Personalized Posture and Fall Classification with Shallow Gated Recurrent Units

Abstract : Activities of Daily Living (ADL) classification is a key part of assisted living systems as it can be used to assess a person autonomy. We present in this paper an activity classification pipeline using Gated Recurrent Units (GRU) and inertial sequences. We aim to take advantage of the feature extraction properties of neural networks to free ourselves from defining rules or manually choosing features. We also investigate the advantages of resampling input sequences and personalizing GRU models to improve the performances. We evaluate our models on two datasets: a dataset containing five common postures: sitting, lying, standing, walking and transfer and a dataset named MobiAct V2 providing ADL and falls. Results show that the proposed approach could benefit eHealth services and particularly activity monitoring.