This patent describes an AI-driven functional electrical stimulation (FES) device for neuro-rehabilitation therapy. It uses a pre-trained deep reinforcement learning model to adapt stimulation in real time during a patient’s therapy session. Instead of fixed settings, the device adjusts electrical stimulation based on each patient’s condition, progress, and biological response models. This personalized, dynamic control aims to improve motor recovery and patient comfort by making rehabilitation safer and more efficient. It is intended for patients with compromised muscle function (for example after a stroke or spinal cord injury) and the clinicians who treat them. Therapists gain a smarter tool that can automatically tune therapy rather than manual adjustments. The main benefits are more effective, patient-tailored therapy and potentially faster recovery. This approach promotes neural plasticity and better outcomes, improving the lives of individuals with motor impairments. Overall, it offers a modern, technology-enhanced solution to boost the effectiveness of existing rehabilitation methods.
Problem
Current FES therapy systems use static or manually adjusted stimulation patterns that cannot adapt to a patient’s changing condition or progress. This lack of real-time adaptability can limit effectiveness and safety of rehabilitation. The invention addresses this problem by providing adaptive, AI-driven control of stimulation parameters during therapy.
Target Customers
While not explicitly stated, likely customers include neuro-rehabilitation clinics, physical therapists, hospitals, and medical device manufacturers. End-users would be patients with motor impairments (e.g., stroke survivors, spinal cord injury patients) undergoing electrical stimulation therapy. Health systems or insurers invested in improved rehab outcomes may also be stakeholders.
Existing Solutions
Existing rehabilitation systems provide FES with pre-set stimulation protocols or manual adjustments by therapists. They typically do not use advanced AI for real-time optimization. The patent implies that prior systems are largely static. Other solutions may include standard physical therapy and exercise, but none with the described automated adaptive model (no prior art detail is given).
Market Context
This technology sits in the medical device market, specifically neuro-rehabilitation. It appears specialized to healthcare and assistive therapy, rather than a broad consumer market. The market size is not given, but could include any setting offering physical therapy and rehabilitation. Its relevance depends on the prevalence of patients needing FES for recovery, which is a known segment but not mainstream consumer.
Regulatory Context
As a medical therapy device, it would be regulated as a medical device and require safety and efficacy approvals (e.g., FDA or equivalent). Healthcare data privacy and medical device standards apply. Specific rules for AI in medical devices may also apply. The text does not detail this, but the regulatory burden is likely significant.
Trends Impact
This invention aligns with trends in digital health, personalized medicine, and use of AI for medical therapy. It promotes use of data-driven, patient-specific treatments and supports neuroplasticity research. It contributes to automation and efficiency in healthcare therapy.
Limitations Unknowns
Key unknowns include the availability and size of patient data needed for training, integration with existing equipment, and actual clinical efficacy. Cost, ease of use, and ROI are unspecified. Boundary conditions (e.g., applicable patient groups) and real-world performance data are not provided in the text.
Rating
The invention scores well on addressing a meaningful healthcare problem and offering clear improvements. Its strengths include personalized therapy and alignment with digital health trends, providing strong practical benefits in rehabilitation. Weaknesses include a niche market with regulatory hurdles and uncertain IP breadth. Adoption may be slow due to medical device validation needs and limited information on performance.
Problem Significance ( 8/10)
The patent targets neuro-rehabilitation, where lack of adaptable stimulation is indeed a real issue. Motor impairment recovery is high-impact (patient quality of life). The description emphasizes a need for safety and efficiency. This suggests a significant problem, though the exact scale of patient population is not provided.
Novelty & Inventive Step ( 7/10)
Using a deep reinforcement learning model for real-time FES control appears to be a new combination in this domain. The text contrasts with static models, implying a novel approach. Without claims, it's hard to assess existing alternatives, but adapting FES parameters via AI is not known to be standard practice, indicating a non-trivial inventive step.
IP Strength & Breadth ( 6/10)
Only a concept is described (AI-based adaptive FES control). No detailed claims are given, making breadth uncertain. It covers a general approach (pre-trained DRL controlling FES) which could be enforceable if novel, but specifics are unclear. Designing around this idea may be possible if claims are narrow.
Advantage vs Existing Solutions ( 7/10)
The adaptive AI approach offers clear advantages over static FES systems. It promises improved safety, efficiency, and comfort, as well as better recovery outcomes. These improvements are explicitly stated in the text. Quantitative gains are not given, but benefits seem significant.
Market Size & Adoption Potential ( 6/10)
Neuro-rehab is a specialized medical market. Adoption depends on clinical validation and cost. The segment (motor impairment patients) is substantial but niche relative to general consumer tech. No market data is provided, so this is judged as a moderate opportunity with adoption barriers (e.g., evidence needed).
Implementation Feasibility & Cost ( 6/10)
Technically, combining FES hardware with AI control is feasible with current technology. Development will require data collection, training resources, and integration with devices. It is plausible but requires specialized expertise; costs are non-trivial but not extreme given modern AI tools.
Regulatory & Liability Friction ( 4/10)
This is a medical device (FDA oversight likely) and uses AI, raising safety and validation needs. Regulatory approval and liability for patient safety are significant concerns. This implies notable time and cost to comply with medical standards.
Competitive Defensibility (Real-World) ( 5/10)
If patented, the idea could deter competitors for a while, but AI algorithms are imitable and others could develop similar systems. The domain is complex, which offers some advantage, but this is unlikely to be an unassailable lead in the long term.
Versatility & Licensing Potential ( 4/10)
The invention is focused on one application (neuro-rehabilitation FES). It might extend to related therapy devices but is not broad. Licensing opportunities exist mainly within medical device companies and clinics. There are not many distinct industry uses given.
Strategic & Impact Alignment ( 8/10)
This aligns well with strategic health and technology themes: it advances healthcare outcomes (disability recovery) and uses AI/digitalization. It promotes inclusion by improving therapy. It fits trends in personalized medicine, making it socially impactful.