About 14 percent of all women and 9 percent of all men in Germany suffer from bladder incontinence. The likelihood of incontinence increases with age. To improve continence performance, those affected need to exercise their pelvic floor muscles.

The aim of the PelFit project is to develop an intelligent, routine textile that uses biofeedback to monitor pelvic floor training. The design approach is based on the textile integration of highly sensitive electromyography sensors (EMG), which measure electrical activity based on action currents of the relevant muscles. Before each training session, the anatomical location of the EMG sensors is determined by a calibration routine. Using AI methods, the collected EMG signals will be analyzed during the training session for visual guidance of the patient to be able to prevent tightening of the abdominal and gluteal muscles and overtraining of the external bladder muscle. Technical challenges include discrimination of muscle group activity, correct electrode positioning, and disinfectability and washability at high temperatures.

The project is carried out in cooperation with the gynecology department of the Charité-Universitätsmedizin Berlin.

08/2021 – 01/2024