2023 | Professional

MUSE Design Awards Gold Winner

Digital Gait Training Robot

Entrant Company

One Measurement Group Ltd.

Category

Product Design - Healthcare

Client's Name

Beijing Zhongyi Technology Co.,Ltd.

Country / Region

China

There is an old Chinese saying that goes like this: A journey of a thousand miles begins with the first step. Yes, we believe that every little step matters. However, most people cannot walk properly and we are not just talking about pathological gaits caused by severe diseases, we are talking about the guy sitting with cross legs next to you in the office, your neighbors' annoying kids that are always running and tripping, or your friend who "walks kinda funny". Gait abnormality can be so normal yet harmful: it increases the risk of sudden falls or injuries, causes pain, and in the long run, eventually reduces the quality of life. However, factors that affect walking gaits can be complicated and difficult to determine. With the ultimate goal of improving human life quality, we developed our Digital Gait Training Robot. By integrating dynamic plantar pressure distribution data and virtual projection technology, this product aims to help patients with walking abnormalities collect gait assessment data in order to correct abnormal gait.

Our core technology is a built-in +0.1km/h medical low-speed motor that consists of thousands of high-precision pressure distribution sensors. It collects plantar pressure distribution through the running platform, projected biofeedback training, training scene display screen, and training console. Module data integration performs real-time data analysis and gives a data report approved by doctors. Through the footprint projection on the running belt, the abnormal gait pattern is corrected, gradually returning to the normal gait pattern.

Digital gait training equipment has extremely high clinical research and rehabilitation training value for several reasons. Firstly, the device is easy to operate, the system automatically recognizes the left and right strides and divides the gait cycle, generating a gait analysis report. Secondly, the device can quantitatively identify patients and assess the severity of the physical disability, providing an objective basis for formulating an overall rehabilitation plan. Finally, the device fills the empty market space where rehabilitation medical devices previously have no data support. With an aging population and shortage of medical resources, digital gait training robot is in great need.

Credits

Product designer
Ziying Li
Project manager
Song Han
Marketing manager
Yuting Qin
PR liaison
Xiaoqian Qin
Engineer
Baoquan Yang
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