In this work, the novel design of a sliding mode TriboElectric Nano Generator (TENG)—which can utilize vibration amplitude of a few hundred microns to generate useful electric power—is proposed for the first time. Innovative design features include motion modification to amplify relative displacement of the TENG electrodes and use of biological material-based micron-sized powder at one of the electrodes to increase power output. The sliding mode TENG is designed and fabricated with use of polyurethane foam charged with the biological material micropowder and PolyTetraFluoroEthylene (PTFE) strips as the electrodes. Experimentations on the prototype within frequency range of 0.5–6 Hz ensured peak power density of 0.262 mW/m², corresponding to the TENG electrode size. Further numerical simulation is performed with the theoretical model to investigate the influence of various design parameters on the electric power generated by the TENG. Lastly, application of the proposed TENG is demonstrated in a wearable device as an in-shoe sensor. Conceptual arrangement of the proposed in-shoe sensor is presented, and numerical simulations are performed to demonstrate that the real size application can deliver peak power density of 0.747 mW/m² and TENG; the voltage will accurately represent foot vertical force for various foot force patterns.

Cureus Journals Human lung capacity is measured by one way of measuring maximum speed of exhaled air blown in a Peak Flow Meter (PFM). This traditional method requires a physical device, PFM, which may not be always be available for screening test in remote or resource-limited areas. Hence, a new method has been devised in this research work, which is user-friendly, non-invasive, and easily accessible to all. This research paper presents a novel method for measuring human respiratory peak flow rate in liters per minute, which is much more convenient than existing devices like the PFM. Peak airflow rate from human lung is estimated from a person's maximum exhaled air blown audio files recorded using a smart phone application and performing its feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs) coupled with a Random Forest Regressor. The procedure is done by recording voice samples from 86 healthy people both male and female, in the age group from 12 to 65 years, with height from 4.4 ft to 6.1 ft, and weight from 20 kg to 89 kg, as per medical doctor recommendations. MFCC features are extracted from the collected voice samples. The model is trained and saved along with the person's actual a peak flow rate measured through PFM with standard procedure. A Flask web app is developed for users, in which users can upload audio files which are then processed using Librosa for MFCCs feature extraction. It also gives a JSON result with volume of air exhaled in liters per minute, along with a MFCC graph as defined by the model. Model validation has been carried out by comparing the values predicted by MFCC and standard PFM measurements. Model accuracy obtained is up to 87%, which reveals that the performance of the audio-based predictions is consistent with actual peak flow measurements as indicated on the smartphone web app easily.