An energy-harvesting dosimeter for iPhones

Smartphones are increasingly becoming an essential tool in life because of their many useful features. One of the most widely used features of a smartphone is its music player. With ease of access to a music player, people of all ages are often plugged in to their smartphones listening to music for long periods of time. However the majority do not know the danger of listening to loud music for extended periods of time. Studies have shown that many people, especially the younger people are slowly suffering hearing loss through excessive listening of loud music through earphones/headphones. Furthermore, over many years are prone to the risk of having permanent hearing loss. This is mainly due to the fact that they do not adhere to the safe noise dosage level and are unaware that they are listening to music above the safe dosage level. Therefore it is very important to make the user be aware of their noise dosage level as well as whether it exceeds the recommended safe dosage level. This Final Year Project pertains to the development of a personal (noise) dosimeter for smartphones. The Apple iPhone is used for the prototyping, and possibly for commercialization is as it holds a large majority of the global market. Unlike the dosimeter for industrial purposes, the personal dosimeter is light, compact, and user-friendly. The dosimeter is connected to the iPhone through the audio jack to sample the audio signal to the user’s earphone/headphone without distorting the sound quality and the iPhone’s microphone. Furthermore the dosimeter is self-powered, drawing its power from the iPhone. In this FYP, we have designed an improved noise dosage algorithm and simulated it in Matlab for emulation in hardware. We have programmed the Atmel Atmega2560 microcontroller to mirror the functions of the proposed Tiny43U microcontroller for testing. We have also implemented an improved Analog- to-Digital (ADC) algorithm to sample the analog signal at a higher frequency, written the first program to convert ADC data to Pulse-Width-Modulation (PWM) signal and verified several parts of the algorithm.Bachelor of Engineerin