverse the one or more routes. In response to receiving a query from a given computing device, one or more amounts of physical exertion for a given user to traverse at least a portion of the one or more routes utilizing a given mobility assistive tool are predicted. One or more routes for the given user to traverse are selected based at least in part on the predicted amounts of physical exertion. One or more contextual factors of the given user are estimated to at least one of optimize and prioritize the selected one or more routes for the given user based on analyzing user data.