EleMotion Technology Limited

Our products are designed to assist athletes to reach their desired fitness level or maximum potential, whether they are novices, amateurs or professionals. With real-time monitoring of muscle activity, our product can help people train correctly and effectively. In the long term, we will integrate our product with more functions, like online coaching, injury alarm and consulting service. At the same time, we will expand our market, providing diverse products for sedentary behaviour detection, tools for the meta-universe and elderly health. For sedentary users like office workers, we will develop a vest-like product mainly focusing on the spine-related muscle groups near the neck, shoulders and lumbar area to help the user avoid lumbar muscle strain and other related physical conditions. For the elderly, we will launch a split mode product focusing on the lower limb muscle group. Simplified instructions and extra fall detection functions will be provided to prevent possible injuries in daily activities. By motion detection, our products will serve as tools for the connection between virtual and physical spaces, which facilitate the construction of meta-universe in the future.

Our company aims to tackle the limitations of current EMG sensors by developing two key technology i.e. advanced micro-structured electrode fabrication techniques, and EMG detection and analysis algorithm. Taking advantage of nanofabrication technology, we have developed improved dry electrodes by introducing micro-structures. The microneedle electrodes featuring sharp structures ensure stable adhesion for long-term detection and prevent bleeding and pain. In addition, the mesh patterned electrodes exhibit high flexibility and good breathability for long-time wearing. With the combination of electrodeposition and photolithography, we can minimise the size of EMG wearable electronics with low cost and high throughput. For the signal analysis, a power-efficient filtering algorithm is developed to remove motion artifact and environmental noise. A highly integrated FPGA/RISC-V based processing unit is adopted for wearable design. We will be able to achieve a modularised design to enable the products to meet the needs of different potential users, i.e., professional athletes, office workers and the elderly. A machine learning evolving algorithm will be developed to predict the suitable exercise intensity for potential users to achieve efficient workout progress.