AI Robotic Physical Therapy for Real-Time Stimulus-Response Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional physical therapy relies heavily on human specialists, leading to inefficiencies, inconsistent treatment quality, and insufficient resources, with minor patient responses often going unnoticed, and there is a need for a more effective and resource-efficient solution.
Innovation Solution
An AI-based robotic system that provides physical therapy, capable of adjusting treatments based on real-time user responses, generating quantifiable feedback, and personalizing therapy plans using neural networks trained on stimulus-response datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human specialists provide physical therapy, then treatment quality can be maintained, but resource insufficiency and inefficiency occur
Solution Approach 1:
The robotic system performs physical therapy treatments autonomously without requiring continuous human intervention. The system independently executes therapy protocols, adjusts parameters based on sensor feedback, and monitors patient progress, thereby eliminating resource constraints related to human specialist availability while maintaining treatment quality through standardized, repeatable procedures.
Solution Approach 2:
The patent replaces human therapeutic actions with a robotic mechanical system equipped with sensors and actuators. The robot performs physical therapy maneuvers through controlled mechanical movements guided by neural networks and real-time sensor data, substituting human manual therapy with an automated mechanical system that can operate continuously without fatigue.
2Adaptability or versatility
If human specialists guide patients through movements, then therapy can be provided, but only a handful of movements can be tracked and minor responses go unnoticed
Solution Approach 1:
The robotic system incorporates multiple sensors that continuously monitor patient responses during therapy and feed this information back to the control system. This real-time feedback loop enables the system to detect even minor patient responses, adjust therapy parameters dynamically, and customize movements based on individual patient needs, thereby achieving both high measurement precision and adaptability.
Solution Approach 2:
The robotic system is designed to perform multiple therapy functions simultaneously - it can guide patients through various movements, track numerous parameters (position, speed, range of motion), and detect different types of patient responses. This multi-functional capability allows the system to comprehensively monitor and adapt to individual patient needs across diverse therapy scenarios.
3Productivity
If more instruments and devices are used to meet increasing physical therapy needs, then service coverage improves, but system complexity increases
Solution Approach 1:
The robotic system integrates multiple therapy functions, sensors, and control capabilities into a single unified platform. Rather than requiring separate instruments for different therapy types, the system can perform various physical therapy maneuvers through its programmable robotic arms and sensors, thereby expanding service coverage without proportionally increasing system complexity.
Solution Approach 2:
The patent combines multiple previously separate components - robotic actuators, sensors, control systems, and software algorithms - into an integrated therapeutic robot. This merging of functions allows the system to provide comprehensive physical therapy services through a single device rather than requiring multiple separate instruments, thus improving service coverage while managing complexity through integration.
Data Source
AI summary
A robotic system for physical therapy includes a stimulus device, a server arrangement with a first artificial intelligence (AI)-based system, and control circuitry. During a training phase, the server arrangement instructs multiple robotic systems to apply various stimuli to test users and collects stimulus-response pairs that identify a stimulus type and a corresponding response level. The system generates a one-stimulus multi-response (OSMR) dataset from the collected data and updates a second AI-based system using the dataset. The control circuitry determines test stimuli for a user based on the updated second AI-based system, applies the test stimuli, and reconfigures the stimulus device to shift a user condition from a current health state toward a target health state.