The proposed project aims to develop a robotic arm that can scan human hair using a wireless dermatoscope. This scanning process will enable us to capture digital images of the scalp and hair strands without any physical contact. The captured images will then be transferred wirelessly to a computer for further analysis.

To analyze the captured images, we will develop a neural network that will conduct pattern recognition on hair loss and diseases. The neural network will analyze numerous features of the hair images such as hair density, hair shaft thickness, hair follicle shape, and hair color. It will then compare these features with known databases of healthy and diseased hair images to determine if there are any signs of hair loss or disease on the scanned head.

One of the significant advantages of using a robotic arm and wireless dermatoscope over conventional hair analysis is the non-invasive nature of this method. The patient will not need to undergo any painful procedures such as biopsies or blood tests. Moreover, the process will be quicker as it will take only a few seconds to scan the entire head and get the results.

This project holds significant potential as it can benefit several fields, including medical diagnosis of hair-related issues, education, and research. The captured images and analyzed data will provide doctors with an efficient and accurate diagnosis of hair disorders. It can help researchers study the patterns of hair loss and understand the causes of various hair diseases. The students will focus on robotic arm control by using Python, machine learning and system integration. Commitment of at least 2 continuous semester is required. The students are expected to design and manufacture a prototype within the time frame.