AI-powered hand assistance for rehab and daily grip support.
A soft robotic glove prototype designed to help people with impaired hand movement practise rehabilitation exercises, grip everyday objects, and track recovery over time.
Grip pressure
84%
Range of motion
72°
Hand recovery is hard to measure at home.
Millions recovering from stroke, neurological injury, Parkinson's symptoms, arthritis, or age-related weakness struggle with hand movement. Rehab depends on short clinic sessions, while most practice happens at home without objective tracking.
Limited home rehab feedback
Patients practise exercises at home with no objective data on quality, range, or progress between clinic visits.
Difficulty gripping everyday objects
Weakness or tremor makes routine tasks — holding a cup, writing, dressing — frustrating and fatiguing.
Clinicians lack real-world data
Short clinic sessions give only a snapshot. What happens between appointments is largely invisible.
What the glove currently includes.
An early functional demonstrator combining soft robotics, wearable sensing, and AI-assisted movement analysis.
Flex Sensor Strips
Thin strips along the back of each finger — measure bend at every joint
IMU Module
Back of the hand — tracks overall orientation and acceleration
Wrist Strap
Controller + battery housed here — heaviest parts kept off the fingers
How the components are arranged
Three zones — fingers, hand, and wrist.
Flex Sensor Strips
Thin flexible strips run along the back of each finger to measure how much each joint bends. The fingertip ends are left free so they don't restrict grip or touch sensitivity. Wiring from each sensor routes along the dorsal (back) surface of the hand toward the wrist module — typically sewn into the fabric or threaded through small channels to keep the layout tidy and snag-free.
IMU Module
A small inertial measurement unit (IMU) sits on the back of the hand — a stable, relatively flat surface with minimal movement relative to the wrist. It measures overall hand orientation and acceleration, adding spatial context to the per-finger bend data. This combination lets the system distinguish a raised open hand from a lowered one, and detect larger arm movements that affect how grip force is interpreted.
Wrist Strap
The wrist strap houses both the controller electronics and the battery — deliberately keeping the heaviest components at the wrist for comfort and balance, rather than loading the fingers. A stretchy, breathable glove material such as spandex or compression fabric keeps the sensors snug against the fingers without restricting natural motion or grip. The strap fastens with a velcro clasp for easy donning and doffing.
Four stages — sense, understand, assist, track.
The glove combines wearable sensors, AI movement analysis, soft robotic actuation, and a companion app into a closed feedback loop.
01
Sense
The glove measures finger bend, hand movement, and grip pressure in real time using embedded flex and pressure sensors.
02
Understand
AI analyses movement patterns and predicts the intended grip type or rehabilitation exercise being attempted.
03
Assist
Soft robotic actuation gently supports finger movement — helping users close a grip, practise a rehab motion, or hold an object.
04
Track
The app records range of motion, repetitions, grip quality, assistance level, and progress over time for the user and clinician.
Who can benefit from the prototype.
The glove is designed to support — not replace — rehabilitation. It assists, tracks, and measures. It does not treat, cure, or guarantee recovery.
Stroke rehab
Supports repetitive hand-opening and gripping practice at home between clinic sessions.
Neurological hand weakness
Helps users practise controlled movement and grip patterns associated with neurological conditions.
Parkinson's hand function
Tracks tremor, slowness, grip difficulty, and medication-related changes over time.
Arthritis / elderly grip
Assists with gentle grip practice and daily hand-function tracking for older adults.
Physiotherapy clinics
Gives clinicians objective progress data and range-of-motion trends between appointments.
Rehab tracking dashboard.
The app turns glove sensor data into readable progress — session-by-session, week-by-week — for patients, carers, and clinicians.
Range of motion
68°
+4° this week
Grip stability
79%
Index / middle / thumb
Reps completed
42
Today's session
Fatigue trend
Low
vs last 7 days
Grip modes
Current session
12:34
Grip exercise · rep 18 / 25
Bluetooth connected
Controlled assistance, not autonomous movement.
The prototype is designed around low-force, human-controlled operation. The user or clinician is always in control.
Important notice
The prototype is designed around controlled, low-force assistance. Safety features include manual override, emergency release, adjustable assistance levels, and movement limits.
The current version is for research, demonstration, and supervised feedback only. It is not currently a certified medical device.
From prototype to clinical-grade product.
Building openly and incrementally — one validated stage at a time.
Prototype
- Finger sensing
- Assisted grip
- App dashboard
User testing
- Healthy-user testing
- Physio feedback
- Comfort improvements
Pilot study
- Supervised rehab testing
- Clinician partnerships
- Data collection
Clinical product
- Regulatory planning
- Manufacturing design
- Formal validation
One platform, multiple stakeholders.
Designed to be useful to patients, clinicians, researchers, and partners from the earliest prototype stage.
Patients & carers
People who want more feedback and support during hand-rehab practice between clinical appointments.
Clinicians & physios
Professionals who want objective hand-function data — range of motion, rep quality, grip trends — between sessions.
Researchers & partners
Teams working in AI, soft robotics, neurorehabilitation, or assistive technology interested in collaboration.
Investors & grant bodies
Early-stage partners interested in hardware-enabled rehabilitation and objective movement data platforms.
Built on emerging advances in AI, soft robotics, and wearable rehabilitation
Soft robotic gloves are being actively explored for hand rehabilitation in peer-reviewed research.
AI vision and sensor fusion can classify objects and grip types in real time.
Wearable sensors can measure movement, grip quality, and rehab progress objectively.
Home-based rehab is under-measured — objective tracking could improve outcomes and clinical decision-making.
Interested in testing, advising, or partnering?
We are looking for patients, clinicians, researchers, and partners to help shape the next phase of the prototype.
Patients & carers
Clinicians & physios
Investors & partners
Get in touch
For questions about the prototype, potential collaborations, research enquiries, or press — reach us directly.