TY - GEN A1 - Haddadi Esfahani, Ali A1 - Maye, Oliver A1 - Frohberg, Max A1 - Speh, Maria A1 - Jöbges, Michael A1 - Langendörfer, Peter T1 - Machine Learning based Real Time Detection of Freezing of Gait of Parkinson Patients Running on a Body Worn Device T2 - IEEE/ACM international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE 2023), 181 (2023) N2 - For those who have Parkinson's disease, one of the most incapacitating symptoms is Freezing of Gait (FOG). Gait impairment and disruptions limit everyday activities and reduce quality of daily life along with the increase in the risk of falling [1]. Thanks to recent advancement in embedded electronics and sensors as well as their adaptation in the wearable device market, low power devices are becoming more and more capable running neural networks. This enables researchers to implement complex models on wearable devices that capture and analyze sensor data to detect FOGin real-time. KW - Parkinson's disease KW - Wearable computers KW - Neural networks KW - Machine learning KW - Real-time systems Y1 - 2023 SN - 979-8-4007-0102-3 U6 - https://doi.org/10.1145/3580252.3589423 SN - 2832-2975 SP - 181 EP - 182 ER -