Rystor makes powered knee support that senses a user’s intent and provides targeted torque assistance to help active adults rise from seats, climb stairs, and move more confidently throughout the day. The lightweight, skin‑friendly device pairs with a companion app that tracks strength and mobility metrics, offering a discreet “e‑bike for your legs” experience for everyday activities.
Funding
Funding not disclosed
Founders
Product
Problem
Active adults often experience difficulty with movements such as standing up from a seated position or climbing stairs due to reduced knee strength or joint fatigue, which can limit daily activity and independence.
Solution
Rystor offers a powered knee support that detects the wearer’s movement intent and provides precise torque assistance at the knee joint. The device delivers a sit‑to‑stand boost and active torque support while climbing stairs, helping users perform these motions with confidence. Its lightweight, skin‑friendly construction is designed to be unobtrusive, allowing wearers to forget it’s present after a short period. An accompanying mobile app records strength and mobility metrics, enabling users to monitor progress over time. The system continuously learns individual movement patterns through AI, adapting assistance in real time to suit each user’s biomechanics.
Target Audience
Primary customers are active adults who want to maintain mobility for everyday activities such as standing, walking, and stair navigation, including fitness enthusiasts, outdoor explorers, and individuals managing age‑related joint fatigue.
Features
- Intent‑sensing sensors that identify user movement and trigger targeted knee torque assistance
- Sit‑to‑stand boost that reduces effort required to rise from any seat
- Stair‑climbing support delivering step‑by‑step torque to aid ascent
- Featherlight, soft‑material frame engineered for comfort and minimal visibility
- Rystor app for tracking personal strength and mobility data
- AI‑driven personalization that learns and adjusts to individual movement patterns in real time