Orthotic support and stimulus systems and methods
Inventors
Assignees
Publication Number
US-9734296-B2
Publication Date
2017-08-15
Expiration Date
2031-09-29
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Abstract
An embodiment of the invention includes (a) modeling a first internal force applied to a model of a user's joint based on a first external force externally applied to the joint at a first position; (b) modeling a second internal force applied to the model based on a second external force externally applied to the joint at a second position unequal to the first position; (c) comparing the first and second modeled internal forces; and (d) simulating the user based on the comparison. Other embodiments are described herein.
Core Innovation
The invention provides a system and method using a physical model of a user's joint to determine internal force responses resulting from external forces applied during movement. It measures external motion or position states using sensors, conditions these signals, and applies them to a structural model of the joint. This allows the system to calculate modeled internal force vectors corresponding to different joint activities.
Unlike conventional systems that rely solely on pre-defined pattern templates or direct external measurements, the invention captures and compares internal joint forces under differing conditions. By associating user-identified undesirable states, such as pain or instability, with force model outputs, the system can stimulate muscle activity to protect the joint. This enables adaptive protection based on real-time or historical data, improving over static template-driven approaches.
The invention also incorporates a finite state machine that dynamically selects optimal measurement parameters for state definition, based on observed or modeled data with the highest relevance or deviation at the time of a significant event. This approach allows personalized response to both familiar and previously unencountered conditions through real-time modeling and data-driven state transitions, supporting robust control in environments with significant variability.
Claims Coverage
The patent contains one independent claim outlining several inventive features of the system.
Modeling internal joint forces from conditioned sensor measurements
The system captures measurements of limb movement or position at a user's joint using at least one sensor and conditions these signals with a signal conditioner. It then models a first internal force on the joint based on a measurement at a first time and a second internal force based on a different measurement at a second time, using a finite state controller.
Comparison and control based on modeled internal forces
A processor and comparator compare the first and second modeled internal joint forces. Based on this comparison, the system stimulates muscle via an electrode—the stimulation being triggered by detecting modeled internal force conditions associated with undesirable states.
The inventive features focus on using conditioned sensor inputs to generate joint force models, followed by comparison of these models to inform muscle stimulation for joint protection.
Stated Advantages
The system facilitates robust and accurate control in both previously-observed and completely new operational conditions by incorporating modeled internal joint responses into state decision criteria.
Adaptive protection is achieved by associating stimulation control directly with modeled internal joint forces, reducing the need for exhaustive pre-defined pattern templates and enabling response to individual and contextual variability.
The approach greatly improves system response by decoupling internal joint force detection from solely external position or movement measurements, thus offering more precise and personalized joint support.
Significant reduction in training hazardous or numerous conditions, as the system learns and remembers states marked undesirable by the user, even if caused by distinctly different activities.
Documented Applications
Orthopedic muscle stimulation devices for joint protection, such as knee support based on force modeling applied during various movements.
Vehicle stability systems, including golf carts, where the system reduces the likelihood of rollover through adaptive control of steering ratio based on modeled vehicle dynamics and terrain.
Dynamic damping and stability enhancement for devices such as pogo sticks, where rider state and stick dynamics are modeled for real-time damping adjustment and safety.
Use with animal (including human) joints, such as elbow, finger, toe, ankle, hip, shoulder, neck, wrist, and back/spine, including applications coupling stimulation systems with braces or prostheses.
Potential use in non-physical applications like data flow control, where state machine architectures benefit from adaptive state definition based on modeled system responses.
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