The exponential development of portable electronic devices has created platforms for experimenting with new innovative ways for technical risk assessment methods in manual work. This master thesis project aimed to develop and validate a method that uses IMU sensors to support the assessment of risk factors connected to extreme arm positions. An iPhone-based smartphone application was developed to measure and evaluate arm positions and movements during work. The mobile application uses an integration of MEMS sensors (located in IMUs) which contain accelerometers, gyroscopes, and magnetometers. The app measures analysis and gives direct results in the form of statistics of the hand's vertical position in relation to the shoulder joint. Validation with 4 volunteers was performed using a camera and image analysis software as a gold standard method. The results showed relatively high validity in standardized static arm positions and a higher validity in simulated dynamic work tasks. With features such as good cost-effectiveness, ease of use, and real-time data, the application has the potential to be used in risk assessments. This thesis results indicate that portable devices, together with apps similar to the one developed in this project, may be used both in controlled experiments and in workplaces, for the assessment of risk factors connected to manual work.