A free-standing electronic commode bidet system, a bidet system, and an electronic commode bidet system for insertion onto a wheelchair, all three systems using artificial intelligence and spectroscopy software incorporating fecal detection algorithms. The systems use one or more internal cameras, or other sensors, to capture one or more images of a user as he or she sits on the commode prior to use. The systems automatically detect feces on a user by detecting the absorption spectra of bilirubin, a biomarker of feces. The systems calculate the ratio of green or red light to blue light on an RGB light spectrum. In the presence of bilirubin, more blue light will be absorbed by feces, and the green and red light are not absorbed. Once feces are detected and located on the user, the systems activate one or more water sprays to clean the feces from the user.
A free-standing electronic commode bidet system, a bidet system, and an electronic commode bidet system for insertion onto a wheelchair, all three systems using artificial intelligence and spectroscopy software incorporating fecal detection algorithms. The systems use one or more internal cameras, or other sensors, to capture one or more images of a user as he or she sits on the commode prior to use. The systems automatically detect feces on a user by detecting the absorption spectra of bilirubin, a biomarker of feces. The systems calculate the ratio of green or red light to blue light on an RGB light spectrum. In the presence of bilirubin, more blue light will be absorbed by feces, and the green and red light are not absorbed. Once feces are detected and located on the user, the systems activate one or more water sprays to clean the feces from the user.
An electronic bidet system that uses one or more internal cameras to capture images or video of a user as he or she sits on the bidet. The images or video are analyzed using machine learning computer vision technology to identify, and locate, the types, sizes, shapes, and positions of the lower body orifices, and conditions (e.g. hemorrhoids), in the user's genital and rectal areas as well as update the computational model of the user's genital and rectal areas and the computational model for cleaning the user's rectal and genital areas. Based on these analyzed images or video, the system automatically and repeatedly adjusts the bidet settings for the specific conditions (e.g. hemorrhoids) and types of orifices, locations of orifices, sizes of orifices, shapes of orifices, gender, body type, and weight of the user. Furthermore, machine learning software is used to control the cleaning and drying cycles.
An electronic bidet system that uses one or more internal cameras to capture one or more images or video of a user as he or she sits on the bidet prior to use. The images or video are analyzed using object recognition technology to identify and segment/bound the types, sizes, shapes, and positions of the lower body orifices, and conditions (e.g. hemorrhoids), in the user's genital and rectal areas. Based on these analyzed images or video, the system automatically adjusts the bidet settings for the specific conditions (e.g. hemorrhoids) and types of orifices, locations of orifices, sizes of orifices, shapes of orifices, gender, body type, and weight of the user.
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