Aim Smartphone-based digital movement biomarkers are gaining popularity in neurodegenerative diseases. In this context, phone placement critically affects the accuracy of the algorithms used to derive gait metric from sensor data under unsupervised conditions. This thesis addresses this challenge by developing a robust classification pipeline to automatically detect phone location. The ultimate objective is to enhance the reliability of smartphone-derived features, particularly in real-world conditions where phone placement is uncontrolled. Methods The classifier was developed and tested using three datasets. The lab_testing dataset (n=83 healthy adults) was used for training and validation. Participants performed a 30-second walk test wearing smartphones in three predefined positions: front pockets (FP), back pockets (BP), and a waist pouch positioned at the belly (BB).