"Wheelchair Wellness" is a smartphone app that uses your phone's sensors to track how you use your manual wheelchair, including distance and impacts. It creates a "usage index" to predict breakdown risks, helping users and suppliers schedule timely, personalized maintenance. This aims to reduce breakdowns, injuries, and healthcare costs.
Wheelchair users frequently experience breakdowns, with over half encountering an incident every six months, often resulting in injuries, loss of access to essential activities, increased hospitalization risks, and higher healthcare costs due to prolonged repair times. While routine maintenance can mitigate these issues, current approaches are ineffective because existing maintenance schedules are generic and fail to account for actual wheelchair usage patterns or environmental conditions. There is a critical need for systems that can monitor real-world usage to enable personalized, evidence-based preventive care, as current monitoring solutions, particularly for manual wheelchairs, are limited to basic metrics like distance traveled and do not capture the specific usage conditions, such as impacts and wear, that directly contribute to damage and necessitate timely repairs.
Description
1. The system is a mobile application for manual wheelchair users that employs smartphone GPS and accelerometer sensors. It collects data on both distance traveled and specific usage conditions, such as impacts and wear, which contribute to wheelchair damage. This information is then processed to create a "wheelchair usage index" that quantifies breakdown risk. This unique approach allows for the prediction of maintenance needs by capturing detailed usage conditions, a capability not present in existing manual wheelchair monitoring technologies.
2. Use of a smartphone accelerometer to quantify manual wheelchair usage conditions: This appears to be novel because the provided text states that existing monitoring for manual wheelchairs is limited to research sensors that only measure distance. This invention specifically applies smartphone accelerometer data to capture conditions like impacts and wear that contribute to damage, which, according to the disclosure, are "not currently monitored by other technologies." This builds on prior dissertation research focused on using accelerometer data to quantify wheelchair caster durability.
A composite "wheelchair usage index" for predicting breakdown risk: The creation of a single index that synthesizes both distance traveled (from GPS) and the specific conditions of use (from an accelerometer) appears to be a new concept. This index is designed to provide a holistic measure of the stress and wear on a manual wheelchair, moving beyond simple distance tracking to create a more accurate basis for maintenance predictions.
3. A predictive maintenance system for manual wheelchairs: While predictive maintenance exists in other fields and diagnostic apps exist for power wheelchairs (monitoring batteries and motors), the application of a predictive model to forecast maintenance needs for manual wheelchairs based on real-world usage data appears to be novel. The disclosure contrasts this with current "generic" maintenance schedules and notes that "no app exists for manual wheelchair users to monitor usage and predict maintenance needs" in this manner.
Applications
1. Proactive Maintenance and Service Management: This system enables personalized, evidence-based maintenance schedules for individual users, repair services, and fleet managers, significantly reducing wheelchair breakdowns and associated costs.
2. Product Design, Selection, and Quality Improvement: Real-world usage data can inform manufacturers in designing more durable wheelchairs, guide providers in selecting appropriate models, and establish more realistic testing and warranty standards.
3. Insurance Risk Assessment and Cost Reduction: Insurers can leverage the wheelchair usage index to assess risk more accurately, potentially influencing policy terms and reducing healthcare expenditures linked to preventable equipment failures.
4. Clinical Care and Patient Management: Healthcare providers can utilize the monitoring data to proactively manage patient equipment, ensuring functional mobility and preventing adverse health outcomes due to wheelchair malfunctions.
Advantages
1. Personalized and Predictive Maintenance: Unlike **generic maintenance schedules**, the invention uses real-world usage data (distance, impacts, wear) to generate a "wheelchair usage index," enabling evidence-based, personalized maintenance schedules that predict breakdown risk.
2. Comprehensive Usage Data Capture for Manual Wheelchairs: Unlike **existing research sensors for manual wheelchairs** that only measure distance, this invention uniquely leverages smartphone GPS and accelerometer data to capture both distance traveled and critical usage conditions like impacts and wear, which directly contribute to damage.
3. Addresses an Unmet Need in Manual Wheelchair Monitoring: While **power wheelchair manufacturers (e.g., Permobil)** offer diagnostic apps for battery and motor issues, no existing app provides usage monitoring and predictive maintenance specifically for **manual wheelchair users**, filling a significant market gap.
4. Reduced Healthcare Costs and Downtime: By enabling timely, predictive maintenance, the invention can significantly reduce the frequency of costly wheelchair breakdowns, associated injuries, hospitalizations, and prolonged repair times (1-4 weeks), leading to lower healthcare expenditures and improved user productivity.
5. Enhanced Product Development and Standards: The real-world usage data collected by the invention can inform and improve **product selection, purchase planning, and wheelchair testing standards**, moving beyond laboratory conditions to reflect actual user environments and needs.
Invention Readiness
Design
IP Status
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