As a gait detection solution based on deep learning and video analysis, the AICU Gait Scanner has core requirements of achieving high-precision, user-friendly, and long-term gait data collection and analysis. It needs to extract 20 types of clinical gait information (such as step frequency, stance phase proportion, and stride length coefficient of variation) and reach an accuracy close to that of the professional device GAITRite (with a maximum intraclass correlation coefficient (ICC) of 0.987). Meanwhile, it supports convenient measurement in non-medical scenarios and continuous health monitoring. The previously translated USB camera module, relying on core hardware parameters such as global shutter, global exposure, and 3μm pixels, can accurately match the technical needs of the Gait Scanner, providing crucial support for the accuracy, fluency, and scenario adaptability of its gait data collection. The specific advantages are analyzed as follows:
The AICU Gait Scanner needs to capture dynamic scenarios during walking, such as leg swing, foot landing, and body center of gravity transfer. If the images suffer from motion distortion or blurred details, it will directly affect the accuracy of the AI model in extracting clinical gait information.
The global shutter of the USB camera module can completely eliminate the "rolling shutter effect" caused by high-speed motion—for instance, rapid leg swings and instantaneous foot landings during walking will not result in image stretching or local distortion, ensuring the geometric accuracy of gait trajectories (such as stride length and sole contact angle). The global exposure technology guarantees uniform image brightness, avoiding the loss of gait details (such as toe bending status) due to uneven ambient light (e.g., strong light near windows or weak light in corners of a room). Combined with the 3μm×3μm large pixel size, the module can enhance the light intake per pixel and detail resolution, clearly presenting micro-features in gait (such as the visual mapping of sole pressure distribution and the range of ankle joint movement). The synergy of these three features provides high-fidelity image data for the AI model, directly supporting the Gait Scanner to achieve a maximum ICC of 0.987 in analysis accuracy and meeting the requirements of clinical-grade gait assessment.
The AICU Gait Scanner relies on deep learning algorithms to analyze continuous video streams. Insufficient image frame rate or transmission delay may lead to the loss of key gait frames (such as the moment of transition from stance phase to swing phase), affecting the extraction accuracy of time-dimensional parameters like step frequency and gait cycle.
The 2MP pixels + 1080P@60FPS output of the USB camera module enables continuous collection of high-definition dynamic images—the 60FPS high frame rate can fully record the dynamic process of each step, avoiding parameter misjudgment caused by "gait frame skipping" at low frame rates. The USB 3.0 high-speed interface (with a transmission bandwidth of up to 5Gbps) can transmit video streams to the AI analysis module in real time without data accumulation or delay, ensuring the algorithm can synchronously extract 20 types of clinical gait information (such as coefficient of variation parameters like StrideTm_CV_R and Stance_Pere_R). This "HD collection - high-speed transmission" link avoids analysis errors caused by image freezes or data lag, serving as the core hardware guarantee for the Gait Scanner to achieve "professional-grade analysis results".
One of the core advantages of the AICU Gait Scanner is "measurement anytime, anywhere without professional medical equipment". It needs to adapt to diverse scenarios such as homes, communities, and rehabilitation centers, placing high demands on the integration and compatibility of the camera module.
The USB camera module supports the UVC (Universal Video Class) protocol, which is directly compatible with mainstream operating systems such as Windows and Linux without the need for additional driver development—this means the Gait Scanner can be quickly integrated into kiosk terminals and portable detection devices, allowing users to start measurement without professional operations, aligning with the "user-friendliness" requirement. Its compact size of 38mm×38mm facilitates integration into various device forms (such as desktop detection platforms and mobile detection boxes), without limiting scenario deployment due to hardware volume. Meanwhile, the module supports customizable replacement of color/monochrome CMOS sensors—the color sensor adapts to normal indoor and outdoor lighting, while the monochrome sensor enhances detail contrast in low-light environments (such as nighttime home monitoring), further expanding the application scenarios of the Gait Scanner and lowering the measurement threshold in "non-professional environments".
The AICU Gait Scanner needs to support "long-term gait monitoring" to track users' brain health status (such as early warning of cognitive impairment through gait changes), requiring the camera module to have long-term stability and compliance.
The USB camera module adopts SMT (ROHS-compliant) environmentally friendly processes and AA (Active Alignment) manufacturing technology, which ensures hardware consistency in mass production—avoiding fluctuations in gait parameters caused by differences in image quality between different devices and guaranteeing the comparability of long-term monitoring data. At the same time, it has passed international certifications such as FCC, CE, Reach, and RoSH, meeting compliance requirements in the medical and health field and enabling deployment in different regions around the world. Its stable hardware performance (such as no pixel attenuation during long-term use and stable interface transmission) can support the Gait Scanner to realize the function of "regular gait detection - historical data comparison", providing reliable image collection support for the continuous tracking of users' brain health status.
In summary, through four core advantages—"accuracy in dynamic capture, support for AI analysis, flexibility in scenario adaptation, and stability in long-term use"—the USB camera module deeply matches the technical needs of the AICU Gait Scanner. It not only provides clinical-grade image quality for gait data collection but also helps the Gait Scanner break through the scenario limitations of professional medical equipment, becoming a key hardware component for its application in fields such as medical diagnosis, rehabilitation assessment, and elderly care.